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Reconfigurable Intelligent Surface Assisted Non-Orthogonal Multiple Access
Abstract
Reconfigurable intelligent surface (RIS) is a revolutionary technology to achieve spectrum-, energy-, and cost-efficient wireless networks. This paper considers an RIS-assisted downlink non-orthogonal-multiple-access (NOMA) system. To optimize the rate performance and ensure user fairness, we maximize the minimum decoding signal-to-interference-plus-noise-ratio (equivalently the rate) of all users, by jointly optimizing the (active) transmit beamforming at the base station (BS) and the phase shifts (i.e., passive beamforming) at the RIS. A combined-channel-strength based user-ordering scheme for NOMA decoding is first proposed to decouple the user-ordering design and the joint beamforming design. Efficient algorithms are further proposed to solve the non-convex problem, by leveraging the block coordinated descent and semidefinite relaxation (SDR) techniques. For the single-antenna BS setup, the optimal power allocation at the BS and the asymptotically optimal phase shifts at the RIS are obtained in closed forms. For the multiple-antenna BS setup, it is shown that the rank of the SDR solution of the transmit beamforming design is upper bounded by two. Also, the proposed algorithms are analyzed in terms of convergence and complexity. Simulation results show that the RIS-assisted NOMA system can enhance the rate performance significantly, compared to traditional NOMA without RIS and traditional orthogonal multiple access with/without RIS.
... Among them, reconfigurable intelligent surface (RIS) has recently demonstrated its potential in enhancing system performance by exploiting passive electronic scattering components to direct electromagnetic waves and achieve a constructive combination at the decoder. For harsh propagation environments with weak or even without line-of-sight (LoS), RIS can guarantee wireless connectivity over extra multi-paths and, therefore, extends transmission coverage under obstacles such as blockage and shadowing [2]. Integrating RIS into radio networks and combining it with other advanced wireless variables, including active beamforming vectors, introduce effective resource management strategies. ...
... Integrating RIS into radio networks and combining it with other advanced wireless variables, including active beamforming vectors, introduce effective resource management strategies. Nonetheless, the optimization problems are often inherently nonconvex and challenging to obtain the global optimum or reach a local optimum with a high implementation cost if the model-based approaches are considered [2]. Despite widely investigated in the literature, the model-based approaches are nontrivial to deploy for practical applications due to the fast change of radio channels [3]. ...
... The input of the encoder is a bit string i k , represented by a one-hot vector of length M . 2 This one corresponds to a modulated signal of a constellation, for example, M -QAM (quadrature amplitude modulation). The input is a sequence denoted by ...
Autoencoder permits the end-to-end optimization and design of wireless communication systems to be more beneficial than traditional signal processing. However, this emerging learning-based framework has weaknesses, especially sensitivity to physical attacks. This paper explores adversarial attacks against a double reconfigurable intelligent surface (RIS)-assisted multiple-input and multiple-output (MIMO)-based autoencoder, where an adversary employs encoded and decoded datasets to create adversarial perturbation and fool the system. Because of the complex and dynamic data structures, adversarial attacks are not unique, each having its own benefits. We, therefore, propose three algorithms generating adversarial examples and perturbations to attack the RIS-MIMO-based autoencoder, exploiting the gradient descent and allowing for flexibility via varying the input dimensions. Numerical results show that the proposed adversarial attack-based algorithm significantly degrades the system performance regarding the symbol error rate compared to the jamming attacks.
... In [18] propose a joint transmission coordinated multi-point (JT-CoMP) method to increase the ergodic capacity of the far users without reducing the capacity of the near users. In contrast with other previous work, [19] proposed capacity fairness for all NOMA users in SISO-RIS-NOMA and MIMO-RIS-NOMA. ...
... In contrast with [8], [19] which incorporate RIS and NOMA in the proposed system, this study proposed CRI-RIS-NOMA to reduce system complexity. In addition, this study derived analytical exact for 4 users NOMA with upper-bound approximation. ...
... In this study, CRI-RIS-NOMA BER performance is compared to conventional 2 users RIS-NOMA in [8], [19]. Then, with given power allocation, this study extends 2 users to 4 users to compare proposed system superiority to RIS-NOMA. ...
The concept of reconfigurable intelligent surface-assisted (RIS) for assisting non-orthogonal multiple access (NOMA) transmission, where the phases can be adjusted to decrease error rates and increase capacity, has been gaining popularity as a promising candidate for 6G communication and beyond. This paper considers RIS-NOMA downlink transmission under the Rician fading channel. A coordinate reflector interleaving (CRI) is proposed, where two groups of reflector elements are introduced, such as in-phase element and quadrature element groups. Therefore, it can reduce system complexity because of a reduced number of successive interference cancellations (SIC) among users. As a result, a computer simulation shows that CRI-RIS-NOMA preserves a lower BER compared to conventional RIS-NOMA. Moreover, the superiority of CRI-RIS-NOMA over RIS-NOMA is that it can work at any power allocation for 2 users. This paper comprehensively studies theoretical derivation for 2 and 4 users to verify the computer-simulated BER. An upper-bound analysis of CRI-RIS-NOMA was also studied to observe the impact of RIS elements with respect to transmitting power from a base station.
... Recent studies explore integrating RIS into PLS to improve security by passively reshaping the wireless environment. This passive approach can significantly reduce energy costs and hardware complexities associated with traditional active security measures, such as artificial noise and beamforming, which increase system resource consumption [39][40][41][42]. Current research positions RIS as a passive reflector, enhancing signal strength and network coverage but lacking adaptability for security in dynamic environments. ...
... Current research positions RIS as a passive reflector, enhancing signal strength and network coverage but lacking adaptability for security in dynamic environments. This study advances RIS functionality by introducing active modulation in secure backscatter communication, enabling RIS to selectively direct signals to legitimate users while reducing the risk of interception [42,43]. This paper builds on the potential of RIS by introducing a novel DDPG control mechanism, allowing real-time phase shift adjustments for adaptive security in RISenhanced networks. ...
In 6 G wireless networks, secure communication is crucial due to the inherent susceptibility of electromagnetic waves to eavesdropping. Reconfigurable intelligent surfaces (RIS) and backscatter communication technologies offer promising solutions by securely directing signals to authorized users, even in the presence of multiple passive eavesdroppers equipped with multi-antenna setups. This paper proposes a novel RIS-enhanced backscatter communication system that utilizes radio frequency (RF) signals from a power beacon (PB) to transmit confidential information to multiple authorized users, each equipped with a single antenna. To optimize system performance, the deep deterministic policy gradient (DDPG) algorithm is employed to dynamically control RIS beamforming and mitigate eavesdropping attempts by adversaries using linear decoding techniques. Simulation results demonstrate that the proposed DDPG-based strategy significantly improves multicast secrecy rates while satisfying transmit power and unit modulus constraints. Compared to conventional optimization methods, the DDPG algorithm enhances the alignment of RIS reflections toward intended users and minimizes signal leakage to eavesdroppers. This research highlights how RIS and backscatter communication technologies can enhance security and energy efficiency in 6 G networks, providing a scalable solution to reduce eavesdropping threats in future wireless systems.
... [188], [189], [190] RIS devices involve redirecting incident signals towards a desired destination, with the objective of enhancing SNR by circumventing obstacles and obstructions between the Tx and Rx. ...
... Ding et al. [209] have proposed the RIS as a solution to manipulate the UE's wireless channel and broaden the application of NOMA technology, which is infeasible via the current techniques. Furthermore, Yang et al. have introduced a low complex scheme on how to maximize the rate of NOMA via joint exploitation of beam transmission and the phase shifting operation of the RIS elements in [190]. As the decoding order increases with UE number, U-OUI CCI cancellation becomes a big challenge in conventional NOMA, which can be further complicated in the presence of RF impairments or imperfect CSI. ...
Interference represents one of the most common barriers for the wireless communications society to bring the fully connected world to life, where everybody and everything is connected at any time, aiming to support a wide range of services and applications with increasing demand in terms of data rate with a higher degree of reliability and security, while keeping an affordable overall system capacity, complexity, and latency. Essentially, interference clearly explains the primitive nature of the wireless communications systems, where there is always an unwanted physical signal that disrupts the communication link, occurring from the physical layer (PHY) architecture of transmission signal, its interaction with the wireless channel and transceiver architecture in particular. Therefore, in past wireless technologies, waveform design along with wireless channel impairments and handset architecture define the main sources of interference, leading to inter-symbol interference (ISI), inter-carrier interference (ICI) and co-channel interference (CCI) types. In this line, recent advances in wireless technologies have revealed unprecedented interference types including inter-numerology interference (INI), inter-antenna interference (IAI), inter-waveform interference (IWI), cross-link interference (CLI) and inter-Doppler interference (IDI), while additional unique interference types are expected in near future. Consequently, a broader view of the interference has become a crucial need in order to avoid and relax its impact towards beyond 5G radio access technologies. Despite the extensive research in the literature performed by academia and industry, to the best of the authors’ knowledge, there is no work that provides a comprehensive taxonomy framework of interference sources and types, and a review of management techniques from the perspective of the PHY layer. This work aims to fill this gap in the literature. With this notation, in this survey, we propose an intuitive, generic, and expandable framework that categorizes the interference sources and their corresponding management solutions. In particular, we split the interference sources into two main groups by taking into account the user of interest such as self-user-interference (SUI) and other-user-interference (OUI), which we further classify considering the user’s intention about the presence of interference named intentional SUI (I-SUI), unintentional SUI (U-SUI), intentional OUI (I-OUI), and unintentional OUI (U-OUI). In line with this, we offer a classification of the interference management techniques regarding the source of interference. Lastly, the survey presents open research perspectives for beyond 5G wireless systems and concluding remarks.
... In wireless communications, RIS can be easily deployed on various outdoor and indoor structures, including walls, vehicle windows, advertising billboards, etc., due to their use of small, low-cost, and lightweight elements [5]. RIS can provide additional channel paths to construct stronger combined channels with significant differences in strength [6]. ...
... His main research interests are in the design and analysis of mobile communication systems, physical layer algorithms, multiple access, resource allocation, cooperative/ context-aided/ secure communications, mm-wave/ sub-THz communications, C-V2X, JCAS, satellite networks, sustainable design, and end-to-end architecture. He has co-authored 6 ...
Non-orthogonal multiple access (NOMA) is a promising technology for next-generation wireless communication systems due to its enhanced spectral efficiency. In this paper, we consider an uplink NOMA system operating together with a high-dimensional absorptive reconfigurable intelligent surface (A-RIS). We aim to minimize the total power transmitted by the users in order to meet signal-to-interference-plus-noise constraints at the base station in the presence of a jammer. We propose an iterative algorithm to solve the high-dimensional non-convex optimization problem using linear programming to find the transmit powers and a fractional programming algorithm based on the Dinkelbach algorithm with a sequential convex relaxation procedure to optimize the reflection coefficients. We show that our algorithm converges on large optimization problems, with a jammer comprising as many as 64 antennas, and an A-RIS with 128 elements. Our numerical results show that, compared with a standard RIS that reflects all impinging energy, the A-RIS can dramatically reduce the users' required transmit power and successfully mitigate interference from the jammer. The absorption capability of the A-RIS is in particular useful in cases when the number of jammer antennas is of the same order as the number of A-RIS elements.
