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Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead
Abstract
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.
... Besides ISAC, one of the most relevant technological innovations associated to future 6G wireless networks is the adoption of reconfigurable intelligent surfaces (RISs) [4], [5], which are planar structures made of several small elements capable of reflecting incident electromagnetic waves with a tunable and electronically controllable phase shift. The last few years have witnessed progressive attention towards using RISs to support communication and radar systems in their standalone [11], coexistence [12], [13], and DFRC scenarios [14]- [15]. ...
... The last few years have witnessed progressive attention towards using RISs to support communication and radar systems in their standalone [11], coexistence [12], [13], and DFRC scenarios [14]- [15]. As shown in Fig. 1, an RIS is fabricated as a planar array of controllable reflecting elements of meta-material, which are able to apply tunable reflection coefficients to the incident signals to combine them either constructively or destructively in specific directions [4]. RIS can offer significant benefits to all wireless communication scenarios including ISAC systems. ...
... The emergence of active RIS has spurred comparisons between RISs and relays, revealing notable disparities in terms of hardware complexity, noise generation, spectral efficiency, and power requirements [1], [4]. Relays exhibit higher hardware complexity due to active components and power needs, while RISs present a simpler and potentially more cost-effective solution, particularly in mass production using inexpensive electronic components [4]. ...
Reconfigurable intelligent surfaces (RISs) have emerged as a revolutionary technology for controlling wireless channels to improve the performance of wireless communication and radar systems. However, the performance improvement of these wireless systems is shown to be limited due to the multiplicative fading effect. Active RISs have been introduced to circumvent this effect while simultaneously increasing the degrees-of-freedom in the design of RIS-assisted networks. On a parallel theme, integrated sensing and communications (ISAC) has been identified as one of the promising 6G paradigms for enabling sensing and communication functionalities within an integrated platform, which allows more efficient use of energy and spectral resources at a reasonable cost, and paves the way to the birth of new use-cases and applications. This article investigates the roles of active and passive RISs in different ISAC scenarios. Indeed, while there is abundant literature covering the use of both passive and active RISs for wireless communications, there is still a shortage in studying their benefits for radar and ISAC scenarios. This paper presents an overview of the currently available studies on the use of active and passive RISs for ISAC purposes and provides numerical examples to show the superiority and greater flexibility of active RISs in the context of ISAC. Finally, possible use cases for active RISs in modern ISAC systems are explored, and research challenges are highlighted along with the relevant opportunities that RISs offer.
... In addition, to address the high sensitivity of mmWave communication to blocking, as well as the high dependence on the layout of the wireless environment, Reconfigurable Intelligent Surface (RIS) recently becomes an important research direction, which can effectively improve the signal quality. RIS can control the reflection of signals arriving on the RIS Surface, to enhance the received signal strength [14]. RIS is composed of multiple programmable positiveintrinsic-negative (PIN) diode elements, each of which can be used to adjust the phase shift of the signal reaching the RIS surface [15]. ...
... Therefore, the uplink throughput of the user c i , denoted as R ci , is give in (14). The sum uplink throughput of all users in the system can be expressed as (15). ...
... In the HCN network, the number of users that initially access one BS c N is |c N |, c N ∈ C. According to (14), the sum uplink throughput of the BS c N , that is utility function of the coalition, is referred to as: ...
Due to the development of communication technology and the rise of user network demand, a reasonable resource allocation for wireless networks is the key to guaranteeing regular operation and improving system performance. Various frequency bands exist in the natural network environment, and heterogeneous cellular network (HCN) has become a hot topic for current research. Meanwhile, Reconfigurable Intelligent Surface (RIS) has become a key technology for developing next-generation wireless networks. By modifying the phase of the incident signal arriving at the RIS surface, RIS can improve the signal quality at the receiver and reduce co-channel interference. In this paper, we develop a RIS-assisted HCN model for a multi-base station (BS) multi-frequency network, which includes 4G, 5G, millimeter wave (mmwave), and terahertz networks, and considers the case of multiple network coverage users, which is more in line with the realistic network characteristics and the concept of 6G networks. We propose the optimization objective of maximizing the system sum rate, which is decomposed into two subproblems, i.e., the user resource allocation and the phase shift optimization problem of RIS components. Due to the NP-hard and coupling relationship, we use the block coordinate descent (BCD) method to alternately optimize the local solutions of the coalition game and the local discrete phase search algorithm to obtain the global solution. In contrast, most previous studies have used the coalition game algorithm to solve the resource allocation problem alone. Simulation results show that the algorithm performs better than the rest of the algorithms, effectively improves the system sum rate, and achieves performance close to the optimal solution of the traversal algorithm with low complexity.
... The system performance will degrade if the hybrid beamforming is targeted for partial connections. Multi-user beamforming for millimeter-wave massive MIMO in UAV leads to high-dimensional operations [34][35][36][37]. To maximize the spectral efficiency of the system, it is necessary to jointly design its RIS reflection coefficient and hybrid beamforming. ...
... The optimization equation (76) is decoupled by keeping one variable fixed and optimizing iteratively the other variable. The optimization equation (76) is rewritten as the following equation: 17 Two sets of alternating minimization methods (AltMin, Alternating Minimization) are defined in the literature [35]. One of them is a method for high complexity manifold optimization. ...
UAV (Unmanned aerial vehicle) communication offers the possibility to establish the new net-works. To overcome the PE (pointing error) and beam misalignment of millimeter-wave massive MIMO (Multiple-in multiple-out)/FSO (Free Space Optical) caused by UAV jitter, a Tensor-train decomposition based hybrid beamforming for millimeter-wave massive MIMO/FSO in UAV with RIS (Reconfigurable Intelligence Surface) networks is investigated to improve the system spectral efficiency. Firstly, the high-dimensional channels of the RIS-assisted millimeter-wave massive MIMO/FSO in UAV are represented as the low-dimensional channels by Tensor-train decomposition. Secondly, the FSO PE caused by UAV jitter can be effectively solved by BIGRU (Bidirectional Gated Recurrent Unit)-attention neural network model. The fast-fading channels and Doppler shifts are estimated by the FCTPM (Fast Circulant Tensor Power Method) based on the Tensor-train decomposition. Finally, the RIS phase shift matrix is optimized by the SVD (Singular Value Decomposition). The Hybrid beamforming and RIS phase shift matrix are esti-mated by the low-complexity PE-AltMin (Phase Extraction Alternating Minimization) method to solve the beam misalignment. Simulation experiments demonstrate that the proposed method has higher spectrum utilization than other methods.
... In response to these issues, as one of the key technologies in 6G, reconfigurable intelligent surface (RIS) can help to reshape wireless propagation environments, by adjusting the amplitude and phase of the reflection units. RIS can strengthen the desired signals and suppress the interfering signals, helping to provide a reliable transmission environment for metaverse services [47][48][49]. Moreover, to further enhance transmission reliability, the access network introduces many new advanced error correction coding technologies and flexible retransmission mechanisms, and, in the meantime, the core network adopts more robust redundant-based protection schemes [50]. ...
... This will sufficiently connect the internet of humans and the IoT, wireless and wired networks, wide and local coverages, and aerial and terrestrial communications systems. In addition, as mentioned earlier, the introduction of RIS can overcome challenges imposed by propagation obstacles, channel fading, and environmental interference, which can fill coverage holes and blind spots [47,48]. Under the new 6G network, broadband high-speed communications and narrow-band communications for the IoT, as well as global positioning and real-time sensing capabilities, will be integrated together, to serve metaverse business with seamless coverage. ...
The metaverse, as an envisioned paradigm of the future internet, aims to establish an immersive and multidimensional virtual space in which global users can interact with one another, as in the real world. With the rapid development of emerging technologies—such as digital twins (DT), blockchain, and artificial intelligence (AI)—the diverse potential application scenarios of the metaverse have attracted a great deal of research attention and have created a prosperous market. The demand for ubiquitous communications, pervasive sensing, ultra-low latency computing, and distributed storage has consequently surged, due to the massive heterogeneous devices and data in the metaverse. In order to achieve the metaverse, it is essential to establish an infrastructure system that integrates communications, sensing, computing, and storage technologies. Information about the physical world can be obtained by pervasive sensing, computing resources can be scheduled in a reasonable manner, quick data access can be achieved through the coordination of centralized and distributed storage, and, as the bridge, mobile communications systems connect communications, sensing, computing, and storage in a new system, which is the integration of communications, sensing, computing, and storage (I-CSCS). Following this trend, this paper discusses the requirements of the metaverse for spectrum resources, ultra-reliable transmission, seamless coverage, and security protection in wireless mobile communications systems, and analyzes the fundamental supporting role of the sixth-generation mobile communications system (6G) in the metaverse. Then, we explore the functions and roles of the integrated sensing and communications technologies (ISAC), as well as the integration of communications, computing, and storage technologies for the metaverse. Finally, we summarize the research directions and challenges of I-CSCS in the metaverse.
