No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Utility Maximization for Multi-UAV-assisted IoT Sensor Systems With NOMA
Abstract
In this paper, we consider a multi-unmanned aerial vehicle (UAV)-assisted Internet-of-things (IoT) sensor system with non-orthogonal multiple access (NOMA) and aim to maximize the sum utility of bit rates of all IoT sensors through the joint optimization of user association, three-dimensional (3-D) UAV placement, decoding ordering, and power allocation. To cope with the formulated mixed-integer non-convex problem, we decompose it into four subproblems relating to each dimension of radio resources under the alternating optimization framework. For the user association or decoding ordering subproblems involving only binary variables, a novel algorithm is proposed by leveraging variable relaxation, fractional programming, and efficient bounding. For the non-convex 3-D UAV placement subproblem, the sequential quadratic programming algorithm is applied to obtain an efficient sub-optimal solution by solving a sequence of quadratic programming problems that are iteratively modified. For the power allocation subproblem, the global-optimal solution is achieved based on auxiliary variables, variable transformation, and convex optimization. Simulation results show that the proposed resource allocation strategy outperforms the state-of-the-art benchmarks that are based on the greedy algorithm, the K-means algorithm, the geometric center-based algorithm, and the deep reinforcement learning, and strikes a good balance between performance and complexity in contrast to those based on the genetic algorithm and the brute-force search.
... Compared with the earliest SPR sensor utilizing a prism structure, the structure of the PCF-based SPR sensor is simpler, higher in integration, and lower in cost. Therefore, the PCF sensor-based SPR is broadly used to measure temperature, 5,6 strain, 7 vibrations, 8 distortions, 9 magnetic fields, 10 RI, 11 and biological 12 samples, it can be widely adopted in the emerging Internet-of-things 13,14 or medical sensing applications. ...
An all-fiber refractive index and temperature sensor based on D-type photonic crystal fiber employing surface plasmon resonance is proposed. The sensor is constructed by a polishing surface and three layers of air holes. The coupling is enhanced by reducing the distance between the analyte and the fiber core through the polishing surface. The gold film coated on the polishing surface is applied to measure the refractive index. The results have shown that the refractive index sensitivity reaches up to 6125 nm RIU⁻¹ in the measuring range of 1.37 ∼ 1.41 with a maximum sensitivity of 10400 nm RIU⁻¹. The temperature measurement is realized by combining gold film and mixed liquid. The gold film is covered on the surface of the air hole in one layer and the mixed liquid is filled in the adjacent air hole in the same layer. The temperature sensitivity reaches up to 1.36 nm °C⁻¹ in the measurement range of 0 ∼ 60 °C with a maximum temperature sensitivity of 2 nm °C⁻¹. The proposed sensor is a good candidate for environmental monitoring and medical sensing applications due to its relatively high sensitivity.
Compared with the terrestrial network, the air-ground integrated network composed of unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) has the advantages of large coverage, high capacity and seamless connectivity, which can provide effective communication services for Internet of remote things (IoRT) sensors. In this paper, considering two transmission modes for two types of data with different delay requirements, and the limited battery capacity of UAV, we formulate a joint resource allocation and UAV trajectory design problem in clustered non-orthogonal multiple access (C-NOMA) air-ground integrated IoRT sensors network to maximize the data collection efficiency. For the formulated non-convex problem, the deep deterministic policy gradient (DDPG) method can solve it. However, the DDPG method has the Q-value overestimation problem, in order to alleviate the problem, the twin-delayed deep deterministic policy gradient (TD3) method with a double critic network is applied, and a TD3-based resource allocation algorithm is proposed to solve the primal problem. Simulation results verify that the proposed algorithm has better performance in terms of improving the data collection efficiency than other benchmark methods.