... In particular, the successive convex approximation (SCA) and S-procedure methods were utilized to optimize the power allocation and phase shift matrix, while the Charnes-Cooper method was applied to adjust beamforming. Similarly, MISO STAR-RIS-assisted downlink network SINR [85] MISO IGS-assisted multi-cell system EC, minimum weighted rate [86] MISO Multi-user downlink system with wiretap channel Sum secure rate [87] MISO Downlink network with LoS link Minimum decoding SINR [88] MISO Multi-user STAR-RIS-assisted downlink network EE [89] MISO Multi-cluster network Total transmission power the position of the UAV, power allocation at the transmitter, and transmission/reflection beamforming were jointly optimized to maximize the sum rate of the NOMA-RIS system [82]. The advantage of this scheme is that it does not require trajectory planning and can adapt to scenarios where CSI is not available. ...
... To maximize the minimum decoding SINR of users, the authors of [87] jointly optimized the beamforming of BS and RIS based on semi-definite relaxation (SDR) and block coordinated descent (BCD) methods. The results also indicated that there was a trade-off between the number of NOMA users and the sum rate of the system, which can be adjusted according to demands. ...
Satellite-aerial-terrestrial network (SATN) is considered as a promising architecture for sixth-generation (6G) wireless communication networks to achieve seamless coverage, flexible wireless access, and high data rate. Moreover, non-orthogonal multiple access (NOMA), and reconfigurable intelligent surface (RIS) can significantly increase spectrum and energy efficiency. Recently, the integration of these two technologies and SATN has attracted a lot of attention both in academia and industry. This survey provides a comprehensive overview of RIS-empowered SATN with NOMA. In particular, the rudimentary knowledge of SATN, NOMA scheme, and RIS technology is presented. Then, the motivations for investigating the NOMA-RIS-assisted SATN are discussed. In addition, we introduce the three usage modes of RIS, two scenarios of NOMA-RIS, and the path loss model of NOMA-RIS-assisted SATN. Next, the system performance is analyzed for a case study. Besides, a comprehensive overview of resource allocation in NOMA-RIS-assisted SATN is provided, where theoretical and artificial intelligence-based methods are compared and analyzed. Moreover, physical layer security and covert communication are selected as two representative security techniques to be discussed in NOMA-RIS-aided SATN. Furthermore , the combination of other emerging technologies with NOMA-RIS-assisted SATN is investigated. Finally, this survey provides a detailed discussion of the main challenges and open issues that need to be deeply investigated from a practical point of view, including channel modeling, channel estimation, deployment strategies, and backhaul control.
... The design and performance of RIS-aided NOMA communication systems has been extensively studied in the literature. For example, the authors of [33] and [34] focus on the optimal beamforming problem of the assisted NOMA-MISO downlink system. The former achieves the optimal design by maximizing the minimum decoding SINR to ensure rate performance and user fairness, whereas the latter aims to minimize the total transmit power. ...
... Both works decouple the user ordering and the beamforming problems and then solve the transmit beamformer and phase shift matrix subproblems iteratively. Further, in order to obtain the solution to this joint beamformer problem, a unified algorithm based on block coordinate descent (BCD) has been proposed for [33], and difference-of-convex (DC)-based method for [34]. Similarly, the authors of [35] propose the design of a RIS-aided MISO system with NOMA that pairs well-separated users. ...
This paper aims to explore reconfigurable intelligent surface (RIS) integration in a millimeter wave (mmWave) communication system with low-complexity transceiver architecture under imperfect channel state information (CSI) assumption. Motivated by this, we propose a RIS-aided system with a fully analog architecture at the base station (BS). However, to overcome the drawback of single-user transmission due to the single RF chain in the analog architecture, we propose to employ NOMA to enable multi-user transmission. For such a system, we formulate two problems to obtain the joint transmit beamformer, RIS phase shift matrix, and power allocation solutions that maximize sum rate and energy efficiency such that the minimum rate for each user is satisfied. However, both problems are intractable due to 1) the fractional objective, 2) non-convex minimum rate and unit modulus RIS phase shift constraints, and 3) the coupled optimization variables. Hence, we first tackle the fractional objectives of both problems by reformulating the sum rate and energy efficiency maximization problems into equivalent quadratic forms using the quadratic transform. On the other hand, we employ successive convex approximation and the semi-definite relaxation technique to handle the non-convex minimum rate and unit modulus constraint of the RIS phase shifts, respectively. However, the problems remain non-convex due to the coupled optimization variables. Thus, we propose an alternating optimization-based algorithm that iterates over the transmit beamformer, power allocation, and RIS phase shift subproblems. Further, we also show that the quadratic reformulation is equivalent to the weighted mean square error-based reformulation for the case of sum rate maximization problem. Our numerical results show that the proposed RIS-NOMA integrated analog architecture system outperforms the optimally configured fully digital architecture in terms of sum rate at low SNR and energy efficiency for a wide range of SNR while still maintaining low hardware complexity and cost. Finally, we present the numerical performance analysis of the RIS-NOMA integrated low-complexity system for various system configuration parameters.
... After 2014, it suddenly gained momentum and became a hot research topic in the field of multiple-access techniques, and one can see that the cumulative results of yearly searches for publications using the acronym "NOMA" on the IEEE Xplore Digital Library over the past decade include a sharp increase in conference papers and journal publications, as illustrated in Figure 1. Besides, it can integrate with other existing key techniques, for example massive multiple-input multiple-output (massive MIMO) [11][12][13][14][15][16][17], cooperative communication [18][19][20][21], millimeter wave (mmWave) [22,23], index modulation (IM) [10,[24][25][26], the reconfigurable intelligent surface (RIS) (also termed the intelligent reflecting surface (IRS)) [27][28][29][30][31][32][33][34], artificial intelligence (AI) [35][36][37][38][39][40][41][42], edge computing [43,44], and holographic technology [45], to strive for further improvement of system performance. With the ubiquity and pervasiveness of IoT devices in our daily activities, in a word, NOMA is currently attracting more and more attention from both academia and industry [46]. ...
... It has been pointed out that the RIS can offer additional channel paths to create stronger combined channels with noticeable strength differences. Additionally, it can artificially realign the combined channels of users to achieve NOMA gains for challenging scenarios where the channel qualities of different users are similar [30]. ...
High effectiveness and high reliability are two fundamental concerns in data transmission. Non-orthogonal multiple-access (NOMA) technology presents a promising solution for high-speed data transmission, which has long been pursued by academia and industry. However, there is still a significant road ahead for it to effectively support a wide range of applications. This paper provides a comprehensive study, comparison, and classification of the current advanced NOMA schemes from the perspectives of single-carrier (SC) systems, multicarrier (MC) systems, reconfigurable-intelligent-surface (RIS)-assisted systems, and deep-learning (DL)-assisted systems. Specifically, system implementation issues such as the transition from SC-NOMA to MC-NOMA, the relaxation of distinct channel gains, the consideration of imperfect channel knowledge, and the mitigation of error propagation/intra-group interference are involved. To begin with, we present an overview of the state-of-the-art developments related to the advanced design of SC-NOMA. Subsequently, a generalized MC-NOMA framework that provides the diversity–multiplexing gain by enhancing users’ signal-to-interference-plus-noise ratio (SINR) is proposed for better system performance. Moreover, we delve into discussions on RIS-assisted NOMA systems, where the receiver’s SINR can be enhanced by intelligently reconfiguring the reflected signal propagations. Finally, we analyze designs that combine NOMA/RIS-NOMA with DL to achieve highly efficient data transmission. We also identify key trends and future directions in deep-learning-based NOMA frameworks, providing valuable insights for researchers in this field.
... It is preferable to pair users with asymmetric rates and/or asymmetric deployment to improve the NOMA performance compared to the OMA. The integration of the RIS and NOMA has been shown to reduce energy consumption [13], improve transmission capacity [14], and enhance flexibility [15]. The RIS can also provide an additional channel gain for UAV-assisted wireless networks. ...
In this paper, we consider an aerial reconfigurable intelligent surface (ARIS)-assisted wireless network, where multiple unmanned aerial vehicles (UAVs) collect data from ground users (GUs) by using the non-orthogonal multiple access (NOMA) method. The ARIS provides enhanced channel controllability to improve the NOMA transmissions and reduce the co-channel interference among UAVs. We also propose a novel dual-mode switching scheme, where each UAV equipped with both an ARIS and a radio frequency (RF) transceiver can adaptively perform passive reflection or active transmission. We aim to maximize the overall network throughput by jointly optimizing the UAVs' trajectory planning and operating modes, the ARIS's passive beamforming, and the GUs' transmission control strategies. We propose an optimization-driven hierarchical deep reinforcement learning (O-HDRL) method to decompose it into a series of subproblems. Specifically, the multi-agent deep deterministic policy gradient (MADDPG) adjusts the UAVs' trajectory planning and mode switching strategies, while the passive beamforming and transmission control strategies are tackled by the optimization methods. Numerical results reveal that the O-HDRL efficiently improves the learning stability and reward performance compared to the benchmark methods. Meanwhile, the dual-mode switching scheme is verified to achieve a higher throughput performance compared to the fixed ARIS scheme.
... To enhance NOMA's effectiveness, RIS can provide extra channel paths to develop more strongly defined combined channels and artificially realign users' channels [35]. As a result, NOMA achieves significant gains in challenging scenarios, such as a blocked LoS channel, which represents a significant obstacle that must be overcome by the RIS's reflective elements. ...
As we move towards the next generation of mobile networks, new approaches are essential to enhance data rates, reduce latency, and ensure robust connectivity. In my project, I explored the potential of Non-orthogonal Multiple Access (NOMA) and Reconfigurable Intelligent Surfaces (RIS), promising technologies to overcome these challenges. By employing Deep Learning tools, specifically CNN-LSTM layers, the work focuses on channel prediction in complex and nonlinear systems.
Among the key outcomes, I highlight the proposal of a new Deep Learning architecture that reduces inference time by 17% and decreases the number of training parameters by over 35% compared to the state-of-the-art.
... BackCom uses radio frequency (RF) tags to enable passive communication links by scattering RF signals to the reading device [24]- [28]. Researchers have conducted extensive researches on BackCom technology in terms of system architecture [21]- [23], signal processing [29]- [31], multiple access [32]- [34], interference cancellation [35], and so on. These inexpensive tags consume little power while possessing strong signal reflection capabilities. ...