... Additionally, it is possible to use UEs themselves as sensing devices to generate coverage maps for path planning, route planning, or similar tasks. Current technology developments for 6G are allowing to influence the channel and coverage map via Reconfigurable Intelligent Surfaces (RIS) so that nearly line of sight quality can be reached if an alternative path via RIS can be used [108]. This environmental awareness can drive the network as an active component in the 6G system which provides better control of stochastic parameters in wireless systems in general for an improved communication quality. ...
Over the last decade, society and industries are undergoing rapid digitization that is expected to lead to the evolution of the cyber-physical continuum. End-to-end deterministic communications infrastructure is the essential glue that will bridge the digital and physical worlds of the continuum. We describe the state of the art and open challenges with respect to contemporary deterministic communications and compute technologies— 3GPP 5G, IEEE Time-Sensitive Networking, IETF DetNet, OPC UA as well as edge computing. While these technologies represent significant technological advancements towards networking Cyber-Physical Systems (CPS), we argue in this paper that they rather represent a first generation of systems which are still limited in different dimensions. In contrast, realizing future deterministic communications systems requires, firstly, seamless
convergence
between these technologies and, secondly,
scalability
to support heterogeneous (time-varying requirements) arising from diverse CPS applications. In addition, future deterministic communication networks will have to provide such characteristics
end-to-end
, which for CPS refers to the entire communication and computation loop, from sensors to actuators. In this paper, we discuss the state of the art regarding the main challenges towards these goals: predictability, end-to-end technology integration, end-to-end security, and scalable vertical application interfacing. We then present our vision regarding viable approaches and technological enablers to overcome these four central challenges. In particular, we argue that there is currently a window of opportunity to establish, through 6G standardization, the foundations towards a scalable and converged deterministic communications and compute infrastructure. Key approaches to leverage in that regard are 6G system evolutions, wireless-friendly 6G integration with TSN and DetNet, novel end-to-end security approaches, efficient edge-cloud integrations, data-driven approaches for stochastic characterization and prediction, as well as leveraging digital twins towards system awareness.
... Traditional technologies, such as dense and heterogeneous networking, massive MIMO, and relaying can improve coverage and capacity effectively but incur high costs, much power consumption, and severe signal interference. In contrast, RIS not only reflects signals in a full-duplex and noiseless way but also provides a power-efficient and cost-efficient solution that supports green and sustainable performance growth in the 6G system since each RIS element is passive, lightweight, and cheap [2]. Previous studies have investigated different aspects for RISaided wireless communications, e.g., reflection optimization [3], RIS channel estimation [4], hardware constraints [5], and the interplay with other technologies, such as MIMO [6], OFDM [7], and Terahertz communications [8]. ...
This paper focuses on the design of transmission methods and reflection optimization for a wireless system assisted by a single or multiple reconfigurable intelligent surfaces (RISs). The existing techniques are either too complex to implement in practical systems or too inefficient to achieve high performance. To overcome the shortcomings of the existing schemes, we propose a simple but efficient approach based on opportunistic reflection and non-orthogonal transmission. The key idea is opportunistically selecting the best user that can reap the maximal gain from the optimally reflected signals via RIS. That is to say, only the channel state information of the best user is used for RIS reflection optimization, which can in turn lower complexity substantially. In addition, the second user is selected to superpose its signal on that of the primary user, where the benefits of non-orthogonal transmission, i.e., high system capacity and improved user fairness, are obtained. Additionally, a simplified variant exploiting random phase shifts is proposed to avoid the high overhead of RIS channel estimation.
... R ECONFIGURABLE intelligent surfaces (RISs) represent one of the key enabling technologies for the nextgeneration wireless communication systems, making it possible to create an intelligent electromagnetic (EM) environment [1], [2], [3], [4]. Specifically, RISs are almost-planar structures engineered to dynamically control reflections and refractions of impinging EM waves. ...
Reconfigurable intelligent surfaces (RISs) are planar, almost 2-D, structures that can intelligently manipulate electromagnetic waves by low-cost near passive reflecting elements. RISs are considered a potential enabling technology for the sixth-generation (6G) wireless communication systems due to their capability to tune wireless signals, thus smartly controlling propagation environments. This work has a twofold objective: firstly, we present a systematic methodology for characterizing RISs in the time domain, which rigorously includes propagation delays and mutual coupling among RIS elements; secondly, we analyze the RISs through a convolution-based solver that is enriched by a specialized convolution scheme that enables the analysis of systems with complex linear, nonlinear as well terminations. The proposed approach is validated by extensive numerical evaluations against well-established methods based on both frequency- and time-domain analyses, with a varying number of RIS elements, interdistance, and frequency of operation.
... It can be used for obtaining real-time traffic information due to which vehicles can keep track of road conditions and cautions with its onboard communication tools [6]. It can also be used for accurate vehicle positioning, which is very beneficial for autonomous driving [7]. ...
In this paper, we propose an innovative framework for vehicular communication utilizing reconfigurable intelligent surfaces (RIS) in the millimeter-wave (mmWave) spectrum. Specifically, we consider a scenario where a base station (BS) employs multiple RISs to serve a vehicular user, considering both perfect and imperfect channel state information (ICSI) to account for real-world conditions. The vehicular user sends an uplink training pilot sequence, which is received by the BS through multiple RISs. We formulate an optimization problem to simultaneously optimize the precoder at the BS and the passive beamforming/passive phase shift matrix at each RIS in order to maximize the achievable rate for the vehicle. By leveraging the received signals with varying phase shifts, we provide a solution based on deep learning (DL) that effectively learns to utilize these signals for predicting the optimal phase shift matrix. Through extensive numerical analysis, we validate the effectiveness of our proposed solution by comparing it to the successive refinement (SR) benchmark scheme. Furthermore, it demonstrates that the proposed DL-based beamforming solution attains a performance level near to the maximum achievable rate with the increase in dataset size, eliminating the need for additional training overhead. The incorporation of RIS amplifies the achievable rate, especially in high-mobility scenarios, without necessitating additional complex beamforming solutions. In addition, we also evaluate the performance of the proposed solution in the presence of ICSI. We analyze the impact of the key system parameters, including the number of elements in the RIS, the speed of the vehicle, the transmit power, the distance between devices, and other relevant factors, on the performance of the considered system. INDEX TERMS Reconfigurable intelligent surface (RIS), beamforming, deep neural network (DNN), vehicular communication, millimeter wave (mmWave)
... As for the wall region Ξ wall , equations (4) still hold true provided that the area Ξ wall is par- (5) and the associated electric/magnetic local surface susceptibilities are set to a constant value over time (i.e., K ...
An innovative evolutionary method for the dynamic control of reconfigurable intelligent surfaces (RISs) is proposed. It leverages, on the one hand, on the exploration capabilities of evolutionary strategies and their effectiveness in dealing with large-scale discrete optimization problems and, on the other hand, on the implementation of memory-enhanced search mechanisms to exploit the time/space correlation of communication environments. Without modifying the base station (BS) beamforming strategy and using an accurate description of the meta-atom response to faithfully account for the micro-scale EM interactions, the RIS control (RISC) algorithm maximizes the worst-case throughput across all users without requiring that the Green's partial matrices, from the BS to the RIS and from the RIS to the users, be (separately) known/measured. Representative numerical examples are reported to illustrate the features and to assess the potentialities of the proposed approach for the RISC.
... Lemma 2. The ergodic channel capacity in (29) can be formulated in the closed-form expression as follows: ...
Reconfigurable intelligent surface (RIS) has recently gained significant interest as an emerging technology for future wireless networks thanks to its potential for improving the coverage probability in challenging propagation environments. This paper studies an RIS-assisted propagation environment, where a source transmits data to a destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, we derive closed-form expressions for the coverage probability and ergodic rate, which explicitly unveil the impact of both the propagation environment and the RIS on the system performance. Besides the optimization of the RIS phase profile, we formulate an RIS placement optimization problem with the aim of maximizing the coverage probability by relying only on partial channel state information. An efficient algorithm is proposed based on the gradient ascent method. Simulation results are illustrated in order to corroborate the analytical framework and findings. The proposed RIS phase profile is shown to outperform several heuristic benchmarks in terms of outage probability and ergodic rate. In addition, the proposed RIS placement strategy provides an extra degree of freedom that remarkably improves system performance.
... Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), have drawn significant interest from the wireless sector because of their considerable potential for managing propagation settings and thus improving wireless communication network performance [1]. In the traditional wireless communications, the transmitted radio waves interact uncontrollably with the surrounding objects, thus resulting in quality degradation in the received signal. ...
... Within this context, reconfigurable intelligent surface (RIS) has recently emerged as a disruptive technology that effectively improves both spectrum and energy efficiencies in wireless networks [8]- [13]. In general, an RIS is made of an artificial metasurface consisting of a large number of low-cost nearly passive elements. ...
Intelligent metasurface has recently emerged as a promising technology that enables the customization of wireless environments by harnessing large numbers of inexpensive configurable scattering elements. However, prior studies have predominantly focused on single-layer metasurfaces, which have limitations in terms of the number of beam patterns they can steer accurately due to practical hardware restrictions. In contrast, this paper introduces a novel stacked intelligent metasurface (SIM) design. Specifically, we investigate the integration of SIM into the downlink of a multiuser multiple-input single-output (MISO) communication system, where a SIM, consisting of a multilayer metasurface structure, is deployed at the base station (BS) to facilitate transmit beamforming in the electromagnetic wave domain. This eliminates the need for conventional digital beamforming and high-resolution digital-to-analog converters at the BS. To this end, we formulate an optimization problem that aims to maximize the sum rate of all user equipments by jointly optimizing the transmit power allocation at the BS and the wave-based beamforming at the SIM, subject to both the transmit power budget and discrete phase shift constraints. Furthermore, we propose a computationally efficient algorithm for solving this joint optimization problem and elaborate on the potential benefits of employing SIM in wireless networks. Finally, the numerical results corroborate the effectiveness of the proposed SIM-enabled wave-based beamforming design and evaluate the performance improvement achieved by the proposed algorithm compared to various benchmark schemes. It is demonstrated that considering the same number of transmit antennas, the proposed SIM-based system achieves about 200\% improvement in terms of sum rate compared to conventional MISO systems.
... In contrast to wireless networks ranging from the initial 1G to the latest 5G, which were primarily designed to overcome challenges posed by unpredictable radio conditions such as signal fading and blockages, RISs enable the establishment of a "smart radio environment" to facilitate the realization of 6G in a flexible and sustainable manner [206]. Significantly, the two-dimensional configuration and nearly passive operational characteristics of RISs contribute to their exceptional compatibility with prevailing wireless technologies. ...
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.
... However, RISs make strides toward intelligent control of EM waves: the sensing and feedback components act as the "eyes " to see the environment or meet the requirements, while onsite or cloud algorithms act as the "brains" to determine their output EM behavior. Hence, RISs have strong self-decision capabilities in which they can intelligently perform a series of successive software/algorithmdefined tasks, such as smart beam shaping [85], adaptive retroreflection [52], and gesture recognition [59], [89]. Although RIS research has been widely performed across nearly the whole spectrum, thus far, most of the reported RISs are aimed at engineering microwaves for the following reasons. ...
Metasurfaces, ultrathin two-dimensional version of metamaterials, have attracted tremendous attention due to their exotic capabilities to freely manipulate electromagnetic waves. By incorporating various tunable materials or elements into metasurface designs, reconfigurable metasurfaces and related metadevices with functionalities controlled by external stimuli can be realized, opening a new avenue to achieving dynamic manipulation of electromagnetic waves. Recently, based on the tunable metasurface concept, reconfigurable intelligent surfaces (RISs) have received significant attention and have been regarded as a promising emerging technology for future wireless communication due to their potential to enhance the capacity and coverage of wireless networks by smartly reconfiguring the wireless propagation environment. Here, in this article, we first focus on technical issues of RIS system implementation by reviewing the existing research contributions, paying special attention to designs in the microwave regime. Then, we showcase our recent attempts to practically demonstrate RIS systems in real-world applications, including deploying reflective RIS systems in indoor scenarios to enhance the wireless network coverage and utilizing intelligent omni-metasurfaces to improve both indoor and through-wall wireless communication quality. Finally, we give our own perspectives on possible future directions and existing challenges for RISs toward a truly commercial intelligent technology platform.
... Reconfigurable Intelligent Surfaces (RISs) are a new wireless transmission technology that enables the concept of smart radio environments in future generations of wireless communication networks. Intelligent Reflecting Surfaces (IRSs) are another term for RISs, which operate as reflectors and allow the phase response of adjustable unit element metasurfaces to be individually modified and optimized for beam-steering, focusing, and other related purposes [1]. Two main types of IRSs used in wireless networks is discussed in [2]. ...
This paper presents the substrate comparison on the multiband reflector performance at X-band frequency for an Intelligent Reflecting Surface (IRS). Functioning as a multiband reflector, the proposed reflector is using a circular multi-ring resonator on several substrates such as RO5880, F4BMX220, RO4003C, and FR4, with slightly different substrate thicknesses based on the available market, that can be operated at X-band frequency. The simulated results have shown in the graph of S-parameters and reflection phase among the substrates. In addition, the bandwidth of the proposed reflector is calculated based on the simulation results. Also, the incident wave angle effect against the proposed reflector is shifted along the horizontal axis. For the overall simulation results, the reflector that uses a RO5880 substrate has slightly better results and a wider bandwidth than the F4BMX220 substrate.
... Moreover, deep reinforcement learning (DRL) algorithms has been applied to overcome challenges in RIS beamforming in dynamic channel conditions, such as high-dimensional optimization, non-convexity, and imperfect channel state information (CSI) [29]- [31]. Besides, RIS-aided communication has drawn a lot of attention in mission-critical applications adopting finite block-length (FBL) transmission due to its smart interference management, better coverage capability, and high channel gain diversity [24], [32]- [37]. Based on the study given in [24], the deployment of large-scale RIS can offer even better diversity gains than a single-RIS deployment. ...
Next-generation wireless applications are expected to enable extended ultra-reliable low latency communication (xURLLC) to support high data rates along with ultra-high reliability and low end-to-end latency features beyond the capabilities of existing core services. These consolidated data rates and URLLC requirements in resource-constrained systems necessitate the shift from conventional architectures to more powerful and robust multiple access schemes. This paper investigates a multi-reconfigurable intelligent surface (RIS)-assisted rate-splitting multiple access (RSMA) to prompt an unconventional xURLLC service called mobile broadband reliable low latency communication (mBRLLC) for high spectral efficiency under finite block-length (FBL) transmission constraints. To enable spectral-efficient resource allocation, we formulate a sum throughput maximization problem for joint optimization of precoder design at the base-station (BS), block-length of common and private symbols of each user, and passive beamforming at each RIS. To solve the NP-hardness and non-convexity of the formulated problem, we use an alternating optimization technique to decouple the original problem into three sub-problems: active beamforming at the BS, block-length optimization, and passive beamforming at each RIS which are solved using general convex approximations. Simulations demonstrate the effectiveness of the proposed resource allocation algorithm over conventional schemes. The considered RSMA system achieves high data rates even with lower latency and higher reliability. Additionally, the investigation encompasses the evaluation of RIS deployment implications, the analysis of the worst-case latency scenario, and the assessment of the influence of channel estimation errors. Index Terms-Rate-splitting multiple access (RSMA), recon-figurable intelligent surface (RIS), multiple-input single-output (MISO), sum throughput maximization, ultra-reliable low latency communication (URLLC).
... To this end, reconfigurable intelligent surfaces (RIS) are emerging as the revolutionary technology based on programmable metamaterial-based surfaces and seem to be the fundamental component of 6G communication infrastructures in improving the above mentioned KPIs [21], [22]. In particular, conventional RIS plates comprises an array of nearly passive reflecting meta-elements, which are able to control impinging signals through a field programmable gate array (FPGA) microcontroller. ...
This paper focuses on providing a theoretical framework for the evaluation of the performance of holographic reconfigurable intelligent surface (HRIS) empowered terahertz (THz) wireless systems under fog conditions. In more detail, we present a comprehensive methodology for evaluating the geometric losses of the end-to-end channel. Moreover, the stochastic nature of the end-to-end channel is characterized by novel closed-form probability density and cumulative distribution functions. Building upon them, the outage probability and the throughput of the system are extracted in closed form. These formulas account for the impact of transceiver hardware imperfections and are expected to become useful tools for the design of HRIS empowered THz wireless systems due to its remarkable engineering insights.