Contemporary mobile communication systems encounter formidable challenges in urban environments and dead zones, manifesting as issues in reliability, coverage, and quality of service (QoS). Addressing these challenges, the integration of intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications emerges as a transformative solution. This paradigm not only offloads ground base stations but also ensures reliability and QoS adherence in a cost-effective manner. Additionally, the synergistic deployment of reconfigurable intelligent surfaces (RIS) with non-orthogonal multiple access (NOMA) holds significant promise for elevating the performance of mobile networks. This paper delves into the integration of multi-IRS configurations to augment UAV communication within intelligent mobile networks. The overarching objective is to minimize average total power consumption while simultaneously maximizing network-wide throughput and reliability. This is achieved through the joint optimization of resource allocation, UAV trajectory, velocity, and IRS phase shift design. The focal point is to establish reliable and efficient connectivity for mobile users and Internet of Things (IoT) nodes across diverse scenarios, thereby ensuring QoS benchmarks and unlocking new commercial opportunities. Given the non-convex and NP-hard nature of the optimization problem, we first introduce a deep deterministic policy gradient algorithm to optimize multi-IRS multi-user associations, paving the way for efficient convergence. Subsequently, a sequential convex approximation (SCA) algorithm and a Dinkelbach-based approach are employed to optimize UAV trajectory, followed by the fine-tuning of successive interference cancellation (SIC) decoding order scheduling and power allocation. Theoretical analysis and performance evaluations substantiate the profound advantages of the proposed algorithm, showcasing commendable convergence rates and enhanced energy efficiency.
The Industrial Internet of Things (IIoT) envisions enhanced surveillance and control for industrial applications through diverse IoT devices. However, the increasing heterogeneity of deployed end devices poses challenges to current practices, hampering overall performance as device numbers escalate. To tackle this issue, we introduce an innovative distributed power control algorithm leveraging the wireless channel's nature to approximate the centralized maximum-weight scheduling algorithm. Employing ubiquitous multi-protocol mobile devices as intermediaries, we propose a concurrent dual-hop/multi-hop backhauling strategy, improving interoperability and facilitating data relay, translation, and forwarding from end IoT devices. Our focus is directed towards addressing large-scale network stability and queue management challenges. We formulate a long-term time-averaged optimization problem, incorporating considerations of end-to-end rate control, routing, link scheduling, and resource allocation to guarantee essential network-wide throughput. Furthermore, we present a real-time decomposition-based approximation algorithm that ensures adaptive resource allocation, queue stability, and meeting Quality of Service (QoS) constraints with the highest energy efficiency. Comprehensive numerical results verify significant energy efficiency improvements across diverse traffic models, maintaining throughput requirements for both uniform and hotspot User Equipment (UE) distribution patterns. This work offers a comprehensive solution to enhance IIoT performance and address evolving challenges in industrial applications.
The detection of fine-grained objects in remote sensing images has been recognized as a challenging issue, which cannot be well addressed by the existing deep learning-based methods due to their inadaptability to multi-scale objects, slow convergence speed, and limitations to scarce datasets. To cope with the above issue, we propose a deep convolutional neural network (CNN)-based detection method for multi-scale fine-grained objects in complex remote sensing scenarios. Specifically, we adopt a deep CNN with a residual structure as the backbone network, to extract deep-level details from the image. Besides, we introduce a multi-scale region generation network to overcome the limitations of fixed receptive field convolution kernels and enable multi-scale object detection. Lastly, we replace fully connected layers in the fully convolutional region classification network with 1×1 convolutional layer to enhance detection efficiency and detection speed. To overcome the limitation of scarce datasets, we conducted experiments on the FAIR1M dataset, which is currently the largest fine-grained object detection dataset in the remote sensing field. Simulation results show that the proposed detection method achieves the highest average precision (35.86%) among all benchmarks and outperforms the classic Faster R-CNN-based method by 3.44%. Furthermore, our method demonstrates significantly improved detection speed compared to the Faster R-CNN-based methods.
In this paper, we consider an unmanned aerial vehicle (UAV)-enabled massive multiple-input multiple-out (MIMO) non-orthogonal multiple access (NOMA) full-duplex (FD) two-way relay (TWR) system with low-resolution analog-to-digital converters/digital-to-analog converters (ADCs/DACs), where the UAV provide services for multi-pair ground users (GUs). By employing maximum ratio combining/maximum ratio transmission (MRC/MRT), the approximate closed-form expressions for sum spectrum/energy efficiency (SE/EE) with imperfect channel state information (CSI), imperfect successive interference cancellation (SIC) and quantization noise are derived. To evaluate the effects of the parameters on system performance, the asymptotic analysis and the power scaling laws are further provided. Finally, an optimization scheme is proposed to maximize the SE of the considered system. The numerical results verify the accuracy of theoretical analysis and show that the interference and noise can be effectively eliminated by deploying large-scale antennas %, adopting larger Rician factors, and applying proper power scaling law. We also demonstrate that the proposed system can obtain better SE by adjusting the height of the UAV. Moreover, the system performance is related to the ADCs/DACs quantization bits, where the SE saturation values increase by increasing number of quantization bits, while the EE first increases and then decreases. Finally, the SE/EE trade-off at low precision ADCs/DACs can be achieved by choosing the appropriate number of quantization bits, and the trade-off region grows as Rician factor increases.