The integration of backscatter communication (BackCom) technology with integrated sensing and communication (ISAC) technology not only enhances the system sensing performance, but also enables low-power information transmission. This is expected to provide a new paradigm for communication and sensing in internet of everything (IoE) applications. Existing works only consider sensing rate and detection performance, while none consider the estimation performance. The design of the system in different task modes also needs to be further studied. In this paper, we propose a novel system called backscatter-ISAC (B-ISAC) and design a joint beamforming framework for different stages (task modes). We derive communication performance metrics of the system in terms of the signal-to-interference-plus-noise ratio (SINR) and communication rate, and derive sensing performance metrics of the system in terms of probability of detection, estimation error of linear least squares (LS) estimation, and the estimation error of linear minimum mean square error (LMMSE) estimation. The proposed joint beamforming framework consists of three stages: tag detection, tag estimation, and communication enhancement. We develop corresponding joint beamforming schemes aimed at enhancing the performance objectives of their respective stages by solving complex non-convex optimization problems. Extensive simulation results demonstrate the effectiveness of the proposed joint beamforming schemes. The proposed B-ISAC system has broad application prospect in sixth generation (6G) IoE scenarios.
... Efficient algorithms are further proposed to solve the nonconvex problem, by leveraging the block coordinated descent and semidefinite relaxation (SDR) techniques. In [8], researchers investigate the ergodic sum rate gain (ESG) of non-orthogonal multiple access (NOMA) compared to orthogonal multiple access (OMA) in uplink mobile communication systems. They analyze the large-scale near-far gains and small-scale fading gains arising from multipath channel fading. ...
The Non-Orthogonal Multiple Access (NOMA) tech-nique, supported by Intelligent Reflecting Surfaces (IRS), is con-sidered a promising solution to enhance the quality of radio com-munication networks. However, as IRS-NOMA wireless networksare increasingly deployed more densely, inter-cell interferencehas become a significant factor limiting system capacity. Thisissue has not been adequately addressed in most existing IRS-NOMA wireless network research. In this paper, we use a randomgeometry approach to model an uplink DIRS-NOMA densewireless system. Specifically, a group of two users (UE) at the celledge communicates with the base station (BS) through a pair ofIRS, without a direct link to the BS. Based on the proposed model,we develop theoretical frameworks and calculate the BS-IRS-UElink channel statistics using a Rayleigh fading distribution. Wederive the probability density function (PDF) and the cumulativedensity function (CDF). Based on statistical data analysis, wederive closed-form expressions for the outage probability (OP),ergodic rate (ER), and bit error rate (BER) of UEs at the BSas functions of the signal-to-interference-plus-noise ratio (SINR).From the obtained results, we evaluate the overall performanceof the system. We also propose an algorithm that optimizesthe sum rate of all users under individual power constraints.This formulated problem requires simultaneous optimization ofpower at the user and phase shift at the IRS and is non-convex.Finally, we verify the mathematical analysis through comparisonswith Monte Carlo simulations. The presented numerical resultsdemonstrate that the proposed solution achieves near-optimalperformance and outperforms the OMA-based solution in termsof sum rate.
... In order to have the correct SINR for the k th user, the target SINR of the k th user is defined as [24] Therefore, the rate of the k th user in m th cluster is obtained as It should be noted that BS generates the artificial jamming addition of NOMA signals to reduce the eavesdropper effects. Therefore, the eavesdropping SINR in the m th cluster can be stated as where H mre = diag(h rme )H m and h re is the channel vector between the m th IRS and eavesdropper. ...
Intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) network is considered to enhance convergence and wireless communications. However, eavesdropper can access an IRS reflection link, therefore, securing IRS-aided networks is an important challenge. In this paper, a secure IRS aided NOMA clustered network is proposed via artificial jamming. In each cluster, NOMA and jamming signals are sent using a multi-antenna base station to their users in the presence of an eavesdropper with assistance of IRS which has the capability of energy harvesting. The purpose of the paper is maximizing the sum-secrecy rate by proper selection of IRS, power allocation coefficients of the users in each cluster, optimizing the IRS reflecting vector, jamming and beamforming vectors with constraints on the quality of service requirement. We propose a low complexity iterative algorithm based on the convex optimization method and Karush–Kuhn–Tucker conditions to determine the best IRS as a relay for transmission and also the IRS reflecting, jamming and beamforming vectors. Simulation results are verified to show the effectiveness of the proposed scheme in different situations.
... The Signal to-Noise Ratios (SNR) γ k reflects the proportion of the desired signal power received by kth user, considering contributions from both BS and IRS, relative to the combined interference from other users and the noise power. A greater SNR indicates improved signal quality, resulting in higher attainable data rates and enhanced overall system performance [34,35]. ...
Wireless communication systems are inherently challenged by factors such as fading, path loss, and shadowing, leading to potential errors in data transmission. Traditional methods to mitigate these issues include power control, diversification, variable beamforming, and modulation techniques. However, the unpredictable nature of the wireless medium often limits their effectiveness. A new approach to address these challenges is the implementation of cascaded intelligent reflecting surfaces (IRS). IRS systems consist of multiple passive elements that intelligently reflect electromagnetic waves, thereby enhancing signal quality. The Advanced Discrete Fourier Transform (ADFT) matrix scheme is explored in channel estimation, a novel method particularly suitable for wireless networks utilising cascaded IRS. The ADFT matrix scheme is significant for its efficiency in managing the common‐link configuration of cascading channel coefficients, which effectively reduces pilot overhead. When compared to traditional channel estimation methods like the Least Square|least squares, Maximal a posteriori probability, and Linear Minimum Mean Square Error, the ADFT matrix scheme exhibits superior performance. It achieves a remarkable reduction in normalised mean squared error (NMSE) – 66% and 80% at 20 dB and 15 dB Signal to‐Noise Ratios (SNR), respectively. Furthermore, increasing the pilot length correlates with enhanced NMSE performance, with a noted 33% improvement as the base station distance increases. Simulations demonstrate that with an escalation in the number of IRS elements and SNR, the ADFT matrix scheme consistently surpasses conventional methods. This advancement represents a significant leap in the field of wireless communication technology.
... Following [49], let us denote the objective function in problem (10) as f (w, α α α, Θ). This function is a logarithmic function, which is monotonically increasing. ...
With the extensive deployment of Internet-of-Things (IoT) in next generation wireless systems, the problems of energy efficiency and battery life become exacerbated, highlighting the pressing need for innovative solutions. Symbiotic radio (SR) is considered one of the emerging technologies that aims at providing an energy-efficient solution for the ubiquitous IoT applications. In this paper, we propose an SR system that is assisted with a double-faced active intelligent reflecting surface (DFA-IRS). The proposed system consists of an active transmitter (AT), an active receiver (AR), a backscatter receiver (BR), a DFA-IRS, and an IoT device that is connected to the DFA-IRS. We formulated a BR spectral efficiency maximization problem via optimizing the active beamforming vector at the AT, the power amplification factors of the IRS active elements, and the IRS phase shift at each active element under the constraints of a maximum power budget and the AR spectral efficiency requirements. The formulated problem is non-convex due to the coupling between different variables. Hence, we divided the main problems into three sub-problems and utilized the successive convex approximation (SCA) and the semidefinite relaxation (SDR) techniques to obtain a convex equivalent problem that can be solved using conventional optimization tools such as CVX. Simulation results show that the proposed algorithm converges in few number of iterations. Moreover, the proposed scheme achieves better BR spectral efficiency when compared to the case where a single-faced active IRS or a simultaneously transmitting and reflecting IRS (STAR-IRS) counterpart is used.
... .., v ϵ M ] H , and |v| 2 = 1, and use the same transformation method as (16), problem (14) can be equivalently recast as ...
This paper investigates a new self-sustainable intelligent omni-surface (S-IOS) aided multi-user wireless network, where the S-IOS harvests the radio frequency energy from the signals transmitted by the access point (AP) and exploits the harvested energy to provide full-dimensional beamforming services for the users. Three efficient operating protocols for the S-IOS, namely time switching, power splitting, and mode switching, are proposed to enable the dual-functionality of energy harvesting and information transmission. For each protocol, we design a joint optimization framework of transmit beamforming at the AP, refraction/reflection beamforming at the S-IOS, and energy harvesting schedule at the S-IOS, to maximize the network sum rate. Despite the challenging non-convex optimization problems with highly coupled and/or integer optimization variables, we develop computationally-efficient algorithms to solve them in an iterative manner, which exploit the intrinsic structure of the problems and employ the penalty-based method and the successive convex approximation. Numerical results confirm the efficiency of our developed optimization algorithms, demonstrate the significant importance of the S-IOS for spectral and energy efficient wireless communications, and quantify the performance advantage of the proposed designs over the baseline schemes.
... Research on the fusion of RIS and OFDM technology has been conducted to further boost spectral performance in recent years [11]- [16]. Nevertheless, due to the hardware implementation, RIS can only reflect the signals and achieve half-space service coverage [17]- [19], which significantly limits its effectiveness. ...
Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is emerging as a promising technology by achieving full-space coverage and further improving system performance. However, most existing works adopted an independent phase-shift model, which is high-cost and may be difficult to achieve in realistic wideband systems. Consequently, a coupled phase-shift STAR-RIS enhanced downlink multi-user multiple-input single-output orthogonal frequency division multiplexing system is investigated for both unicast and broadcast communications in this paper. We aim to maximize the average sum-rate (ASR) for all subcarriers by jointly optimizing the precoding matrices and the reflecting and transmitting coefficients (RTCs). Specifically, a block coordinate descent algorithm is proposed to iteratively design each block of a multiblock problem reformulated by the original one. The precoding matrices are optimized by the Lagrangian multiplier method for low computational complexity. For the RTCs, an element-based alternating optimization algorithm is proposed to optimize the coupled phase-shift and amplitude coefficients. Simulation results validate the effectiveness of the proposed algorithm by comparing the ASR with that of other benchmarks. Moreover, its performance closely approaches the upper bound under various practical user proportion scenarios on both sides of the STAR-RIS.
... For example, RISs can provide additional channel paths to build stronger arXiv:2309.00559v1 [eess.SP] 1 Sep 2023 combined channels with apparent strength differences and also artificially re-align users' combined channels to obtain more gain [23]. In cell-free massive MIMO, since access points (APs) serve all users on the network, colliding users located far away can also be detected by some APs based on the distinction of the pilot powers received [24]. ...
Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed.
To enhance the massive connectivity and spectral efficiency of Internet-of-Things applications supporting batteryless devices, we integrate active intelligent reflecting surface (IRS) into multiple input single output (MISO) wireless powered communication networks (WPCNs). The networks are designed with either nonorthogonal multiple access (NOMA) or time division multiple access (TDMA) towards the maximum throughput. For this purpose, we develop alternative optimization (AO) algorithms optimizing energy beamforming (BF) and active IRS BF in wireless power transfer, active IRS BF and receive BF in wireless information transfer, and time allocation iteratively, for both NOMA and TDMA networks. The proposed algorithm for the NOMA network solves each subproblem using either a convex optimization solver or a closed-form expression with the help of with the help of fractional programming. For TDMA networks, we develop a more computationally efficient algorithm than the conventional algorithm for the TDMA network. The results show that active IRS is more effective than passive IRS with a moderate number of IRS elements for MISO WPCNs. In addition, NOMA significantly outperforms TDMA for active IRS aided MISO WPCN although a slight gain of TDMA over NOMA is observed for the single-antenna counterpart.