... RISs are intended to engineer the propagation environment to improve the performance of wireless communications, especially in situations where the direct link between the transmit side (Tx) and receive side (Rx) is more or less blocked. Since many publications about RIS elaborate on why this is an interesting and promising technology [1], [2], we will not repeat this reasoning here but focus on the physically consistent modeling process and its consequences for the system optimization. ...
RISs are an emerging technology for engineering the channels of future wireless communication systems. The vast majority of research publications on RIS are focussing on system-level optimization and are based on very simplistic models ignoring basic physical laws. There are only a few publications with a focus on physical modeling. Nevertheless, the widely employed model is still inconsistent with basic physical laws. We will show that even with a very simple abstract model based on isotropic radiators, ignoring any mismatch, mutual coupling, and losses, each RIS element cannot be modeled to simply reflect the incident signal by manipulating its phase only and letting the amplitude unchanged. We will demonstrate the inconsistencies with the aid of very simple toy examples, even with only one or two RIS elements. Based on impedance parameters, the problems associated with scattering parameters can be identified enabling a correct interpretation of the derived solutions.
... 14 With the deployment of IRS in the communication system, 6G can reconfigure the wireless channels, and achieve the paradigm shift in wireless communication. [15][16][17] The IRS-aided mobile wireless communication system has attracted extensive attention to mitigate the Doppler shift caused by vehicular mobility. In Reference 18, the aerial intelligent reflecting surface (AIRS) assisted multiple-input multiple-output (MIMO) communication system was researched with multipath fading and Doppler shift, in which a narrowband channel model was proposed to characterize the AIRS-aided channel, and phase shifts were designed to mitigate Doppler shift impact. ...
Intelligent reflecting surface (IRS) has been recognized as one of the core technologies in the sixth generation (6G). Nevertheless, the character of channel state rapid change is a major problem in vehicular networks due to the dynamic vehicle movement and electromagnetic wave random scattering. In order to address the above problem, a strategy that joint phase shifts and channel alignment (PS‐CA) is proposed to reduce Doppler shift impact and improve communication performance in IRS‐aided vehicular networks. In this article, the channels can be divided into two types: the one type is the cascaded channel from the base station (BS) to the IRS and then to the vehicle; the other one is the direct channel from the BS to the vehicle. Thus, the proposed PS‐CA strategy can be divided into two stages. In the first stage, the cascaded channel fast fading state is reduced by designing IRS reflection phase shifts. In the second stage, considering the phase difference between the direct channel and the cascaded channel, we designed a correction function to align the direct channel with the cascaded channel. Then, we combined the designed correction function with IRS reflection phase shifts to reduce the impact of the direct channel fast fading state on overall performance. Simulation results demonstrate that compared to the RPS strategy, the PS‐CA strategy achieves higher spectral efficiency by 31.5%$$ \% $$.
... [16] introduced the emerging research area of RIS including wireless communication, proposed a framework for analyzing and optimizing RIS, and summarized the current status of RIS research as well as key research questions. Pan et al.[17] examined the role of RIS in the future 6G systems and suggested eight possible research directions. ...
One technology that has the potential to improve wireless communications in years to come is integrated sensing and communication (ISAC). In this study, we take advantage of reconfigurable intelligent surface's (RIS) potential advantages to achieve ISAC while using the same frequency and resources. Specifically, by using the reflecting elements, the RIS dynamically modifies the radio waves' strength or phase in order to change the environment for radio transmission and increase the ISAC systems' transmission rate. We investigate a single cell downlink communication situation with RIS assistance. Combining the ISAC base station's (BS) beamforming with RIS's discrete phase shift optimization, while guaranteeing the sensing signal, The aim of optimizing the sum rate is specified. We take advantage of alternating maximization to find practical solutions with dividing the challenge into two minor issues. The first power allocation subproblem is non-convex that CVX solves by converting it to convex. A local search strategy is used to solve the second subproblem of phase shift optimization. According to the results of the simulation, using RIS with adjusted phase shifts can significantly enhance the ISAC system's performance.
... IRS is considered a revolutionary technology for achieving green communications. Comprising a large number of low-cost passive reflecting elements, IRS forms a planar array that enhances the wireless network's performance by controlling the propagation environment [6]. First, IRS only reflects the received signals without amplification, eliminating the need for radio frequency (RF) chains, leading to lower energy consumption compared to traditional relays [7]. ...
This paper proposes an intelligent reflecting surface (IRS)-assisted energy efficiency optimization algorithm to address the problem of energy efficiency (EE) degradation in high-speed rail communication systems caused by line-of-sight link blockages between base stations and trains. The joint optimization of base station beamforming and IRS phase shifts is formulated as a variable-coupled energy efficiency maximization problem, subject to the base station’s transmission power and the IRS unit’s modulus constraints. This is known to be an NP-hard problem, making it challenging to obtain the global optimal solution. To tackle the issue of optimization variable coupling, an alternating optimization is employed to decompose the original problem into two sub-problems: base station beamforming and IRS phase-shift optimization. The Dinkelbach method is utilized to convert the fractional objective function into a difference form; then, the successive convex approximation (SCA) algorithm is applied to transform non-convex constraints into convex ones, which are solved using CVX. The Riemann conjugate gradient (RCG) algorithm can effectively solve the difficult unit module constraint. Finally, an alternating iterative strategy is employed to converge to a suboptimal solution. Our simulation results demonstrate that the proposed algorithm significantly enhances system efficiency with low computational complexity. Specifically, when the number of IRS reflecting elements is 64, the system’s EE is improved by approximately 12.41%, 35.26%, and 37.96% compared to the semi-definite relaxation algorithm, the random phase shift approach, and no IRS scheme, respectively.
... Several realizations of dynamically reconfigurable metasurfaces and metasurface-based antennas have been demonstrated (e.g. [17], [24]- [28]). The key underlining concept is typically based on controlling individual resonant elements within an array with electronics. ...
The ability to obtain dynamic control over an antenna radiation pattern is one of the main functions, desired in a vast range of applications, including wireless communications, radars, and many others. Widely used approaches include mechanical scanning with antenna apertures and phase switching in arrays. Both of those realizations have severe limitations, related to scanning speeds and implementation costs. Here we demonstrate a solution, where the antenna pattern is switched with optical signals. The system encompasses an active element, surrounded by a set of cylindrically arranged passive dipolar directors, functionalized with tunable impedances. The control circuit is realized as a bipolar transistor, driven by a photodiode. Light illumination in this case serves as a trigger, capable of either closing or opening the transistor, switching the impedance between two values. Following this approach, a compact half-a-wavelength footprint antenna, capable of switching between 6 dBi directional patterns within a few milliseconds’ latency was demonstrated. The developed light activation approach allows constructing devices with multiple almost non-interacting degrees of freedom, as a branched feeding network is not required. The capability of flexible switching between multiple electromagnetic degrees of freedom opens pathways to new wireless applications, where fast beam steering and beamforming performances are required.
With the improvement of fifth generation (5G) and beyond mobile technologies, Internet of Things (IoT) becomes more important in daily life as it provides many facilities for people in their homes and cities. The IoT can be considered a network of physical devices (“things”) used to connect and exchange information with other devices over the Internet. 5G and beyond technologies are expected to provide much more capacity and higher speed, helping the rapid growth of the IoT market. Nowadays, it is estimated that approximately 6–7 billion devices are connected through IoT technology and it is expected to increase to 20–22 billion in the near future. Smart Grid 3.0 is based on smart intelligence, automation, and data-enabled decisions, providing cost-effective electricity efficiency to consumers and reducing peak demand by enabling electrical utilities via smart technologies and improved security. So, IoT is expected to be a major technology for smart home and smart city applications and services that are also a part of the Smart Grid ecosystem. This chapter covers both IoT-based smart homes and smart cities. First, the IoT technology is introduced, and then, the IoT architecture is explained in the context of three layers included in its structure. In addition, new generation mobile technologies, namely 5G and beyond, are discussed for the IoT. After introducing the IoT technology with its architecture and enabling technologies, the chapter’s emphasis is on smart homes and smart cities by explaining protocols and architectures of smart environments with IoT-based services. In the third part of this chapter, the smart homes are presented in detail by mentioning smart home architectures, communication and medium protocols that can be used in smart homes, and several important services based on the IoT. Finally, the smart city concept alongside its architecture is given, and popular smart city services are explored.
In this paper, an approach is proposed toward two-dimensional (2D) beam tailoring in the terahertz band based on programmable metasurface loaded with liquid crystals. Specifically, a 1-bit reflective metasurface element is designed with switchable phase responses, and subsequently, an individually controllable metasurface array in 2D fashion is achieved by pixelating the metallic reflection back plate. As typical examples, programmable metasurfaces operating around 94 and 220 GHz are developed, respectively, and both simulation and experimental results confirm the powerful abilities of the metasurfaces in 2D wide-angle beam manipulations. In addition, the proposed method has advantages of wide frequency range, low cost, and high reliability, implying significant application prospects in terahertz reconfigurable intelligent surfaces and holographic imaging.