Multiple unmanned aerial vehicles (UAVs) are organized into clusters in a flying sensor network (FSNet) to achieve scalability and prolong the network lifetime. There are a variety of optimization schemes that can be adapted to determine the cluster head (CH) and to form stable and balanced clusters. Similarly, in FSNet, duplicated data may be transmitted to the CHs when multiple UAVs monitor activities in the vicinity where an event of interest occurs. The communication of duplicate data may consume more energy and bandwidth than computation for data aggregation. This paper proposes a honey-bee algorithm (HBA) to select the optimal CH set and form stable and balanced clusters. The modified HBA determines CHs based on the residual energy, UAV degree, and relative mobility. To transmit data, the UAV joins the nearest CH. The re-affiliation rate decreases with the proposed stable clustering procedure. Once the cluster is formed, ordinary UAVs transmit data to their UAVs-CH. An aggregation method based on dynamic programming is proposed to save energy consumption and bandwidth. The data aggregation procedure is applied at the cluster level to minimize communication and save bandwidth and energy. Simulation experiments validated the proposed scheme. The simulation results are compared with recent cluster-based data aggregation schemes. The results show that our proposed scheme outperforms state-of-the-art cluster-based data aggregation schemes in FSNet.
In this letter, we consider a non-orthogonal multiple access (NOMA)-based uplink cellular system assisted by an unmanned aerial vehicle (UAV) through dual connectivity. To balance efficiency and fairness, we aim to maximize the sum weighted bit rate by jointly optimizing the three-dimensional UAV placement and power allocation. Considering the non-convexity of the original problem, we propose a hybrid offline-online algorithm: In the offline training, when the UAV placement is given, the property of fractional programming is utilized to obtain a sub-optimal power allocation solution; Based on the results of power allocation, the deep deterministic policy gradient algorithm is leveraged to learn the optimal UAV trajectory policy that can be deployed in an online fashion. Finally, simulations are conducted to show the efficiency of the proposed algorithm.
In order to relieve the traffic burden of the ground base station (BS) and improve system operation efficiency, we study the energy-efficient optimization problem in a non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-assisted data collection system, where a UAV assists a ground BS for traffic offloading. Particularly, the joint optimization problem of the placement of the UAV and transmit power of the sensor nodes is formulated to maximize the energy efficiency of all sensor nodes under the quality-of-service constraints and the probabilistic ground-to-air channel model. To balance the optimized performance and online operation time, we propose an alternating-based offline optimization algorithm for obtaining the optimal online UAV trajectory policy. Under the given UAV placement, the formulated problem is first transformed into a convex power allocation subproblem. Based on the power-allocation solutions, the deep deterministic policy gradient algorithm is then leveraged to approach the optimal UAV placement. Simulations show that the proposed algorithm can obtain an efficient performance while consuming an average online operation time of fewer than 0.2 seconds.
Optimal wireless sensor placement (OWSP) plays a pivotal role in structural health monitoring. This study proposes a method to determine the simultaneous placement of sensors and sinks that minimizes energy consumption and maximizes information effectiveness. Network connectivity and reliability are critical constraints that determine the lifetime of wireless sensor networks. In this study, OWSP was formulated as a constrained multi-objective optimization problem with mixed-integer programming. Accordingly, a dual-population constrained multi-objective optimization (DCCMO) algorithm, which includes new crossover and mutation operators, was developed. In DCCMO, weak cooperation between two offspring populations is exploited to improve the efficiency of the solution search. The performance of DCCMO was compared to that of five other state-of-the-art algorithms using numerical examples with varying network parameters. DCCMO not only successfully matches the constrained Pareto front but also balances energy consumption and information effectiveness while exhibiting greater diversity and faster convergence than all other tested algorithms.