Reconfigurable intelligent surface (RIS), a large array of passive scattering elements, is able to control the properties of electromagnetic waves, thereby enhancing the channel capacity, reducing the bit error rate, and enabling novel signal modulation methods. However, the promising gain of RIS depends on the precision of channel estimation. In this paper, we propose a three-step channel reconstruction framework to improve the channel estimation accuracy inspired by the concept of integrated sensing and communication scenario. Firstly, based on the coarse channel state information (CSI), the proposed dual one-dimensional multiple signal classification (D1D-MUSIC) algorithm improves the localization precision with a reduced complexity. Secondly, expectation maximization-based refined estimation (EMRE) algorithms are proposed to refine the CSI and estimate channel statistical properties (CSP), i.e., the shadow fading, the power of line-of-sight paths, and that of non-line-of-sight components. Thirdly, a gradient descent-based pilot optimization (GDPO) algorithm is further derived to improve the channel estimation precision on the basis of estimated CSPs. Finally, simulation results demonstrate that the developed D1D-MUSIC algorithm has lower localization error and complexity compared with conventional two-dimensional MUSIC algorithm. Moreover, the EMRE algorithms achieve the identical normalized mean square error (NMSE) performances as the ideal minimum mean square error estimator, while possessing robust resistance to the channel model mismatch. Furthermore, the developed GDPO technique is capable of providing an over 11 dB signal-to-noise ratio gain for channel estimation performance at NMSE = 10
-2
.
Security and latency are crucial aspects in the design of future wireless networks. Physical layer security (PLS) has received a growing interest from the research community in recent years for its ability to safeguard data confidentiality without relying on key distribution or encryption/decryption, and for its latency advantage over bit-level cryptographic techniques. However, the evolution towards the fifth generation (5G) technology and beyond poses new security challenges that must be addressed in order to fulfill the unprecedented performance requirements of future wireless communication networks. Among the potential key-enabling technologies, reconfigurable intelligent surface (RIS) has attracted extensive attention due to its ability to proactively and intelligently reconfigure the wireless propagation environment to combat dynamic wireless channel impairments. Consequently, the RIS technology can be adopted to improve the information-theoretic security of both radio frequency (RF) and optical wireless communications (OWC) systems. It is worth noting that the configuration of RIS in RF communications is different from the one in optical systems at many levels (e.g., RIS maraqao@mcmaster.ca, ngatchet@mcmaster.ca}). Jules M. Moualeu is with the School of Electrical and Information Engineering , University of the Witwatersrand,). This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. materials, signal characteristics, and functionalities). This survey paper provides a comprehensive overview of the information-theoretic security of RIS-based RF and optical systems. The article first discusses the fundamental concepts of PLS and RIS technologies, followed by their combination in both RF and OWC systems. Subsequently, some optimization techniques are presented in the context of the underlying system model, followed by an assessment of the impact of RIS-assisted PLS through a comprehensive performance analysis. Given that the computational complexity of future communication systems that adopt RIS-assisted PLS is likely to increase rapidly as the number of interactions between the users and infrastructure grows, machine learning (ML) is seen as a promising approach to address this complexity issue while sustaining or improving the network performance. A discussion of recent research studies on RIS-assisted PLS-based systems embedded with ML is presented. Furthermore , some important open research challenges are proposed and discussed to provide insightful future research directions, with the aim of moving a step closer towards the development and implementation of the forthcoming sixth-generation (6G) wireless technology.
Utilizing reconfigurable intelligent surface (RIS) technology to construct an intelligent radio environment is essential for the development of the sixth-generation (6G) communication network which is marked by ultra-high speed, minimal latency, and extensive connectivity. Therefore, to further enhance the coverage and signal quality of wireless communication systems, we investigate an active RIS-assisted rate-splitting multiple access (RSMA) system. Specifically, we investigate the effect of relevant parameters on system performance by deriving the analytical expressions for the outage probability (OP) of users employing hybrid automatic repeat request (HARQ) transmission strategy. The numerical results show that: 1) The reliability of the system is improved by combining HARQ transmission strategy. 2) The increase in the number of reconfigurable elements also significantly enhances the stability performance of the considered system.
In aerial reconfigurable intelligent surface (RIS) aided networks, high line-of-sight (LoS) and agility characteristics provide favorable conditions for RIS reflection. However, this also introduces extra LoS RIS reflection interference, especially when aerial RIS is fixedly deployed. This paper proposes a dynamic unmanned aerial vehicle (UAV)-mounted RIS aided multi-cell multi-user communication scheme, where the minimum ergodic rate of randomly mobile users is maximized through joint optimization of BS beamforming, RIS phases, use scheduling and UAV trajectory under the assumption of only statistical channel state information. Specifically, a closed-form ergodic rate approximation is derived, based on which the block coordinate descent framework is leveraged to solve the above four variables due to the non-convexity of the problem. Furthermore, the optimal beamforming is acquired in closed form utilizing fractional programming and quadratic transformation, RIS phases are optimized by the complex circle manifold, user scheduling is obtained by the reconstruction-based relaxation, and UAV trajectory is solved through the first-order Taylor expansion and successive convex optimization techniques. Moreover, the complexity of the proposed algorithm is analyzed and its convergence is carefully proved. The numerical results demonstrate our proposed scheme achieves a better ergodic rate than traditional methods and other aerial schemes with heuristic trajectories.
We consider secure transmission of a deterministic complex-valued parameter vector from a transmitter to an intended receiver in the presence of an eavesdropper in a reconfigurable intelligent surface (RIS)-integrated environment. We aim to jointly optimize the RIS phase profile and the power allocation matrix at the transmitter to enhance the estimation accuracy at the intended receiver while limiting that at the eavesdropper. We utilize the trace of the Fisher information matrix (FIM), equivalently, the average Fisher information, as the estimation accuracy metric, and obtain its closed form expression for the intended receiver and the eavesdropper. Accordingly, the joint RIS phase profile and power allocation problem is formulated, and it is solved via alternating optimization. When the power allocation matrix is fixed during alternating optimization, the optimal RIS phase profile design problem is formulated as a non-convex problem and it is solved via semidefinite relaxation and rank reduction. When the RIS phase profile is fixed, a linear programming formulation is obtained for optimal power allocation. Via simulations, the effects of RIS phase design and power allocation are illustrated individually and jointly. Moreover, extensions are provided by considering the presence of line of sight paths in the environment and the availability of RIS elements with adjustable magnitudes.
This paper studies an extremely large-scale reconfigurable intelligent surface (XL-RIS) empowered covert communication system in the near-field region. Alice covertly transmits messages to Bob with the assistance of the XL-RIS, while evading detection by Willie. To enhance the covert communication performance, we maximize the achievable covert rate by jointly optimizing the hybrid analog and digital beamformers at Alice, as well as the reflection coefficient matrix at the XL-RIS. An alternating optimization algorithm is proposed to solve the joint beamforming design problem. For the hybrid beamformer design, a semi-closed-form solution for fully digital beamformer is first obtained by a weighted minimum mean-square error based algorithm, then the baseband digital and analog beamformers at Alice are designed by approximating the fully digital beamformer via manifold optimization. For the XL-RIS’s reflection coefficient matrix design, a low-complexity alternating direction method of multipliers based algorithm is proposed to address the challenge of large-scale variables and unit-modulus constraints. Numerical results unveil that i) the near-field communications can achieve a higher covert rate than the far-field covert communications in general, and still realize covert transmission even if Willie is located at the same direction as Bob and closer to the XL-RIS; ii) the proposed algorithm can enhance the covert rate significantly compared to the benchmark schemes; iii) the proposed algorithm leads to a beam diffraction pattern that can bypass Willie and achieve high-rate covert transmission to Bob.
This paper proposes to integrate reconfigurable intelligent surface with backscatter communication (RIS-BackCom) for downlink non-orthogonal multiple access (NOMA) networks, where a RIS serves as a backscatter device to transmit the modulated signals to multiple single-antenna target users. Building upon the established system architecture, the weighted sum rate (WSR) is maximized for all the users under the constraints of total transmit power, RIS phase shift, rate fairness, and successive interference cancellation decoding rate. By employing the techniques of Lagrangian dual transform, quadratic transform and alternative optimization strategies, the original optimization problem is decomposed into three tractable sub-problems. Then, these sub-problems are effectively addressed using successive convex approximation and semidefinite relaxation methodologies. Experimental results demonstrate the feasibility and superiority of the proposed RIS-BackCom aided NOMA system.
Intelligent surfaces (ISs) have emerged as a key technology to empower a wide range of appealing applications for wireless networks, due to their low cost, high energy efficiency, flexibility of deployment, and capability of constructing favorable wireless channels/radio environments. Moreover, the recent advent of several new IS architectures further expanded their electromagnetic functionalities from passive reflection to active amplification, simultaneous reflection, and refraction, as well as holographic beamforming. However, the research on ISs is still in rapid progress and there have been recent technological advances in ISs and their emerging applications that are worthy of a timely review. Thus, in this article, we provide a comprehensive survey on the recent development and advances of ISs-aided wireless networks. Specifically, we start with an overview on the anticipated use cases of ISs in future wireless networks such as 6G, followed by a summary of the recent standardization activities related to ISs. Then, the main design issues of the commonly adopted reflection-based IS and their state-of-the-art solutions are presented in detail, including reflection optimization, deployment, signal modulation, wireless sensing, and integrated sensing and communications. Finally, recent progress and new challenges in advanced IS architectures are discussed to inspire future research.
Utilizing communication signals for indoor human activity recognition (HAS) is an important component of integrated sensing and communication (ISAC). The current majority HAS solutions adopt a single sensing strategy and only work in a simple environment. In this paper, we propose a new HAS method named WiSMLF that can flexibly select multiple sensing strategies and then use multi-level feature fusion for sensing. We first use the high frequency energy (HFE) method to categorize human activities into two types: static activities (SAs) and moving activities (MAs). Subsequently, for SAs, we adopt a joint localization and activity recognition sensing strategy, and use a multi-level feature fusion network based on visual geometry group (VGG). For MAs, we adopt a joint activity recognition and moving distance estimation sensing strategy, and use a multi-level feature fusion network based on long short-term memory (LSTM). The experimental results show that WiSMLF outperforms the existing methods especially in complex environments, and can obtain 92% higher accuracy in location, activity recognition, and distance estimation.
In this paper, we have analyzed the performance of a new design of the intelligent reflecting surface-non orthogonal multiple access (IRS-NOMA) system by considering all the elements of IRS to provide coherent phase shift to both users in a NOMA pair. In addition, we have designed a phase shift matrix of IRS that combines the components from IRS elements with coherent phases across both the users. The closed-form expression for the user's outage probability (OP) is derived using the Gauss-Chebyshev quadrature (GCQ) and moment-matching method to assess the system performance. Also, system throughput is evaluated in a delay-limited transmission mode. Numerical results reveal that our proposed system model of IRS-NOMA achieves better OP and higher system throughput than the other existing scenarios of IRS-NOMA networks. Finally, the Monte-Carlo simulations are also presented to verify the closed-form expressions.