Digital metasurfaces define a novel methodology for metasurface designs by adopting discrete coding meta‐atoms to engineer electromagnetic waves in programmable ways. Herein, a novel field‐reorganizable digital metasurface (FRDM) that can be used to enhance the indoor non‐line‐of‐sight (NLOS) millimeter‐wave (mmWave) signal coverage is presented. The passive binary supercells which can be reorganized with an in situ optimization characteristic to deflect the incoming mmWaves into the blind area with adjustable angles are proposed. The indoor L‐shaped corridor signal coverage simulated by the ray‐tracing technique confirms that the NLOS blind area is effectively communicated using the passive FRDM. Practical environment experiments using FRDMs with different coding sequences are conducted, indicating that the averaged signal intensity is increased by over 10 dB in the NLOS blind area in a wide operating frequency band from 26 to 30 GHz by reorganizing the passive digital metasurfaces. The results suggest that the proposed FRDM enabled by the reorganizable binary supercells is a highly flexible, low‐cost, and extensible solution for B5G and 6G mmWave wireless communications.
In this manuscript, we propose an energy-efficient optimization framework for a multi-cluster simultaneous transmitting and reflecting intelligent reflecting surfaces (STAR-IRS) enabled time-division multiple-access (TDMA) based hybrid-NOMA system to realize the future sixth-generation (6G) wireless communication systems. Specifically, the energy-efficiency maximization is achieved by optimizing the successive-interference cancellation (SIC) decoding order, time-allocation, and active-beamforming vectors at the transmitter, as well as transmission and reflection coefficients at the STAR-IRS under quality-of-service (QoS), conservation of energy, time-allocation, phase-shifts, and SIC-decoding constraints. Moreover, the proposed alternating optimization algorithm tackles the considered highly non-convex optimization problem in four steps. In first step, for computing the SIC-decoding order of NOMA users, an efficient optimization technique is proposed which maximizes the sum of combined channel gains by optimizing the transmission and reflection beamforming vectors of the considered STAR-IRS assisted hybrid-NOMA system. Further, in second step, an optimal time-allocation for each cluster in transmission and reflection region is computed for given SIC-decoding order. With decoding order and time-allocation in hand, active-beamforming vectors are computed by exploiting the sequential-convex approximation (SCA) and second-order-conic programming (SOCP) in third step. Finally, in the fourth step, the transmission and reflection coefficients of STAR-IRS are computed by transforming the non-convex optimization problem into a semi-definite programming (SDP) problem. The numerical simulation results demonstrate that the proposed optimization framework exhibits high energy efficiency performance and converges within a few iterations.
Reconfigurable intelligent surfaces (RISs) are widely considered to become an integral part of future wireless communication systems. Various methodologies exist to design such surfaces; however, most consider or require a very large number of tunable components. This not only raises system complexity, but also significantly increases power consumption. Sparse RISs (SRISs) consider using a smaller or even minimal number of tunable components to improve overall efficiency while maintaining sufficient RIS capability. The versatile semidefinite relaxation-based optimization method previously applied to transmit array antennas is adapted and applied accordingly, to evaluate the potential of different SRIS configurations. Because the relaxation is tight in all cases, the maximum possible performance is found reliably. Hence, with this approach, the trade-off between performance and sparseness of SRIS can be analyzed. Preliminary results show that even a much smaller number of reconfigurable elements, e.g. only 50%, can still have a significant impact.
In this paper, we consider a reconfigurable intelligent surface (RIS) and model it by using multiport network theory. We first compare the representation of RIS by using $Z$-parameters and $S$-parameters, by proving their equivalence and discussing their distinct features. Then, we develop an algorithm for optimizing the RIS configuration in the presence of electromagnetic mutual coupling. We show that the proposed algorithm based on optimizing the $S$-parameters results in better performance than existing algorithms based on optimizing the $Z$-parameters. This is attributed to the fact that small perturbations of the step size of the proposed algorithm result in larger variations of the $S$-parameters, hence increasing the convergence speed of the algorithm.
Optically-transparent opportunistic electromagnetic skins (OTO-EMSs) are proposed to enable outdoor-to-indoor (O2I) millimiter-wave (mmW) wireless communications with existing windows/glass-panels. More in detail, static passive EMSs consisting of optically-transparent conducting patterned layers attached to standard glass-panels are designed. Towards this end, both the phase coverage and the optical transparency of a meshed copper-based meta-atom printed on a non-dedicated insulated glass substrate are optimized. Successively, the feasibility of OTO-EMSs able to support mmW high-efficiency O2I transmissions along non-Snell refraction directions is numerically demonstrated.
Intelligent reflecting surface (IRS) is a promising technology to enhance the coverage and performance of wireless networks. We consider the application of IRS to non-orthogonal multiple access (NOMA), where a base station transmits superposed signals to multiple users by the virtue of an IRS. The performance of an IRS-assisted NOMA networks with imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC) is investigated by invoking 1-bit coding scheme. In particular, we derive new exact and asymptotic expressions for both outage probability and ergodic rate of the m-th user with ipSIC/pSIC. Based on analytical results, the diversity order of the m-th user with pSIC is in connection with the number of reflecting elements and channel ordering. The high signal-to-noise radio (SNR) slope of ergodic rate for the m-th user is obtained. The throughput and energy efficiency of IRS-NOMA networks are discussed both in delay-limited and delay-tolerant transmission modes. Additionally, we derive new exact expressions of outage probability and ergodic rate for IRS-assisted orthogonal multiple access (IRS-OMA). Numerical results are presented to substantiate our analyses and demonstrate that: i) The outage behaviors of IRS-NOMA are superior to that of IRS-OMA and relaying schemes; ii) The M-th user has a larger ergodic rate than IRS-OMA and benchmarks. However, the ergodic performance of the m-th user exceeds relaying schemes in the low SNR regime; and iii) The IRS-assisted NOMA networks have ability to achieve the enhanced energy efficiency compared to conventional cooperative communications.
Thanks to the strong ability against the inter-cell interference, cell-free network is considered as a promising technique to improve network capacity. However, further capacity enhancement requires to deploy more base stations (BSs) with high cost and power consumption. To address this issue, inspired by the recently developed reconfigurable intelligent surface (RIS) technique, we propose the concept of RIS-aided cell-free network to improve the capacity with low cost and power consumption. The key idea is to replace some of the required BSs by low-cost and energy-efficient RISs. Then, in a wideband RIS-aided cell-free network, we formulate the problem of joint precoding design at BSs and RISs to maximize the network capacity. Due to the non-convexity and high complexity of the formulated problem, we develop an alternating optimization algorithm to solve this challenging problem. In particular, we decouple this problem via fractional programming, and solve the subproblems alternatively. Note that most of the considered scenarios in existing works are special cases of the general scenario in this paper, and the proposed joint precoding framework can also serve as a general solution to maximize the capacity in most of the existing RIS-aided scenarios. Finally, simulation results demonstrate that, compared with the conventional cell-free network, the network capacity under the proposed scheme can be improved significantly.
Owing to the envisioned new use-cases, such as immersive virtual reality and high-fidelity mobile hologram, and their potential challenging new requirements for future wireless networks, extensive research has already started on 6G and beyond wireless technologies. Despite the fact that several modern physical layer solutions have been introduced in the past decade, a level of saturation has been reached in terms of the available spectrum and adapted modulation/coding solutions, which accordingly limits the maximum capacity and reliability. Within this respective, reconfigurable intelligent surface (RIS)-empowered communication appears as a potential candidate to overcome the inherent drawbacks of legacy wireless systems. The core idea of RIS-assisted communication is the transformation of the random and uncontrollable wireless propagation environment into a reconfigurable communication system entity that plays an active role in conveying information and improving system performance. In this paper, the well-known multipath fading phenomenon is revisited in mobile wireless communication systems, and novel and unique solutions are introduced from the perspective of RISs. The feasibility of eliminating or mitigating the multipath fading effect stemming from the movement of mobile receivers is also investigated by utilizing RISs. It is shown that rapid fluctuations in the received signal strength due to the Doppler effect can be effectively reduced by using real-time tunable RISs. It is also proven that for a hypothetical propagation environment where all reflectors are coated with RISs, the multipath fading effect can be totally eliminated. Furthermore, we show that for more general propagation environments with several interacting objects, even a few real-time tunable RISs can remarkably reduce the Doppler spread and the deep fades in the received signal.