The energy efficiency (EE) of femtocells is always limited by the surrounding radio environments in heterogeneous networks (HetNets), such as walls and obstacles. In this paper, we propose to deploy reconfigurable intelligent surfaces (RISs) to improve the EE of femtocells. However, perfect channel state information is more difficult to obtain due to the passive characteristics of RISs and non-cooperative relationship between different tiers. Besides, the low-cost transceivers and reflecting units suffer nontrivial hardware impairments (HWIs) due to the hardware limitations of practical systems. To this end, we investigate a realistic robust beamforming design based on max-min fairness for an RIS-aided HetNet under channel uncertainties and residual HWIs. The joint optimization of transmit beamforming vectors of femto base stations (FBSs) and the phase-shift matrices of RISs is formulated as a non-convex problem to maximize the minimum EE of the femtocell subject to the constraints of the maximum transmit power of FBSs, the quality of service of users, and unit modulus phase-shift constraints of RISs. We develop an iterative block coordinate descent-based algorithm which exploits the semi-definite relaxation, the S-procedure, and the singular value decomposition method. Simulation results reveal that the proposed algorithm outperforms existing algorithms in terms of fairness, EE, and outage probability.
This paper investigates unmanned aerial vehicles (UAVs)-assisted full-duplex (FD) non-orthogonal multiple access (NOMA) system based cellular network, aiming to improve overall sum-rate throughput of the system through dynamic user clustering, optimal UAV placement and power allocation. Since each UAV operates in FD mode, self-interference (SI), co-channel interference (CCI), inter-UAV interference (IUI) and intra-node interference (INI) dominate the system's performance. Consequently, we propose an unconventional two-stage dynamic user clustering for user nodes (UNs) to reduce the cross-interference in multi-UAV aided FD-NOMA system. Particularly, all UNs are initially clustered into clusters using k-means clustering in the first stage where each cluster is served by an UAV. Furthermore, each cluster is further divided into sub-clusters and each sub-clusters are operated in FD-NOMA scheme. Finally, to control interferences, a sum-rate throughput maximization problem is formulated for each UAV to jointly optimize uplink and downlink power allocation and UAV placement. The joint optimization problem is non-convex and difficult to solve directly, for which we decoupled the original problem by addressing UAV placement and power allocation separately. We first fix the UAV position and then solve the problem iteratively using successive convex approximation (SCA) method. By utilizing brute-force search algorithm, an optimal UAV placement is later performed which corresponds to maximum possible sum-rate throughput. Simulation results demonstrate that the proposed solution for the considered FD-NOMA system outperforms the conventional schemes.
The multi-access edge computing (MEC)-based wireless-powered backscatter communication networks (WP-BackComNets) allow wireless devices (WDs) to offload computation resources to lightweight and widely deployed MEC servers with the assistance of backscatter devices (BDs), which have substantial application prospects for the emerging Internet-of-Things applications. However, the limited battery capacity of WDs is one of the bottlenecks restricting its further development. Reducing the energy consumption and the computation burden of WDs while ensuring the quality-of-service requirements is an urgent issue. To this end, a joint computation offloading and radio resource allocation problem is formulated to minimize the total energy consumption of WDs for an MEC-based WP-BackComNet by jointly optimizing user association, the transmit power and transmission time of WDs, the computational offloading coefficient of each task, and the reflection coefficients of BDs, where the circuit power consumption of BDs, the computational capabilities of WDs, and the task execution delay budgets are considered. To handle this non-convex problem, we propose an efficient algorithm to obtain a suboptimal solution. Simulation results demonstrate that the proposed scheme can effectively decrease the energy consumption compared with the benchmarks.
In the fifth-generation (5G) mobile communication system, various service requirements of different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous network (HetNet) has been studied in recent years. Compared with homogeneous networks, HetNets can increase the opportunity in the spatial resource reuse and improve users’ quality of service by developing small cells into the coverage of macrocells. Since there is mutual interference among different users and the limited spectrum resource in HetNets, however, efficient resource allocation (RA) algorithms are vitally important to reduce the mutual interference and achieve spectrum sharing. In this paper, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNets. Second, RA models are discussed. Then, we present a classification to analyze current RA algorithms for the existing works. Finally, some challenging issues and future research trends are discussed. Accordingly, we provide two potential structures for 6G communications to solve the RA problems of the next-generation HetNets, such as a learning-based RA structure and a control-based RA structure. The goal of this paper is to provide important information on HetNets, which could be used to guide the development of more efficient techniques in this research area.