Active intelligent reflecting surface (IRS) can be coupled by non-orthogonal multiple access (NOMA) to enhance the spectrum efficiency. However, this improvement comes at the expense of increased power consumption, which may lead to a decline in energy efficiency (EE). This motivates us to explore the EE performance in an active IRS-assisted uplink NOMA network. In the proposed approach, multiple users are assumed to transmit information to the base station (BS) aided by active IRS. We first develop a novel user ordering based on the strength of cascaded channels. Then, we maximize the EE by properly designing the transmit power and IRS active beamforming. This non-convex problem is addressed with an effective algorithm involving semi-definite relaxation and alternating optimization. Simulation results validate that the presented algorithm can provide marked EE improvement and outperform the benchmarks.
Wireless communication is vulnerable to malicious jamming attacks due to the inherent broadcasting nature of wireless channels. This paper investigates an anti-jamming communication system that employs a reconfigurable intelligent surface (RIS) to enhance desired signals and suppress jamming signals. To optimize the system performance while guaranteeing fairness, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) at the legitimate user equipments by jointly optimizing the transmit beamforming vectors at the base station (BS) and the reflecting coefficients at the RIS, subject to the BS’s maximum transmit power constraint and the RIS’s reflecting coefficient constraints. To solve the non-convex max-min-fairness optimization problem, we propose an alternating-optimization (AO)-based approach that alternates between optimizing variables using a second-order-cone program and semi-definite relaxation techniques. Considering the piratical limitation of imperfect jammer-related channel state information (CSI), we also adopt the stochastic successive convex approximation technique for tackling imperfect CSI in the AO-based approach. Furthermore, we propose a deep-reinforcement-learning (DRL)-based solving approach that does not require the jammer-related CSI. Numerical results show that both approaches improve the minimum SINR performance significantly. Although the AO-based approach with real-time CSI slightly outperforms the DRL-based approach with historical CSI, the DRL-based approach uses the trained deep neural network to obtain the beamforming decision directly without solving optimization problems.
Future wireless systems are envisioned to create an endogenously holography-capable, intelligent, and programmable radio propagation environment, that will offer unprecedented capabilities for high spectral and energy efficiency, low latency, and massive connectivity. A potential and promising technology for supporting the expected extreme requirements of the sixth-generation (6G) communication systems is the concept of the holographic multiple-input multiple-output (HMIMO), which will actualize holographic radios with reasonable power consumption and fabrication cost. The HMIMO is facilitated by ultra-thin, extremely large, and nearly continuous surfaces that incorporate reconfigurable and sub-wavelength-spaced antennas and/or metamaterials. Such surfaces comprising dense electromagnetic (EM) excited elements are capable of recording and manipulating impinging fields with utmost flexibility and precision, as well as with reduced cost and power consumption, thereby shaping arbitrary-intended EM waves with high energy efficiency. The powerful EM processing capability of HMIMO opens up the possibility of wireless communications of holographic imaging level, paving the way for signal processing techniques realized in the EM-domain, possibly in conjunction with their digital-domain counterparts. However, in spite of the significant potential, the studies on HMIMO communications are still at an initial stage, its fundamental limits remain to be unveiled, and a certain number of critical technical challenges need to be addressed. In this survey, we present a comprehensive overview of the latest advances in the HMIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMO systems. We also compare the HMIMO with existing multi-antenna technologies, especially the massive MIMO, present various promising synergies of HMIMO with current and future candidate technologies, and provide an extensive list of research challenges and open directions for future HMIMO-empowered wireless applications.
In a wireless powered communication network (WPCN), wireless energy acquisition is weak due to the fading characteristic of wireless link, which makes it hard to supply the energy. Reconfigurable intelligent surface (RIS) is a promising performance enhancement technology for WPCNs, which can improve the energy and spectral efficiency, expand the network coverage, and thus improve the throughput. This article proposes a new framework for energy harvesting and information transmission of non-orthogonal multiple access (NOMA)-based WPCNs aided by RIS. The total transmission capacity is maximized by joint optimization of energy precoding, information precoding, and the phase shift of RIS, which turns out to be a non-convex problem. Under the multi user scenario, we propose a joint optimization algorithm combing alternating optimization (AO), semidefinite relaxation (SDR), and successive convex approximation (SCA) methods, which is called ASS algorithm for short. Numerical results prove that compared with the non-RIS system, the ASS can increase the throughput of the system under multiuser scenario by 502.49%. For the system under single scenario, the energy precoding and information precoding are determined respectively by using the beamforming and the traditional maximal ratio combining (MRC) algorithm. The optimization problem is transformed into a complex higher order form optimization problem. Finally, a closed-form expression of the channel capacity is derived.
Unmanned aerial vehicle (UAV) has been widely used as an aerial base station (BS) to assist the ground communications for Internet of Things (IoT) due to its wide-area coverage, high mobility and line-of-sight (LoS) communication link. In this paper, a multi-UAV enabled IoT is considered, where the UAVs provide integrated sensing and communication (ISAC) services to the IoT nodes. The radar mutual information (MI) is introduced to measure the sensing performance of ISAC from the information theory perspective. To achieve fair communications, we seek to maximize the minimum communication rate per IoT node by jointly optimizing node scheduling, transmit power and 3D trajectory of the UAV under the constraint of radar MI for each IoT node. The formulated non-convex multi-variable optimization problem is divided into three subproblems, including UAV scheduling optimization, UAV transmit power optimization, and UAV 3D trajectory optimization. The near-optimal solutions of the original optimization problem can be achieved by proposing a three-layer iterative optimization algorithm to optimize the three subproblems iteratively. The simulation results demonstrate that the proposed optimization scheme can obviously improve communication rate under the constraint of radar MI as well as achieve fair communication for each node.
The emerging reconfigurable intelligent surface (RIS) is a prospective technique to modulate the wireless channel and improve performance, in which large amounts of passive elements manipulate independently, inevitably resulting in a high-dimensional optimization problem that is intractable to solve. With the aim to strike a balance between optimality and complexity for RIS assisted multi-user systems, in this paper, we formulate the achievable sum rate maximization problem under a novel RIS segmentation structure, where the distributions and sizes of each segmentation can be adaptively adjusted. Since the formulated optimization problem considering the quality of service (QoS) requirements for the users is non-convex, we suggest a computationally-efficient approach to derive an optimal solution by exploiting fractional programming, successive convex approximation (SCA), greedy algorithm, and alternating optimization. Finally, numerical simulations reveal that the proposed optimization design enables RIS to configure by grouping elements into some sub-surfaces without significant performance degradation while with much lower computational complexity than conventional element-wise optimization RIS. Moreover, our proposed adjustable segmentation outperforms the fixed one employing the determined positions and equal number of reflecting elements in each sub-surface. Additionally, the results demonstrate that the optimization of segmentation is much more significant than the phase shift optimization, showing the superiority and practical significance of the sub-surface segmentation strategy.
Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a promising technology to reconfigure the radio propagation environment in the full space. Prior works on STAR-RISs have mostly considered the energy splitting operation protocol, which has high hardware complexity in practice. Moreover, the full and instantaneous channel state information (CSI) is always assumed available for designing the STAR-RIS passive beamforming, which, however, is practically difficult to obtain due to the large number of STAR-RIS elements. To address these issues, we study the
mode switching
design in STAR-RIS aided non-orthogonal multiple access (NOMA) communication systems. Moreover, two efficient
two-timescale
(TTS) transmission protocols are proposed for different channel setups to maximize the respective average achievable sum-rate. Specifically, 1) for the case of line-of-sight (LoS) dominant channels, we propose the
beamforming-then-estimate
(BTE) protocol, where the long-term STAR-RIS transmission and reflection coefficients are optimized based on the statistical CSI only, while the short-term power allocation at the base station (BS) is designed based on the estimated effective fading channels of all users; 2) for the rich scattering environment, we propose an alternative
partition-then-estimate
(PTE) protocol, where the BS first determines the long-term STAR-RIS surface-partition strategy based on the path-loss information only, with each subsurface being assigned to one user; and then the BS estimates the instantaneous subsurface channels associated with the users and designs its power allocation and STAR-RIS phase-shifts accordingly. For the two proposed transmission protocols, we further propose efficient algorithms to solve the respective long-term and short-term optimization problems. Moreover, we show that both proposed transmission protocols substantially reduce the channel estimation overhead as compared to the existing schemes based on full instantaneous CSI. Last, simulation results validate the superiority of our proposed transmission protocols as compared to various benchmarks. It is shown that the BTE protocol outperforms the PTE protocol when the number of STAR-RIS elements is large and/or the LoS channel components are dominant, and vice versa.
This paper proposes a reconfigurable intelligent surface (RIS) segmented symbiotic ambient backscatter system, which is composed of a primary source (S), a RIS, and a cooperative receiver (D) that decodes the signals from S and RIS with the aid of successive interference cancellation (SIC). For such, the RIS is segmented into two zones, namely, an EP zone and a BD zone, where the EP zone is used to enhance the received primary signal at D while the BD zone is utilized to backscatter RIS's signal to D. The coexistence outage probability (COP) is first developed, which is shown to be dominated by the minimum number of reflectors in the two zones for sufficiently high transmit signal-to-noise ratio (SNR). In contrast, the sum ergodic capacity (EC) can only be improved by increasing the transmit power and the number of reflectors in the EP zone at high transmit SNR. Finally, two algorithms are designed to minimize the number of reflectors in the two zones while achieving acceptable COP or sum EC.
https://doi.org/10.1016/j.dcan.2023.09.002
This paper investigates the secrecy performance for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink multi-carrier non-orthogonal multiple access (NOMA) networks, consisting of multiple legitimate users and eavesdroppers. We propose two STAR-RIS-NOMA schemes for maximizing the secrecy performance by jointly optimizing the transmission and reflection beamforming of the STAR-RIS, the transmit beamforming of the base station (BS), the power allocation coefficients and the user pairing vector under the full channel state information (CSI) and the statistical CSI of the eavesdropping channel, respectively. For the full CSI available to the BS, an alternating beamforming algorithm is proposed for maximizing the secrecy sum rate. Specifically, we first propose a user pairing scheme based on the differences of user’s channel gains. Then the beamforming vectors and the power allocation coefficients are optimized based on the techniques of semidefinite programming and surrogate lower bound approximation, respectively. For the statistical CSI available to the BS, the problem of minimizing the maximum secrecy outage probability (SOP) is investigated. By invoking the subroutines of alternating beamforming algorithm, we first derive an exact SOP given the user pairing. Then, we conceive the beamforming vectors and the power allocation coefficients by linear matrix inequality and linear programming, respectively. Simulation results show that: 1) the secrecy performance of the proposed STAR-RIS-NOMA scheme outperforms the existing conventional RIS-NOMA scheme and RIS assisted orthogonal multiple access (RIS-OMA) scheme; 2) the proposed alternating beamforming algorithm is capable of achieving a near-optimal performance with low complexity compared to the exhaustive search.