This paper deals with channel estimation in reconfigurable intelligent surface (RIS) aided multiple-input multiple-output (MIMO) time-division duplexing systems. In a typical RIS assisted communication, an RIS is deployed in the close proximity of communication devices, thus resulting in ill-conditioned low-rank channel matrices. To effectively estimate these channels, we propose a two-stage channel estimation method. Specifically, in the first stage, the direct MIMO channel between the end terminals is estimated by utilizing the conventional uplink training approach. In the second stage, after the training process, it is noticed that the RIS channel estimation problem becomes equivalent to a well-known dictionary learning problem. Therefore, we propose to use a bilinear adaptive vector approximate message passing (BAdVAMP) algorithm to estimate RIS channels, which has been shown to be accurate and robust for ill-conditioned dictionary learning problems in compressed sensing. We also propose a phase shift design (passive beamforming) for the RIS by formulating an optimization problem that maximizes the total channel gain at the receiver. Due to its non-convex nature, an approximate closed-form solution is proposed to obtain the phase shift matrix. Numerical results show that the proposed BAdVAMP based RIS channel estimation performs better than its counterpart bilinear generalized AMP (BiGAMP) scheme.
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.
Reconfigurable intelligent surfaces (RISs) have recently emerged as a promising technology that can achieve high spectrum and energy efficiency for future wireless networks by integrating a massive number of low-cost and passive reflecting elements. An RIS can manipulate the properties of an incident wave, such as the frequency, amplitude, and phase, and, then, reflect this manipulated wave to a desired destination, without the need for complex signal processing. In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations. In particular, a passive beamformer that can achieve the asymptotic optimal performance by controlling the incident wave properties is designed, under a limited RIS control link and practical reflection coefficients. In order to increase the achievable system sum-rate, a modulation scheme that can be used in an RIS without interfering with existing users is proposed and its average symbol error rate is asymptotically derived. Moreover, a new resource allocation algorithm that jointly considers user scheduling and power control is designed, under consideration of the proposed passive beamforming and modulation schemes. Simulation results show that the proposed schemes are in close agreement with their upper bounds in presence of a large number of RIS reflecting elements thereby verifying that the achievable rate in practical RISs satisfies the asymptotic optimality.
Terahertz (THz) communications open a new frontier for the wireless network thanks to their dramatically wider available bandwidth compared to the current micro-wave and forthcoming millimeter-wave communications. However, due to the short length of THz waves, they also suffer from severe path attenuation and poor diffraction. To compensate the THz-induced propagation loss, this paper proposes to combine two promising techniques, viz., massive multiple input multiple output (MIMO) and intelligent reflecting surface (IRS), in THz multi-user communications, considering their significant beamforming and aperture gains. Nonetheless, channel estimation and low-cost beamforming turn out to be two main obstacles to realizing this combination, due to the passivity of IRS for sending/receiving pilot signals and the large-scale use of expensive RF chains in massive MIMO. In view of these limitations, this paper first develops a cooperative beam training scheme to facilitate the channel estimation with IRS. In particular, we design two different hierarchical codebooks for the proposed training procedure, which are able to balance between the robustness against noise and searching complexity. Based on the training results, we further propose two cost-efficient hybrid beamforming (HB) designs for both single-user and multi-user scenarios, respectively. Simulation results demonstrate that the proposed joint beam training and HB scheme is able to achieve close performance to the optimal fully digital beamforming which is implemented even under perfect channel state information (CSI).
In this6study, the channel estimation problem is investigated for a wireless communication system assisted by a reconfigurable intelligent surface (RIS). The RIS thus creates an assistant channel, which has the features of positivity and dominance. Owing to these features, the channel estimation problem is formulated as a constrained residual sum of squares minimisation problem, which differs radically from the traditional channel estimation issue. An efficient Lagrange multiplier and dual ascent‐based estimation scheme is then designed to obtain an iterative solution for the estimator. Moreover, the Cramér–Rao lower bounds are deduced as a performance benchmark. Simulation results show that the authors' designed scheme improves the estimation accuracy up to 33%, compared with the conventional least‐square method in the low signal‐to‐noise ratio regime.
Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this paper, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms.
5G radio for millimeter-wave (mm-wave) and beyond5G concepts at 0.1-1 THz can exploit angle and delay measurements for localization through an increased bandwidth and large antenna arrays, but they are limited in terms of blockage caused by obstacles. Reconfigurable intelligent surfaces (RISs) are seen as a transformative technology that can control the physical propagation environment in which they are embedded by passively reflecting radio waves in preferred directions and actively sensing this environment in receive and transmit modes. While such RISs have mainly been intended for communication purposes, they can provide great benefits in terms of performance, energy consumption, and cost for localization and mapping. These benefits as well as associated challenges are the main topics of this article.
Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this paper, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.
In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)’s beamforming and the IRS’s reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, a modified PDS scheme is presented to trace the channel dynamic and adjust the beamforming policy against channel uncertainty accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.
Millimeter wave communication is eminently suitable for high-rate wireless systems, which may be beneficially amalgamated with intelligent reflecting surfaces (IRS), while relying on beam-index modulation. Explicitly, we propose three different architectures based on IRSs for beam-index modulation in millimeter wave communication. Our schemes are capable of eliminating the detrimental line-of-sight blockage of millimeter wave frequencies.The schemes are termed as single-symbol beam index modulation, multi-symbol beam-index modulation and maximum-SNR single-symbol beam index modulation. The principle behind these is to embed the information both in classic QAM/PSK symbols and in the transmitter beam-pattern. Explicitly, we proposed to use a twin-IRS structure to construct a low-cost beamindex modulation scheme. We conceive both the optimal maximum likelihood detector and a low-complexity compressed sensing detector for the proposed schemes. Finally, the schemes designed are evaluated through extensive simulations and the results are compared to our analytical bounds.
Considering reconfigurable intelligent surfaces (RISs), we study a multi-cluster multiple-input-single-output (MISO) non-orthogonal multiple access (NOMA) downlink communication network. In the network, RISs assist the communication from the base station (BS) to all users by passive beamforming. Our goal is to minimize the total transmit power by jointly optimizing the active beamforming matrices at the BS and the reflection coefficient vector at the RISs. Because of the constraints on the RIS reflection amplitudes and phase shifts, the formulated quadratically constrained quadratic problem is highly non-convex. For the aforementioned problem, the conventional semidefinite programming (SDP) based algorithm has prohibitively high computational complexity and deteriorating performance. Here, we propose an effective second-order cone programming (SOCP)-alternating direction method of multipliers (ADMM) based algorithm to obtain the locally optimal solution. To reduce the computational complexity, we also propose a low-complexity zero-forcing based suboptimal algorithm. It is shown through simulation results that our proposed SOCP-ADMM based algorithm achieves significant performance gain over the conventional SDP based algorithm. Furthermore, when the target transmission rates of central and cell-edge users are 0.5 bps/Hz, our proposed NOMA RIS-aided system with 32 RIS elements has about 2.5 dB performance gain over the conventional massive multiple-input-multiple-output system with 64 transmit antennas. Index Terms-Alternating direction method of multipliers (ADMM), multiple-input-single-output (MISO), non-orthogonal multiple access (NOMA), reconfigurable intelligent surfaces (RIS-s), zero-forcing (ZF).
This paper considers an artificial noise (AN)-aided secure MIMO wireless communication system. To enhance the system security performance, the advanced intelligent reflecting surface (IRS) is invoked, and the base station (BS), legitimate information receiver (IR) and eavesdropper (Eve) are equipped with multiple antennas. With the aim for maximizing the secrecy rate (SR), the transmit precoding (TPC) matrix at the BS, covariance matrix of AN and phase shifts at the IRS are jointly optimized subject to constrains of transmit power limit and unit modulus of IRS phase shifts. Then, the secrecy rate maximization (SRM) problem is formulated, which is a non-convex problem with multiple coupled variables. To tackle it, we propose to utilize the block coordinate descent (BCD) algorithm to alternately update the variables while keeping SR non-decreasing. Specifically, the optimal TPC matrix and AN covariance matrix are derived by Lagrangian multiplier method, and the optimal phase shifts are obtained by Majorization-Minimization (MM) algorithm. Since all variables can be calculated in closed form, the proposed algorithm is very efficient. We also extend the SRM problem to the more general multiple-IRs scenario and propose a BCD algorithm to solve it. Simulation results validate the effectiveness of system security enhancement via an IRS.