Abstract: The ambitious high data-rate applications in the envisioned future beyond fifth-generation (B5G) wireless networks require new solutions, including the advent of more advanced architectures than the ones already used in 5G networks, and the coalition of different communications schemes and technologies to enable these applications requirements. Among the candidate communications schemes for future wireless networks are non-orthogonal multiple access (NOMA) schemes that allow serving more than one user in the same resource block by multiplexing users in other domains than frequency or time. In this way, NOMA schemes tend to offer several advantages over orthogonal multiple access (OMA) schemes such as improved user fairness and spectral efficiency, higher cell-edge throughput, massive connectivity support, and low transmission latency. With these merits, NOMA-enabled transmission schemes are being increasingly looked at as promising multiple access schemes for future wireless networks. When the power domain is used to multiplex the users, it is referred to as the power domain NOMA (PD-NOMA). In this paper, we survey the integration of PD-NOMA with the enabling communications schemes and technologies that are expected to meet the various requirements of B5G networks. In particular, this paper surveys the different rate optimization scenarios studied in the literature when PD-NOMA is combined with one or more of the candidate schemes and technologies for B5G networks including multiple-input-single-output (MISO), multiple-input-multiple-output (MIMO), massive-MIMO (mMIMO), advanced antenna architectures, higher frequency millimeter-wave (mmWave) and terahertz (THz) communications, advanced coordinated multi-point (CoMP) transmission and reception schemes, cooperative communications, cognitive radio (CR), visible light communications (VLC), unmanned aerial vehicle (UAV) assisted communications and others. The considered system models, the optimization methods utilized to maximize the achievable rates, and the main lessons learnt on the optimization and the performance of these NOMA-enabled schemes and technologies are discussed in detail along with the future research directions for these combined schemes. Moreover, the role of machine learning in optimizing these NOMA-enabled technologies is addressed.
Recently, unmanned aerial vehicles (UAVs) attracted significant popularity in both military and civilian domains for various applications and services. Moreover, UAV-aided wireless sensor networks (UAWSNs) became one of the interesting hot research topics. This is mainly because UAWSNs can significantly increase the network coverage and energy utilization compared to traditional wireless sensor networks (WSNs). However, the high mobility, dynamic path, and variable altitude of UAVs can cause not only unforeseen changes in the network topology but also connectivity and coverage problems, which can affect the routing performance of the network. Therefore, the design of a routing protocol for UAWSNs is a critical task. In this paper, the routing protocols for UAWSNs are extensively investigated and discussed. Firstly, we classify the existing routing protocols based on different network criteria. They are extensively reviewed and compared with each other in terms of advantages and limitation, routing metrics and policies, characteristics, difference performance factors, and different performance optimization factors. Furthermore, open research issues and challenges are summarized and discussed.
In this paper, we investigate a resource allocation problem for a multi-unmanned aerial vehicle (UAV) assisted integrated sensing and communication system (ISAC), where a group of dual-functional UAVs perform simultaneous radar sensing of a target and data communication with multiple ground users. In particular, the trajectory of UAVs, user association, and beamforming design are jointly considered to maximize the sum weighted bit rate of all ground users while ensuring the sensing beampattern gain of the target. To cope with the above mixed-integer non-convex optimization problem, we propose an efficient strategy by decomposing the original problem into two subproblems under the alternating optimization framework. For the user association and beamforming design, we propose a novel algorithm to circumvent the coupling relationship among ground users and UAVs by leveraging matching theory and fractional programming theory. For the non-convex UAV trajectory subproblem, we apply the sequential quadratic programming to obtain a sub-optimal solution by solving a sequence of quadratic programming problems. The above two subproblems are iteratively solved and a stable solution is obtained upon convergence. Simulation results show that the proposed strategy outperforms various benchmark schemes that are based on the deferred acceptance algorithm, K-means algorithm, and a heuristic algorithm. It is demonstrated that the proposed strategy efficiently improve the sensing beampattern gain and communication rate.