This material is aimed to attract attention to the “Incoherent approach for the information transmission in wireless channels”. Such kind of approach might be successfully applied in future (5G+, 6G, etc.) dense networks formed by high-speed-vehicles (HSV networks). Those scenarios take place in Doubly Selective communication channels typical for such kind of radio networks. To be concrete, the main part of the material is related to the Power MIMO-RIS-NOMA networks, which seems to be prospective for the future. The proposal for the incoherent view (“paradigm”) is based on several basic principles, thoroughly discussed in the main body of the paper. First: rejection of the application of any type of Channel State Information (CSI, CSIT); Second: application of the modulation (demodulation) technique “invariant” to the communication channel's distortions; Third: Orthogonal Double Selective channel decomposition by means of “universal” eigen basis, as “artificial (virtual) trajectories” of wave propagation; and Finally: Chaos parameter settings for users UE's, as UE´s signatures together with multi-hypothesis sequential detection algorithms for users' classification. The proposed approach seems rather opportunistic for effective utilization of radio resources and might simplify the implementation problems.
In this paper, we consider a wireless network consisting of a base station that is serving multiple real-time traffic streams forwarding information updates to their destinations in order to sustain the freshness of information for time-critical applications. Since the wireless channels may be unreliable due to the impurities of the propagation environments, such as deep fading, blockages, etc., we integrate a reconfigurable intelligent surface to the wireless system in order to mitigate the propagation-induced impairments, enhance the quality of the wireless links, and ensure that the required freshness of information is achieved for these real time applications. For this network set-up, we investigate the joint optimization of the traffic streams scheduling and the reconfigurable intelligent surface phase-shift matrix with the goal of minimizing the long-term average Age of Information. The formulated optimization problem is a mixed integer non-convex optimization problem, which is difficult to solve. To circumvent the high-coupled optimization variables, and with the aid of bi-level optimization, we decompose the original problem into an outer traffic stream scheduling problem and an inner reconfigurable intelligent surface phase-shift matrix problem. For the outer problem, owing to its complexity and stochastic nature of packet arrivals, we resort to deep reinforcement learning solution where the traffic stream scheduling is modeled as a Markov Decision Process, and Proximal Policy Optimization is invoked to solve it. Whereas, the inner problem that determines the reconfigurable intelligent surface configuration is solved through semi-definite relaxation. Finally, we show through extensive simulations that our approach evaluates the combined impact of scheduling policy and reconfigurable intelligent surface configuration on the long term average Age of Information, where we demonstrate its superiority against other baseline schemes.
Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise massive numbers of nearly-passive elements that interact with the incident signals, for example by reflecting them, in a smart way that improves the wireless system performance. Prior work focused on the design of the LIS reflection matrices assuming full channel knowledge. Estimating these channels at the LIS, however, is a key challenging problem. With the massive number of LIS elements, channel estimation or reflection beam training will be associated with (i) huge training overhead if all the LIS elements are passive (not connected to a baseband) or with (ii) prohibitive hardware complexity and power consumption if all the elements are connected to the baseband through a fully-digital or hybrid analog/digital architecture. This paper proposes efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband). We then develop two solutions that design the LIS reflection matrices with negligible training overhead. In the first approach, we leverage compressive sensing tools to construct the channels at all the LIS elements from the channels seen only at the active elements. In the second approach, we develop a deep-learning based solution where the LIS learns how to interact with the incident signal given the channels at the active elements, which represent the state of the environment and transmitter/receiver locations. We show that the achievable rates of the proposed solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead and with only a few active elements, making them promising for future LIS systems.
The heterogenous wireless services and exponentially growing traffic call for novel spectrum-and energy-efficient wireless communication technologies. Recently, a new technique, called symbiotic radio (SR), is proposed to exploit the benefits and address the drawbacks of cognitive radio (CR) and ambient backscattering communications (AmBC), leading to mutualism spectrum sharing and highly reliable backscattering communications. In particular, the secondary transmitter (STx) in SR transmits messages to the secondary receiver (SRx) over the RF signals originating from the primary transmitter (PTx) based on cognitive backscattering communications, thus the secondary system shares not only the radio spectrum, but also the power, and infrastructure with the primary system. In return, the secondary transmission provides beneficial multipath diversity to the primary system, therefore the two systems form mutualism spectrum sharing. More importantly, joint decoding is exploited at SRx to achieve highly reliable backscattering communications. In this paper, to exploit the full potential of SR, we provide a systematic view for SR and address three fundamental tasks in SR: (1) enhancing the backscattering link via active load; (2) achieving highly reliable communications through joint decoding; and (3) capturing PTx’s RF signals using reconfigurable intelligent surfaces. Emerging applications, design challenges and open research problems will also be discussed.
Reconfigurable intelligent surfaces (RISs) have the potential of realizing the emerging concept of smart radio environments by leveraging the unique properties of metamaterials and large arrays of inexpensive antennas. In this article, we discuss the potential applications of RISs in wireless networks that operate at high-frequency bands, e.g., millimeter wave (30-100 GHz) and sub-millimeter wave (greater than 100 GHz) frequencies. When used in wireless networks, RISs may operate in a manner similar to relays. The present paper, therefore, elaborates on the key differences and similarities between RISs that are configured to operate as anomalous reflectors and relays. In particular, we illustrate numerical results that highlight the spectral efficiency gains of RISs when their size is sufficiently large as compared with the wavelength of the radio waves. In addition, we discuss key open issues that need to be addressed for unlocking the potential benefits of RISs for application to wireless communications and networks.
This letter considers integrating a backscatter link with a reconfigurable intelligent surface to enhance backscatter communication while assisting the direct communication. We derive the probability that the backscatter channel dominates in the composite channel. This probability is a useful performance measure to determine the number of reflectors. Since the exact probability lacks a closed-form solution, we develop two approximations by modeling the gain of the backscatter link with a Gaussian or Gamma distribution. We found that these approximations match well with the exact value. Importantly, with a well-designed number of reflectors, the channel gain of the backscatter link may be always stronger than that of the direct one.
This letter proposes a simple design of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) downlink transmission. In particular, conventional spatial division multiple access (SDMA) is used first at the base station to generate orthogonal beams by using the spatial directions of the near users’ channels. Then, IRS-assisted NOMA is used to ensure that additional cell-edge users can also be served on these beams by aligning the cell-edge users’ effective channel vectors with the predetermined spatial directions. Both analytical and simulation results are provided to demonstrate the performance of the proposed IRS-NOMA scheme and also study the impact of hardware impairments on IRS-NOMA.
Intelligent reflecting surfaces can improve the communication between a source and a destination. The surface contains metamaterial that is configured to “reflect” the incident wave from the source towards the destination. Two incompatible pathloss models have been used in prior work. In this letter, we derive the far-field pathloss using physical optics techniques and explain why the surface consists of many elements that individually act as diffuse scatterers but can jointly beamform the signal in a desired direction with a certain beamwidth. We disprove one of the previously conjectured pathloss models.
Large intelligent surface (LIS)-assisted wireless communications have drawn attention worldwide. With the use of low-cost LIS on building walls, signals can be reflected by the LIS and sent out along desired directions by controlling its phases, thereby providing supplementary links for wireless communication systems. In this study, we evaluate the performance of an LIS-assisted large-scale antenna system by formulating a tight approximation of the ergodic capacity and investigate the effect of the phase shifts on the ergodic capacity in different propagation scenarios. In particular, we propose an optimal phase shift design based on the ergodic capacity approximation and statistical channel state information. Furthermore, we derive the requirement on the quantization bits of the LIS to promise an acceptable capacity degradation. Numerical results show that using the proposed phase shift design can achieve the maximum ergodic capacity, and a 2-bit quantizer is sufficient to ensure capacity degradation of no more than 1 bit/s/Hz.
In this paper, we introduce an intelligent reflecting surface (IRS) to provide a programmable wireless environment for physical layer security. By adjusting the reflecting coefficients, the IRS can change the attenuation and scattering of the incident electromagnetic wave so that it can propagate in a desired way toward the intended receiver. Specifically, we consider a downlink multiple-input single-output (MISO) broadcast system where the base station (BS) transmits independent data streams to multiple legitimate receivers and keeps them secret from multiple eavesdroppers. By jointly optimizing the beamformers at the BS and reflecting coefficients at the IRS, we formulate a minimum-secrecy-rate maximization problem under various practical constraints on the reflecting coefficients. The constraints capture the scenarios of both continuous and discrete reflecting coefficients of the reflecting elements. Due to the non-convexity of the formulated problem, we propose an efficient algorithm based on the alternating optimization and the path-following algorithm to solve it in an iterative manner. Besides, we show that the proposed algorithm can converge to a local (global) optimum. Furthermore, we develop two suboptimal algorithms with some forms of closed-form solutions to reduce the computational complexity. Finally, the simulation results validate the advantages of the introduced IRS and the effectiveness of the proposed algorithms.
The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to 300% higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying.
Future wireless networks are expected to constitute a distributed intelligent wireless communications, sensing, and computing platform, which will have the challenging requirement of interconnecting the physical and digital worlds in a seamless and sustainable manner. Currently, two main factors prevent wireless network operators from building such networks: (1) the lack of control of the wireless environment, whose impact on the radio waves cannot be customized, and (2) the current operation of wireless radios, which consume a lot of power because new signals are generated whenever data has to be transmitted. In this paper, we challenge the usual “more data needs more power and emission of radio waves” status quo, and motivate that future wireless networks necessitate a smart radio environment: a transformative wireless concept, where the environmental objects are coated with artificial thin films of electromagnetic and reconfigurable material (that are referred to as reconfigurable intelligent meta-surfaces), which are capable of sensing the environment and of applying customized transformations to the radio waves. Smart radio environments have the potential to provide future wireless networks with uninterrupted wireless connectivity, and with the capability of transmitting data without generating new signals but recycling existing radio waves. We will discuss, in particular, two major types of reconfigurable intelligent meta-surfaces applied to wireless networks. The first type of meta-surfaces will be embedded into, e.g., walls, and will be directly controlled by the wireless network operators via a software controller in order to shape the radio waves for, e.g., improving the network coverage. The second type of meta-surfaces will be embedded into objects, e.g., smart t-shirts with sensors for health monitoring, and will backscatter the radio waves generated by cellular base stations in order to report their sensed data to mobile phones. These functionalities will enable wireless network operators to offer new services without the emission of additional radio waves, but by recycling those already existing for other purposes. This paper overviews the current research efforts on smart radio environments, the enabling technologies to realize them in practice, the need of new communication-theoretic models for their analysis and design, and the long-term and open research issues to be solved towards their massive deployment. In a nutshell, this paper is focused on discussing how the availability of reconfigurable intelligent meta-surfaces will allow wireless network operators to redesign common and well-known network communication paradigms.