The use of large arrays might be the solution to the capacity problems in wireless communications. The signal-to-noise ratio (SNR) grows linearly with the number of array elements N when using Massive MIMO receivers and half-duplex relays. Moreover, intelligent reflecting surfaces (IRSs) have recently attracted attention since these can relay signals to achieve an SNR that grows as N2, which seems like a major benefit. In this paper, we use a deterministic propagation model for a planar array of arbitrary size, to demonstrate that the mentioned SNR behaviors, and associated power scaling laws, only apply in the far-field. They cannot be used to study the regime where N∞. We derive an exact channel gain expression that captures three essential near-field behaviors and use it to revisit the power scaling laws. We derive new finite asymptotic SNR limits but also conclude that these are unlikely to be approached in practice. We further prove that an IRS-aided setup cannot achieve a higher SNR than an equal-sized Massive MIMO setup, despite its faster SNR growth. We quantify analytically how much larger the IRS must be to achieve the same SNR. Finally, we show that an optimized IRS does not behave as an “anomalous” mirror but can vastly outperform that benchmark.
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.
In this paper, the adoption of an intelligent reflecting surface (IRS) for multiple user pairs in two-hop networks is investigated. Different from the existing studies on IRS that mainly focused on tuning the reflection coefficients of all elements, we consider the implementation of
true
reflection resource management (RRM) through the identification of the best triggered module subset. More precisely, the implementation of
true
RRM builds on the premise of our proposed modular IRS structure consisting of multiple independent and controllable modules. In the context of modular IRS structure, we investigate the signal-to-interference-plus-noise ratio (SINR)-based max-min problem subject to per source terminals (STs) power budgets and module size constraint, via joint triggered module subset identification, transmit power allocation, and the corresponding passive beamforming. Whereas this problem is NP-hard due to the module size constraint, which can be addressed by the convex sparsity-inducing approximation to the hard module size constraint using mixed
$\ell _{1,F}\text {-norm}$
, where it yields a suitable semidefinite relaxation. Using techniques from separable convex programming, we provide a two-block alternating direction method of multipliers (ADMM) algorithm for the approximated problem. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm as a function of the system parameters. Further energy efficiency (EE) performance comparison demonstrates the necessity and meaningfulness of the introduced modular IRS structure. Specifically, for a given network setting, there is an optimal value of the number of triggered modules for system, when the EE is considered.
In this paper, we introduce a physics-consistent analytical characterization of the free-space path-loss of a wireless link in the presence of a reconfigurable intelligent surface. The proposed approach is based on the vector generalization of Green’s theorem. The obtained path-loss model can be applied to two-dimensional homogenized metasurfaces, which are made of sub-wavelength scattering elements and that operate either in reflection or transmission mode. The path-loss is formulated in terms of a computable integral that depends on the transmission distances, the polarization of the radio waves, the size of the surface, and the desired surface transformation. Closed-form expressions are obtained in two asymptotic regimes that are representative of far-field and near-field deployments. Based on the proposed approach, the impact of several design parameters and operating regimes is unveiled.
Intelligent reflecting surface (IRS) offers a cost-effective solution to link blockage problem in mmWave communications, and the prerequisite of which is the accurate estimation of (1) the optimal beams for base station/access point (BS/AP) and mobile terminal (MT), (2) the optimal reflection patterns for IRSs, and (3) link blockage. In this paper, we carry out beam training designs for IRSs assisted mmWave communications to estimate the aforementioned parameters. To acquire the optimal beams and reflection patterns, we firstly perform random beamforming and maximum likelihood estimation to estimate angle of arrival (AoA) and angle of departure (AoD) of the line of sight (LoS) path between BS/AP (or IRSs) and MT. Then, with the estimated AoDs, we propose an iterative positioning algorithm that achieves centimeter-level positioning accuracy. The obtained location information is not only a fringe benefit but also enables us to cross verify and enhance the estimation of AoA and AoD, and it also facilitates the estimation of blockage indicator. Numerical results show the superiority of our proposed beam training scheme and verify the performance gain brought by location information.
Channel estimation is challenging for the reconfigurable intelligent surface (RIS)-aided wireless communications. Since the number of coefficients of the cascaded channel among the base station (BS), the RIS and the user equipment (UE) is the product of the number of BS antennas, the number of RIS elements, and the number of UEs, the pilot overhead can be prohibitively high. In this paper, we propose a two-timescale channel estimation framework to exploit the property that the BS-RIS channel is high-dimensional but quasi-static, while the RIS-UE channel is mobile but low-dimensional. Specifically, to estimate the quasi-static BS-RIS channel, we propose a dual-link pilot transmission scheme, where the BS transmits downlink pilots and receives uplink pilots reflected by the RIS. Then, we propose a coordinate descent-based algorithm to recover the BS-RIS channel. Since the quasi-static BS-RIS channel is estimated less frequently than the mobile channel is, the average pilot overhead can be reduced from a long-term perspective. Although the mobile RIS-UE channel has to be frequently estimated in a small timescale, the associated pilot overhead is low thanks to its low dimension. Simulation results show that the proposed two-timescale channel estimation framework can achieve accurate channel estimation with low pilot overhead.
The employment of intelligent reflecting surfaces (IRSs) is a potential and promising solution to increase the spectral and energy efficiency of wireless communication networks. Despite their many advantages, IRS-aided communications have limitations as they are subject to high propagation losses. To overcome this, the phase rotation (shift) at each element needs to be designed in such a way as to increase the channel gain at the destination. However, this increases the system’s complexity as well as its power consumption. In this paper, we present an analytical framework for the performance of random rotation-based IRS-aided communications. Under this framework, we propose four low-complexity and energy efficient schemes, based on a coding or a selection approach. Both of these approaches employ random phase rotations and require limited knowledge of channel state information. Specifically, the coding-based schemes use time-varying random phase rotations to produce an equivalent time-varying channel. On the other hand, the selection-based schemes select a partition of the IRS elements based on the received signal power at the destination. Analytical expressions for the achieved outage probability and energy efficiency of each scheme are derived. It is demonstrated that all schemes can provide significant performance gains as well as full diversity order.
Power-domain non-orthogonal multiple access (NOMA) has become a promising technology to exploit the new dimension of the power domain to enhance the spectral efficiency of wireless networks. However, most existing NOMA schemes rely on the strong assumption that users’ channel gains are quite different, which may be invalid in practice. To unleash the potential of power-domain NOMA, we propose a reconfigurable intelligent surface (RIS)-empowered NOMA scheme to introduce desirable channel gain differences among the users by adjusting the phase shifts at the RIS. Our goal is to minimize the total transmit power by jointly optimizing the beamforming vectors at the base station, the phase-shift matrix at the RIS, and user ordering. To address challenge due to the highly coupled optimization variables, we present an alternating optimization framework to decompose the non-convex bi-quadratically constrained quadratic problem under a specific user ordering into two rank-one constrained matrices optimization problems via matrix lifting. To accurately detect the feasibility of the non-convex rank-one constraints and improve performance by avoiding early stopping in the alternating optimization procedure, we equivalently represent the rank-one constraint as the difference between nuclear norm and spectral norm. A difference-of-convex (DC) algorithm is further developed to solve the resulting DC programs via successive convex relaxation, followed by establishing the convergence of the proposed DC-based alternating optimization method. We further propose an efficient user ordering scheme with closed-form expressions, considering both the channel conditions and users’ target data rates. Simulation results validate the ability of an RIS in enlarging the channel-gain difference when the users’ original channel conditions are similar and the superiority of the proposed DC-based alternating optimization method in reducing the total transmit power.
While millimeter-wave (mmWave) communications can enjoy abundant bandwidth resource, their high susceptibility to blockage poses serious challenges to low-latency services. In this paper, a novel intelligent reflecting surface (IRS)-assisted mmWave scheme is proposed to overcome the impact of blockage. The scheme minimizes the user power of a multi-user mmWave system by jointly optimizing the transmit powers of the devices, the multi-user detector at the base station, and the passive beamforming at the IRS, subject to delay requirements. An alternating optimization framework is developed to decompose the joint optimization problem into three subproblems iteratively optimized till convergence. In particular, closed-form expressions are devised for the update of the powers and multi-user detector. The IRS configuration is formulated as a sum-of-inverse minimization (SIMin) fractional programming problem and solved by exploiting the alternating direction method of multipliers (ADMM). The configuration is also interpreted as a latency residual maximization problem, and solved efficiently by designing a new complex circle manifold optimization (CCMO) method. Numerical results corroborate the effectiveness of our scheme in terms of power saving, as compared with a semidefinite relaxation-based alternative.