In this paper, we investigate a resource allocation problem for a multi-unmanned aerial vehicle (UAV)-assisted full-duplex wireless-powered Internet-of-things (IoT) network, where the slot partition, power allocation, user association, and three dimensional (3D) UAV placement are jointly considered to maximize the sum bit rate of all IoT devices under the imperfect self-interference cancellation and generalized probabilistic air-ground channel model. To deal with the formulated mixed-integer non-convex problem, we propose a novel resource allocation strategy with three nested parts by integrating the model-based optimization theory with the data-based learning theory. Particularly, the data-based deep deterministic policy gradient algorithm is only explicitly used to train the 3D UAV placement policy, while the model-based Lagrange dual theory and matching theory are implicitly used to explore the hidden tractability of the rest two parts and design efficient algorithms, where the optimization results are passed onto the data-based part through reward values. Simulation results show that the proposed strategy greatly cuts down the execution time of the exhausting search-based genetic algorithm by 4 orders of magnitude at the cost of less than 5.1 percent performance loss.
Ultra-Reliable Low-Latency Communication (URLLC) has imposed significant challenges on the Internet of Things (IoT) networks due to its stringent Quality of Service (QoS) requirements. Conventionally, to support the latency and reliability requirements, the IoT devices have to leverage the transmission power, which significantly shortened the battery life. As a promising candidate for the future mobile communication systems, reconfigurable intelligent surface (RIS) is preferrable to be deployed in the IoT networks to reduce the power consumption, thanks to its ability in reconfiguring the propagation environment. In this paper, we focus on the design of an aerial RIS aided URLLC system where the RIS is deployed on an unmanned aerial vehicle (UAV). The system operates on the time division multiple access (TDMA) protocol, and the transmission power minimization problem is investigated. A low-complexity algorithm is proposed to jointly optimize the RIS phase shifts, transmission time duration, and the UAV location. Simulation results demonstrate that by deploying aerial RIS in the IoT network, the transmission power at the IoT device can be significantly reduced. The effect of the TDMA frame length is also discussed. When the TDMA frame length is large, the optimal transmission time duration could be much shorter than equal allocation, which implies that our proposed design is able to simultaneously reduce both the average power consumption and the transmission latency.
In this paper, an integrated sensing and communication (ISAC) system is considered, with a dual function base station designated to serve communication users while sensing interested targets. To enhance throughput and spectrum efficiency, the sum weighted bit rate is maximized to guarantee the effective sensing power through beamforming design. To tackle the non-convexity of the original optimization problem, an alternating optimization algorithm which efficiently combinies fractional programming (FP) with successive convex approximation (SCA) is proposed. Specifically, when the non-convex constraint is approximated by SCA as the difference between an affine function and a constant, the remaining non-convex beamforming design problem is equivalently transformed and can be solved by FP. Simulations results validate the superiority of the proposed algorithm in improving the communication performance while ensuring sensing performance.
Human activity recognition (HAR) has become a hotspot. However, the existing HAR has some shortcomings, such as few recognized human activities, no identification, privacy leakage, and battery maintenance. Therefore, we design body RFID skeleton which fully senses the key features of human activity, and further propose human activity recognitions by taking advantage of RFID (privacy protection, identification, and battery-less maintenance). Firstly, we devise RFID skeleton activity graph as human activity model. Secondly, we propose baseline RFID skeleton activity graph convolution network (FSCN) by using the graph convolution network (GCN), and FSCN classifies the RFID skeleton activity graph for human activity recognition. Thirdly, to solve the tag response signal feature data over-smoothing and the different information aggregation degree in FSCN, we propose improved FSCN with the residual network (R-FSCN). Finally, we design the parallel RFID skeleton activity graph convolution network (PR-FSCN) for optimizing R-FSCN. Massive experiments show that PR-FSCN has comprehensive superiority to the existing HARs. As far as we know, this study is the first work that appropriately designs body RFID skeleton for HAR, and that successfully introduces GCN to investigate the new bound-RFID HAR with high recognition performance. The dataset, source code, and materials are available at
http://www.cwnuiot.net/FSCN/
.