When considering the future generation wireless networks, non-orthogonal multiple access (NOMA) represents a viable multiple access technique for improving the spectral efficiency. The basic performance of NOMA is often enhanced using downlink beamforming and power allocation techniques. Although downlink beamforming has been previously studied with different performance criteria, such as sum-rate and maxmin rate, it has not been studied in the multiuser, multipleinput single-output (MISO) case, particularly with the energy efficiency criteria. In this paper, we investigate the design of an energy efficient beamforming technique for downlink transmission in the context of a multiuser MISO-NOMA system. In particular, this beamforming design is formulated as a global energy efficiency (GEE) maximization problem with minimum user rate requirements and transmit power constraints. By using the sequential convex approximation (SCA) technique and the Dinkelbach’s algorithm to handle the non-convex nature of the GEE-Max problem, we propose two novel algorithms for solving the downlink beamforming problem for the MISONOMA system. Our evaluation of the proposed algorithms shows that they offer similar optimal designs and are effective in offering substantial energy efficiencies compared to the designs based on conventional methods.
Non-orthogonal multiple access (NOMA) is envisioned as a key technology to enhance the spectrum efficiency for 5G cellular networks. Meanwhile, ambient backscatter communication (AmBC) is a promising solution to Internet-of- Things (IoT), due to its high spectrum- and power-efficiency. In this paper, we are interested in a symbiotic system of cellular and IoT networks, and propose a backscatter-NOMA system, which incorporates a downlink NOMA system with a backscatter device (BD). In the proposed system, the base station (BS) transmits information to two cellular users according to the NOMA protocol, while a backscatter device transmits its information over the BS signals to one cellular user using passive radio technology. In particular, if the BS only serves the cellular user that decodes BD information, the backscatter-NOMA system turns into a symbiotic radio (SR) system. We derive the expressions of the outage probabilities and the ergodic rates, and analyze the corresponding diversity orders and slopes for both backscatter-NOMA and SR systems. Finally, we provide numerical results to verify the theoretical analysis and demonstrate the interrelationship between the cellular networks and IoT networks.
Electromagnetic waves undergo multiple uncontrollable alterations as they propagate within a wireless environment. Free space path loss, signal absorption, as well as reflections, refractions, and diffractions caused by physical objects within the environment highly affect the performance of wireless communications. Currently, such effects are intractable to account for and are treated as probabilistic factors. This article proposes a radically different approach, enabling deterministic, programmable control over the behavior of wireless environments. The key enabler is the so-called HyperSurface tile, a novel class of planar meta-materials that can interact with impinging electromagnetic waves in a controlled manner. The HyperSurface tiles can effectively re-engineer electromagnetic waves, including steering toward any desired direction, full absorption, polarization manipulation, and more. Multiple tiles are employed to coat objects such as walls, furniture, and overall, any objects in indoor and outdoor environments. An external software service calculates and deploys the optimal interaction types per tile to best fit the needs of communicating devices. Evaluation via simulations highlights the potential of the new concept.
This paper investigates the beamforming design for multiple-input single-output nonorthogonal multiple access (MISO NOMA) downlink systems. The NOMA beamforming design is formulated as a weighted sum rate maximization (WSRM) problem with decoding order constraints on the beamforming vectors and quality-of-service (QoS) constraints for each user. We first investigate the feasibility of the NOMA WSRM problem and then propose an efficient method to achieve the optimized beamforming vectors. Furthermore, we consider the situation where user channels are homogeneous, which is particularly suitable for using NOMA. In this case, we show that the NOMA WSRM beamforming problem admits the favorable convexity and thus the optimal beamforming solution can be efficiently found. We further show that, with homogeneous channels, the optimal power allocation of the users can be analytically characterized.
Driven by the rapid escalation of the wireless capacity requirements imposed by advanced multimedia applications (e.g., ultra-high-definition video, virtual reality etc.), as well as the dramatically increasing demand for user access required for the Internet of Things (IoT), the fifth generation (5G) networks face challenges in terms of supporting large-scale heterogeneous data traffic. Non-orthogonal multiple access (NOMA), which has been recently proposed for the 3rd generation partnership projects long-term evolution advanced (3GPP-LTE-A), constitutes a promising technology of addressing the above-mentioned challenges in 5G networks by accommodating several users within the same orthogonal resource block. By doing so, significant bandwidth efficiency enhancement can be attained over conventional orthogonal multiple access (OMA) techniques. This motivated numerous researchers to dedicate substantial research contributions to this field. In this context, we provide a comprehensive overview of the state-of-the-art in power-domain multiplexing aided NOMA, with a focus on the theoretical NOMA principles, multiple antenna aided NOMA design, on the interplay between NOMA and cooperative transmission, on the resource control of NOMA, on the coexistence of NOMA with other emerging potential 5G techniques and on the comparison with other NOMA variants. We highlight the main advantages of power-domain multiplexing NOMA compared to other existing NOMA techniques. We summarize the challenges of existing research contributions of NOMA and provide potential solutions. Finally, we offer some design guidelines for NOMA systems and identify promising research opportunities for the future.
In this paper, the performance of multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is investigated when multiple users are grouped into a cluster. The superiority of MIMO-NOMA over MIMO orthogonal multiple access (MIMO-OMA) in terms of both sum channel capacity and ergodic sum capacity is proved analytically. Furthermore, it is demonstrated that the more users are admitted to a cluster, the lower is the achieved sum rate, which illustrates the tradeoff between the sum rate and maximum number of admitted users. On this basis, a user admission scheme is proposed, which is optimal in terms of both sum rate and number of admitted users when the signal-to-interference-plus-noise ratio thresholds of the users are equal. When these thresholds are different, the proposed scheme still achieves good performance in balancing both criteria. Moreover, under certain conditions,it maximizes the number of admitted users. In addition, the complexity of the proposed scheme is linear to the number of users per cluster. Simulation results verify the superiority of MIMO-NOMA over MIMO-OMA in terms of both sum rate and user fairness, as well as the effectiveness of the proposed user admission scheme.
In this paper, we minimize transmit power for multiple-input single-output and non-orthogonal multiple access systems. In our analysis, a large number of users are partitioned into multiple user clusters/pairs with small size and uniform power allocation across the clusters, and each cluster is associated with a beamforming vector. The considered optimization problem involves how to optimize beamforming vectors, power allocation and user clustering. Considering the high computational complexity in solving the whole problem, we decompose the problem into two parts, and design a joint algorithm to iteratively optimize them. Firstly, given a user partition, we formulate the beamforming and power allocation problem under a set of practical constraints. The problem is nonconvex. To tackle it, we reformulate, transform, and approximate the nonconvex problem to a quadratically constrained optimization problem, and develop a joint beamforming and power allocation algorithm based on semidefinite relaxation to solve it. Secondly, to address the issue of high complexity in obtaining the optimal clusters, we propose a low-complexity algorithm to efficiently identify a set of promising clusters, forming as a candidate user partition. Based on these two algorithms, we design an algorithmic framework to iteratively perform them and to improve performance. By the algorithm design, the produced user partition can be further improved in later iterations, in order to further reduce power consumption. Numerical results demonstrate that the performance of the proposed solution with iterative updates for user clustering, and joint beamforming and power allocation optimization outperforms that of previous schemes.
Ambient backscatter communication (AmBC) enables radio-frequency (RF) powered backscatter devices (BDs) (e.g., sensors, tags) to modulate their information bits over ambient RF carriers in an over-the-air manner. This technology also called "modulation in the air", thus has emerged as a promising solution to achieve green communications for future Internet-of-Things. This paper studies an AmBC system by leveraging the ambient orthogonal frequency division multiplexing (OFDM) modulated signals in the air. We first model such AmBC system from a spread-spectrum communication perspective, upon which a novel joint design for BD waveform and receiver detector is proposed. The BD symbol period is designed to be in general an integer multiplication of the OFDM symbol period, and the waveform for BD bit `0' maintains the same state within a BD symbol period, while the waveform for BD bit `1' has a state transition in the middle of each OFDM symbol period within a BD symbol period. In the receiver detector design, we construct the test statistic that cancels out the direct-link interference by exploiting the repeating structure of the ambient OFDM signals due to the use of cyclic prefix. For the system with a single-antenna receiver, the maximum-likelihood detector is proposed to recover the BD bits, for which the optimal threshold is obtained in closed-form expression. For the system with a multi-antenna receiver, we propose a new test statistic, and derive the optimal detector. Moreover, practical timing synchronization algorithms are proposed, and we also analyze the effect of various system parameters on the system performance. Finally, extensive numerical results are provided to verify that the proposed transceiver design can improve the system bit-error-rate (BER) performance and the operating range significantly, and achieve much higher data rate, as compared to the conventional design.
Metamaterials are artificial structures that are usually described by effective medium parameters on the macroscopic scale, and these metamaterials are referred to as ‘analog metamaterials’. Here, we propose ‘digital metamaterials’ through two steps. First, we present ‘coding metamaterials’ that are composed of only two types of unit cells, with 0 and π phase responses, which we name ‘0’ and ‘1’ elements, respectively. By coding ‘0’ and ‘1’ elements with controlled sequences (i.e., 1-bit coding), we can manipulate electromagnetic (EM) waves and realize different functionalities. The concept of coding metamaterials can be extended from 1-bit coding to 2-bit coding or higher. In 2-bit coding, four types of unit cells, with phase responses of 0, π/2, π, and 3π/2, are required to mimic the ‘00’, ‘01’, ‘10’ and ‘11’ elements, respectively. The 2-bit coding has greater freedom than 1-bit coding for controlling EM waves. Second, we propose a unique metamaterial particle that has either a ‘0’ or ‘1’ response controlled by a biased diode. Based on this particle, we present ‘digital metamaterials’ with unit cells that possess either a ‘0’ or ‘1’ state. Using a field-programmable gate array, we realize digital control over the digital metamaterial. By programming different coding sequences, a single digital metamaterial has the ability to manipulate EM waves in different manners, thereby realizing ‘programmable metamaterials’. The above concepts and physical phenomena are confirmed through numerical simulations and experiments using metasurfaces.
Non-orthogonal multiple access (NOMA) systems have the potential to deliver
higher system throughput, compared to contemporary orthogonal multiple access
techniques. For a linearly precoded multiple-input multiple-output (MISO)
system, we study the downlink sum rate maximization problem, when the NOMA
principles are applied. Being a non-convex and intractable optimization
problem,we resort to approximate it with a minorization-maximization algorithm
(MMA), which is a widely used tool in statistics. In each step of the MMA, we
solve a second-order cone program, such that the feasibility set in each step
contains that of the previous one, and is always guaranteed to be a subset of
the feasibility set of the original problem. It should be noted that the
algorithm takes a few iterations to converge. Furthermore, we study the
conditions under which the achievable rates maximization can be further
simplified to a low complexity design problem, and we compute the probability
of occurrence of this event. Numerical examples are conducted to show a
comparison of the proposed approach against conventional multiple access
systems. NOMA is reported to provide better spectral and power efficiency with
a polynomial time computational complexity.