The fundamental capacity limits of intelligent reflecting surface (IRS)-assisted multi-user wireless communication systems are investigated in this paper. Specifically, the capacity and rate regions for both capacity-achieving non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission schemes are characterized by jointly optimizing the IRS reflection matrix and wireless resource allocation under the constraints of a maximum number of IRS reconfiguration times. In NOMA, all users are served in the same resource blocks by employing superposition coding and successive interference cancelation techniques. In OMA, all users are served by being allocated orthogonal resource blocks of different sizes. For NOMA, the ideal case with an asymptotically large number of IRS reconfiguration times is firstly considered, where the optimal solution is obtained by employing the Lagrange duality method. Inspired by this result, an inner bound of the capacity region for the general case with a finite number of IRS reconfiguration times is derived. For OMA, the optimal transmission strategy for the ideal case is to serve each individual user alternatingly with its effective channel power gain maximized. Based on this result, a rate region inner bound for the general case is derived. Finally, numerical results are provided to show that: i) a significant capacity and rate region improvement can be achieved by using IRS; ii) the capacity gain can be further improved by dynamically configuring the IRS reflection matrix.
Reconfigurable intelligent surfaces (RISs) are regarded as a promising emerging hardware technology to improve the spectrum and energy efficiency of wireless networks by artificially reconfiguring the propagation environment of electromagnetic waves. Due to the unique advantages in enhancing wireless channel capacity, RISs have recently become a hot research topic. In this article, we focus on three fundamental physical-layer challenges for the incorporation of RISs into wireless networks, namely, channel state information acquisition, passive information transfer, and low-complexity robust system design. We summarize the state-of-the-art solutions and explore potential research directions. Furthermore, we discuss other promising research directions of RISs, including edge intelligence and physical-layer security.
Reconfigurable intelligent surface (RIS) as an emerging cost-effective technology can enhance the spectral- and energy-efficiency of wireless networks. In this article, we consider an RIS-aided green edge inference system, where the inference tasks generated from resource-constrained mobile devices (MDs) are uploaded to and cooperatively performed at multiple resource-enhanced base stations (BSs). Taking into account both the computation and uplink/downlink transmit power consumption, we formulate an overall network power consumption minimization problem, which calls for the joint design of the set of tasks performed by each BS, uplink/downlink beamforming vectors of BSs, transmit power of MDs, and uplink/downlink phase-shift matrices at the RIS. However, the resulting combinatorial optimization problem is nonconvex and highly intractable. We tackle the challenge of combinatorial variables by exploiting the group sparsity structure of the beamforming vectors. Moreover, a block-structured optimization with mixed
$\ell _{1,2}$
-norm and difference-of-convex-functions (DC) based three-stage framework is proposed to solve the problem, where the mixed
$\ell _{1,2}$
-norm and DC techniques are adopted to induce the group sparsity structure and handle the nonconvex rank-one constraint, respectively. Simulations demonstrate the supreme performance gain of deploying an RIS and confirm the effectiveness of the proposed algorithm over the baseline algorithms in reducing the overall network power consumption.
Metasurfaces have drawn significant attentions due to their superior capability in tailoring electromagnetic waves with a wide frequency range, from microwave to visible light. Recently, programmable metasurfaces have demonstrated the ability of manipulating the amplitude or phase of electromagnetic waves in a programmable manner in real time, which renders them especially appealing in the applications of wireless communications. In this paper, we present the fundamental principle of applying programmable metasurface as transmitter for wireless communications. Then, we establish a prototype system of meta-surface-based transmitter to conduct several experiments and measurements over the air, which practically demonstrate the feasibility of using programmable metasurfaces in future communication systems. By exploiting the dynamically controllable property of programmable metasurface, the design, implementation and experimental evaluation of the proposed metasurface-based wireless communication system are presented with the prototype, which realizes single carrier quadrature phase shift keying (QPSK) transmission over the air. In the developed prototype, the phase of the reflected electromagnetic wave of programmable metasurface is directly manipulated in real time according to the baseband control signal, which achieves 2.048 Mbps data transfer rate with video streaming transmission over the air. In addition, experimental result is provided to compare the performance of the proposed metasurface-based architecture against the conventional one. With the slight increase of the transmit power by 5 dB, the same bit error rate (BER) performance can be achieved as the conventional system in the absence of channel coding. Such a result is encouraging considering that the metasurface-based system has the advantages of low hardware cost and simple structure, thus leading to a promising new architecture for wireless communications.
In this paper, an intelligent reflecting surface (IRS)-aided secure wireless information and power transfer system is studied. To maximize the harvested power of energy harvesting receiver (EHR), we optimize the secure transmit beamforming at the access point (AP) and phase shifts at the IRS subject to the secrecy rate (SR) and the reflecting phase shifts at the IRS constraints. Due to the non-convexity of optimization problem and coupled optimization variables, we convert the optimization problem into a semidefinite relaxation (SDR) problem and a sub-optimal solution is obtained. To reduce the high-complexity of the proposed SDR method, a low-complexity alternating optimization (LC-AO) algorithm is proposed. Simulation results show that the harvested power of the proposed SDR and LC-AO methods approximately double that of the existing method without IRS with the same SR. In particular, the proposed LC-AO achieves almost the same performance as the proposed SDR but with a much lower complexity.
In this letter, the use of intelligent reflecting surface (IRS) to enhance the physical layer security of downlink wireless communication is investigated. Assuming a single-antenna legitimate user and a multi-antenna eavesdropper, we propose an effective algorithm to jointly optimize the active and passive beamforming. In the proposed algorithm, the optimal transmit beamforming vector at the BS under fixed IRS phase shifts is derived, and a low-complexity algorithm based on fractional programming (FP) and manifold optimization (MO) is proposed to obtain near optimal IRS phase shifts. Simulation results demonstrate that the proposed algorithm can almost achieve the performance upper bound with a fast convergence rate.
In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the same performance as computationally complex dirty paper coding, where the quasi-degradation condition is satisfied, conditioned on the users’ channels fall in the quasi-degradation region. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access.
Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs). Meanwhile, intelligent reflecting surface (IRS), also known as reconfigurable intelligent surface (RIS), has been recently proposed as a promising approach to enhance energy efficiency (EE) of wireless communication systems through intelligently reconfiguring the channel environment. To improve both SE and EE, in this paper, we introduce multiple IRSs to a downlink multiple-input single-output (MISO) CR system, in which a single SU coexists with a primary network with multiple PU receivers (PU-RXs). Our design objective is to maximize the achievable rate of SU subject to a total transmit power constraint on the SU transmitter (SU-TX) and interference temperature constraints on the PU-RXs, by jointly optimizing the beamforming at SU-TX and the reflecting coefficients at each IRS. Both perfect and imperfect channel state information (CSI) cases are considered in the optimization. Numerical results demonstrate that IRS can significantly improve the achievable rate of SU under both perfect and imperfect CSI cases.
Millimeter wave (MmWave) communications is capable of supporting multi-gigabit wireless access thanks to its abundant spectrum resource. However, severe path loss and high directivity make it vulnerable to blockage events, which can be frequent in indoor and dense urban environments. To address this issue, in this paper, we introduce intelligent reflecting surface (IRS) as a new technology to provide effective reflected paths to enhance the coverage of mmWave signals. In this framework, we study joint active and passive precoding design for IRS-assisted mmWave systems, where multiple IRSs are deployed to assist the data transmission from a base station (BS) to a single-antenna receiver. Our objective is to maximize the received signal power by jointly optimizing the transmit precoding vector at the BS and the phase shift parameters used by IRSs for passive beamforming. Although such an optimization problem is generally non-convex, we show that, by exploiting some important characteristics of mmWave channels, an optimal closed- orm solution can be derived for the single IRS case and a near-optimal analytical solution can be obtained for the multi-IRS case. Our analysis reveals that the received signal power increases quadratically with the number of reflecting elements for both the single IRS and multi-IRS cases. Simulation results are included to verify the optimality and near-optimality of our proposed solutions. Results also show that IRSs can help create effective virtual LOS paths and thus substantially improve robustness against blockages in mmWave communications.
In this article, we introduce Wireless 2.0, the future generation of wireless communication networks, in which the radio environment becomes controllable and intelligent by leveraging the emerging technologies of reconfigurable metasurfaces (RMSs) and artificial intelligence (AI). In particular, we emphasize AI-based computational methods and commence with an overview of the concept of intelligent radio environments (IREs) based on RMSs. Then, we elaborate on data management aspects, the requirements of supervised learning by examples, and the paradigm of reinforcement learning to learn by acting. Finally, we highlight numerous open challenges and research directions.