Integrating simultaneous wireless information and power transfer (SWIPT) and wireless-powered communication network (WPCN) has been regarded as one of the energy-efficient paradigms, which can complement each other in uplink and downlink transmission. However, severe path loss and imprecise channel estimation hinder the system performance in energy harvesting (EH) and information transmission. To break this gridlock, in this paper, reconfigurable intelligent surface (RIS) is applied to SWIPT-WPCN systems for ameliorating transmission efficiency, and system robustness is considered under imperfect channel state information. Specifically, aiming at minimizing system energy consumption, a robust resource allocation optimization problem is formulated by jointly optimizing the beamforming vector of the base station, the phase shifts of the RIS, power-splitting factors, EH time, and the transmit power of each user, where the non-linearity of EH and channel uncertainties are taken into account. To deal with this intractable non-convex problem, the S-procedure approach is applied to transform the original problem into a deterministic one, and a block coordinate descent-based iterative algorithm with the successive convex approximation and the penalty-based method is proposed to obtain sub-optimal solutions. Simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithms regarding energy saving and lower outage probability.
The employment of the unmanned aerial vehicle (UAV) as communication platform has drawn a lot of interest recently. Although previous studies have presented the performance gain by utilizing the UAV's mobility and communication, they concentrate on the UAV trajectory for homogeneous devices with the same requirements of data size and latency constraints. The UAV trajectory for groups with heterogeneous devices can affect the UAV's service capabilities. Additionally, the technique of non-orthogonal multiple access (NOMA) has been deemed as a promising technique in future communication networks since it can improve the spectral efficiency and support more device connections. The UAV can integrate with NOMA to further enhance the situation. In this article, we consider a NOMA-based UAV system, where a UAV provides services to ground devices in different groups. We aim to maximize the total throughput under the constraints of data rate, sum transmit power, and atomicity of devices' tasks. To tackle this problem, we first derive the closed-form expression of transmit power based on Karush-Kuhn-Tucker (KKT) conditions. Then, we formulate a group-based auction algorithm to decide the service order of UAV to device groups, where a latency estimation algorithm is constructed to design the auction bidding. Additionally, an adaptive device admission algorithm is put forward to settle the device scheduling problem within one group. Numerical results show that our proposed scheme acquires a significant performance gain in terms of the total throughput, compared with other benchmark schemes.
Non-orthogonal multiple access (NOMA) is regarded as a promising solution to improve the energy efficiency and reduce the latency of the unmanned aerial vehicle (UAV)-aided networks. In this paper, we consider an energy-efficient multi-UAV incorporating hybrid NOMA data collection system. Explicitly, the optimization problem of joint trajectory design and power allocation is formulated for maximizing energy utilization of the system. The optimization problem is a mixed integer non-convex problem and involves continuous variables. To tackle this challenging problem, we utilize a multi-agent deep reinforcement learning (MADRL) approach, i.e., multi-agent Twin Delayed Deep Deterministic Policy Gradient (MATD3), which introduces clipped double Q-learning and deep networks to reduce overestimation bias. Furthermore, a reward shaping method is applied to speed up the learning efficiency and convergence. Corroborated by extensive experiments, the proposed hybrid NOMA enhanced multi-UAV outperforms pure NOMA and OMA cases.
Reconfigurable intelligent surface (RIS) is a promising technique that smartly reshapes wireless propagation environment in the future wireless networks. In this paper, we apply RIS to an unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network, in which the transmit signals from multiple UAVs to ground users are strengthened through RIS. Our objective is to minimize the power consumption of the system while meeting the constraints of minimum data rate for users and minimum inter-UAV distance. The formulated optimization problem is non-convex by jointly optimizing the position of UAVs, RIS reflection coefficients, transmit power, active beamforming vectors and decoding order, and thus is quite hard to solve optimally. To tackle this problem, we divide the resultant optimization problem into four independent subproblems, and solve them in an iterative manner. In particular, we first consider the sub-solution of UAVs placement which can be obtained via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we yield the closed-form expression for the RIS reflection coefficients. Subsequently, the transmit power is optimized using standard convex optimization methods. Finally, a dynamic-order decoding scheme is presented for optimizing the NOMA decoding order in order to guarantee fairness among users. Simulation results verify that our designed joint UAV deployment and resource allocation scheme can effectively reduce the total power consumption compared to the benchmark methods, thus verifying the advantages of combining RIS into the multi-UAV assisted NOMA networks.