This paper presents our investigation of non-orthogonal multiple access (NOMA) as a novel and promising power-domain user multiplexing scheme for future radio access. Based on information theory, we can expect that NOMA with a successive interference canceller (SIC) applied to the receiver side will offer a better tradeoff between system efficiency and user fairness than orthogonal multiple access (OMA), which is widely used in 3.9 and 4G mobile communication systems. This improvement becomes especially significant when the channel conditions among the non-orthogonally multiplexed users are significantly different. Thus, NOMA can be expected to efficiently exploit the near-far effect experienced in cellular environments. In this paper, we describe the basic principle of NOMA in both the downlink and uplink and then present our proposed NOMA scheme for the scenario where the base station is equipped with multiple antennas. Simulation results show the potential system-level throughput gains of NOMA relative to OMA.
We study RF-enabled wireless energy transfer (WET) via energy beamforming,
from a multi-antenna energy transmitter (ET) to multiple energy receivers (ERs)
in a backscatter communication system, such as RFID, where each ER (or RFID
tag) reflects back a portion of the incident signal to the ET (or RFID reader).
For such a system, the acquisition of the forward-channel (i.e., ET-to-ER)
state information (F-CSI) at the ET is challenging, since the ERs are typically
too energy-and-hardware-constrained to estimate or feed back the F-CSI. The ET
leverages its observed backscatter signals to estimate the backscatter-channel
(i.e., ET-to-ER-to-ET) state information (BS-CSI) directly. We first analyze
the harvested energy obtained by using the estimated BS-CSI. Furthermore, we
optimize the channel-training energy and the energy allocation weights for
different energy beams, for weighted-sum-energy (WSE) maximization and
proportional-fair-energy (PFE) maximization. For WET to single ER, we obtain
the optimal channel-training energy in a semi-closed form. For WET to multiple
ERs, the optimal WET scheme for WSE maximization is shown to use only one
energy beam. For PFE maximization, we show it is a biconvex problem, and
propose a block-coordinate-descent based algorithm to find the close-to-optimal
solution. Numerical results show that with the optimized solutions, the
harvested energy suffers slight reduction of less than 10%, compared to that
obtained by using the perfect F-CSI. Hence, energy beamforming by using the
estimated BS-CSI is promising, as the complexity and energy requirement is
shifted from the ERs to the ET.
We derive some basic results on the geometry of semidefinite programming (SDP) and eigenvalue-optimization, i.e., the minimization of the sum of the k largest eigenvalues of a smooth matrix-valued function.
We provide upper bounds on the rank of extreme matrices in SDPs, and the first theoretically solid explanation of a phenomenon of intrinsic interest in eigenvalue-optimization. In the spectrum of an optimal matrix, the kth and (k + 1)st largest eigenvalues tend to be equal and frequently have multiplicity greater than two. This clustering is intuitively plausible and has been observed as early as 1975.
When the matrix-valued function is affine, we prove that clustering must occur at extreme points of the set of optimal solutions, if the number of variables is sufficiently large. We also give a lower bound on the multiplicity of the critical eigenvalue. These results generalize to the case of a general matrix-valued function under appropriate conditions.
In this article, we have provided general, comprehensive coverage of the SDR technique, from its practical deployments and scope of applicability to key theoretical results. We have also showcased several representative applications, namely MIMO detection, B¿ shimming in MRI, and sensor network localization. Another important application, namely downlink transmit beamforming, is described in [1]. Due to space limitations, we are unable to cover many other beautiful applications of the SDR technique, although we have done our best to illustrate the key intuitive ideas that resulted in those applications. We hope that this introductory article will serve as a good starting point for readers who would like to apply the SDR technique to their applications, and to locate specific references either in applications or theory.
Large intelligent surface/antennas (LISA), a two-dimensional artificial structure with a large number of reflective-surface/antenna elements, is a promising reflective radio technology to construct programmable wireless environments in a smart way. Specifically, each element of the LISA adjusts the reflection of the incident electromagnetic waves with unnatural properties, such as negative refraction, perfect absorption, and anomalous reflection, thus the wireless environments can be software-defined according to various design objectives. In this paper, we introduce the reflective radio basics, including backscattering principles, backscatter communication, reflective relay, the fundamentals and implementations of LISA technology. Then, we present an overview of the state-of-the-art research on emerging applications of LISA-aided wireless networks. Finally, the limitations, challenges, and open issues associated with LISA for future wireless applications are discussed.
Backscatter communication which enables wireless-powered backscatter devices (BDs) to transmit information by reflecting incident signals, is an energy- and cost-efficient communication technology for Internet-of-Things. This paper considers an unmanned aerial vehicle (UAV)-assisted backscatter communication network (UBCN) consisting of multiple BDs and carrier emitters (CEs) on the ground as well as a UAV. A communicate-while-fly scheme is first designed, in which the BDs illuminated by their associated CEs transmit information to the flying UAV in a time-division-multiple-access manner. Considering the critical issue of the UAV’s limited on-board energy and the CEs’ transmission energy, we maximize the energy efficiency (EE) of the UBCN by jointly optimizing the UAV’s trajectory, the BDs’ scheduling, and the CEs’ transmission power, subject to the BDs’ throughput constraints and harvested energy constraints, as well as other practical constraints. Furthermore, we propose an iterative algorithm based on the block coordinated decent method to solve the formulated mixed-integer non-convex problem, in each iteration of which the variables are alternatively optimized by leveraging the cutting-plane technique, the Dinkelbach’s method and the successive convex approximation technique. Also, the convergence and complexity of the proposed algorithm are analyzed. Finally, simulation results show that the proposed communicate-while-fly scheme achieves significant EE gains compared with the hover-and-fly scheme, the state-of-the-art scheme, and the CE-relay scheme. Useful insights on the optimal trajectory design and resource allocation are also obtained.
Reconfigurable intelligent surfaces (RISs) are an emerging transmission technology for application to wireless communications. RISs can be realized in different ways, which include (i) large arrays of inexpensive antennas that are usually spaced half of the wavelength apart; and (ii) metamaterial-based planar or conformal large surfaces whose scattering elements have sizes and inter-distances much smaller than the wavelength. Compared with other transmission technologies, e.g., phased arrays, multi-antenna transmitters, and relays, RISs require the largest number of scattering elements, but each of them needs to be backed by the fewest and least costly components. Also, no power amplifiers are usually needed. For these reasons, RISs constitute a promising software-defined architecture that can be realized at reduced cost, size, weight, and power (C-SWaP design), and are regarded as an enabling technology for realizing the emerging concept of smart radio environments (SREs). In this paper, we (i) introduce the emerging research field of RIS-empowered SREs; (ii) overview the most suitable applications of RISs in wireless networks; (iii) present an electromagnetic-based communication-theoretic framework for analyzing and optimizing metamaterial-based RISs; (iv) provide a comprehensive overview of the current state of research; and (v) discuss the most important research issues to tackle. Owing to the interdisciplinary essence of RIS-empowered SREs, finally, we put forth the need of reconciling and reuniting C. E. Shannon’s mathematical theory of communication with G. Green’s and J. C. Maxwell’s mathematical theories of electromagnetism for appropriately modeling, analyzing, optimizing, and deploying future wireless networks empowered by RISs.
Recently, the reconfigurable intelligent surface (RIS), benefited from the breakthrough on the fabrication of programmable meta-material, has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology to achieve smart radio environments. Employed as reflecting arrays, RIS is able to assist MIMO transmissions without the need of radio frequency chains resulting in considerable reduction in power consumption. In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL). We first develop a DRL based algorithm, in which the joint design is obtained through trial-and-error interactions with the environment by observing predefined rewards, in the context of continuous state and action. Unlike the most reported works utilizing the alternating optimization techniques to alternatively obtain the transmit beamforming and phase shifts, the proposed DRL based algorithm obtains the joint design simultaneously as the output of the DRL neural network. Simulation results show that the proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks. It is also observed that, appropriate neural network parameter settings will improve significantly the performance and convergence rate of the proposed algorithm.
This work focuses on the downlink of a single-cell multi-user system in which a base station (BS) equipped with
M
antennas communicates with
K
single-antenna users through a reconfigurable intelligent surface (RIS) installed in the line-of-sight (LoS) of the BS. RIS is envisioned to offer unprecedented spectral efficiency gains by utilizing
N
passive reflecting elements that induce phase shifts on the impinging electromagnetic waves to smartly reconfigure the signal propagation environment. We study the minimum signal-to-interference-plus-noise ratio (SINR) achieved by the optimal linear precoder (OLP), that maximizes the minimum SINR subject to a given power constraint for any given RIS phase matrix, for the cases where the LoS channel matrix between the BS and the RIS is of rank-one and of full-rank. In the former scenario, the minimum SINR achieved by the RIS-assisted link is bounded by a quantity that goes to zero with
K
. For the high-rank scenario, we develop accurate deterministic approximations for the parameters of the asymptotically OLP, which are then utilized to optimize the RIS phase matrix. Simulation results show that RISs can outperform half-duplex relays with a small number of passive reflecting elements while large RISs are needed to outperform full-duplex relays.
In this paper, we study the reconfigurable intelligent surface (RIS) based downlink multi-user system where a multi-antenna base station (BS) sends signals to various users assisted by the RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only the large-scale fading gain is required at the BS and a limited number of phase shifts can be realized by the finite-sized RIS. To maximize the sum rate, we propose a hybrid beamforming scheme where the continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively. An iterative algorithm is designed for beamforming and theoretical analysis is provided to evaluate how the size of RIS influences the achievable rate. Simulation results show that the RIS-based system can achieve a good sum-rate performance by setting a reasonable size of RIS and a small number of discrete phase shifts.
Reconfigurable intelligent surfaces (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this paper, we consider a RIS-aided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated. For perfect CSI setup, a low-complexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. Then, we resort to the stochastic successive convex approximation technique and extend the proposed algorithm to the scenario wherein the CSI is imperfect. The validity of the proposed methods is confirmed by numerical results. In particular, the proposed algorithm performs quite well when the channel uncertainty is smaller than 10%.
In this paper, a symbiotic radio (SR) system is proposed to support passive Internet-of-Things (IoT), in which a backscatter device (BD), also called IoT device, is parasitic in a primary transmission. The primary transmitter is designed to assist both the primary and BD transmissions, and the primary receiver is used to decode the information from the primary transmitter as well as the BD. The symbol period for BD transmission is assumed to be either equal to or much greater than that of the primary one, resulting in parasitic SR (PSR) or commensal SR (CSR) setup. We consider a basic SR system which consists of three nodes: a multi-antenna primary transmitter, a single-antenna BD, and a single-antenna primary receiver. We first derive the achievable rates for the primary and BD transmissions for each setup. Then, we formulate two transmit beamforming optimization problems, i.e., the weighted sum-rate maximization problem and the transmit power minimization problem, and solve these non-convex problems by applying the semi-definite relaxation technique. In addition, a novel transmit beamforming structure is proposed to reduce the computational complexity of the solutions. Simulation results show that for CSR setup, the proposed solution enables the opportunistic transmission for the BD via energy-efficient passive backscattering without any loss in spectral efficiency, by properly exploiting the additional signal path from the BD.