Cognitive radio and backscatter communication (BackCom) have been viewed as two promising technologies for the future green Internet of Things (IoT). The combination of these two technologies can not only enhance spectrum efficiency but also improve energy efficiency. However, most of the existing resource allocation (RA) algorithms in cognitive BackCom networks consider ideal channel state information and a time division multiple access protocol, which is unrealistic in practical systems and can not support the massive number of accessing users. To this end, in this paper, we study a robust chance-constrained RA problem for non-orthogonal multiple access (NOMA)-based cognitive BackCom networks to overcome the influence of channel estimation errors and support for large-scale IoT nodes. Specifically, cognitive backscatter users (CBUs) can not only share the spectrum resource owned by primary users but also harvest surrounding radio frequency and transmit their own information over the primary signals. Moreover, CBUs can use the harvested energy to actively transmit information via a NOMA protocol. The robust RA problem with outage-probability constraints is formulated to maximize the total throughput of CBUs by jointly optimizing the transmission time, transmit power, and the reflection coefficients of CBUs. To tackle the non-convex problem, the original problem is converted into an equivalent form by applying the linear objective function, an inequality transformation approach, and an auxiliary variable method. Then, an iteration-based RA algorithm is proposed to solve it. Simulation results demonstrate the effectiveness of the proposed algorithm by comparing it with the benchmark algorithms.
Symbiotic radio (SR) and reconfigurable intelligent surface (RIS) can serve as a complementary technique for each other regarding spectrum efficiency and energy efficiency (EE) improvements. In this paper, we investigate a RIS-aided multiple-input single-output SR system with multiple backscatter devices (BDs). In particular, an EE-based maximization resource allocation problem is formulated by jointly optimizing the passive beamforming vector of the RIS, the active beamforming vector of the primary transmitter (PT), and the reflection coefficient of each BD under multiple constraints, including the minimum rate of each user, the maximum transmit power of the PT, and the minimum harvested energy of each BD. To solve the non-convex problem, a Dinkelbach-based block coordinate descent algorithm is developed to obtain the sub-optimal solution. Numerical results verify that the proposed algorithm achieves two times more than the traditional algorithms in terms of system EE.
The dense deployment of small cell networks is a key feature of next-generation mobile networks aimed at providing the necessary capacity increase. It is noteworthy that small cell networks employ high-capacity backhaul links on millimeter-wave bands to develop multi-hop topologies in order to mitigate data transmission costs. The current static backhaul infrastructures cannot control severe fluctuating network traffic. To resolve this problem, this paper proposed a novel adaptive backhaul topology with the ability to adapt to different traffic patterns. Based on the graph theory, the adaptive system dynamically allows changes to the hybrid millimeter-wave backhaul architecture, and it also provides the possibility of effective channel allocation to each backhaul link to meet capacity and QoS demands. Also, regarding the importance of green networking in integrated-access-and-backhaul networks we proposed a dynamic optimization model which minimizes the overall energy consumption of UL/DL Decoupled NOMA heterogeneous networks in addition to providing the essential coverage and capacity. The proposed model optimizes user association/power utilization and presents an effective modular and scalable framework for analytical technology-oriented modeling of integrated multi-hop backhauls. The numerical results proved that the joint power optimization and hybrid backhaul architecture can increase the total network throughput by 18 percent compared to the current optimized static architectures. It can also reduce the energy consumption level by 30 percent, and enhance users’ quality satisfaction by 24.5 percent with respect to user distribution patterns.
In this work, we study the joint optimization of multiple unmanned aerial vehicles (UAVs)’ trajectories, power allocation, user-UAV association, and user pairing for UAV-assisted wireless networks employing the nonorthogonal multiple access (NOMA) for uplink communications. The design aims to minimize the total energy consumption of ground users while guaranteeing to successfully transmit their required amount of data to the UAV-mounted base stations. The underlying problem is a mixed-integer nonlinear program (MINLP), which is difficult to solve optimally. To tackle this problem, we derive the optimal power allocation as a function of other variables, which is used to transform the optimization problem into an equivalent form. We then propose an iterative algorithm to solve the resulting optimization problem by using the block coordinate descent (BCD) method where three subproblems are solved in each iteration and this process is repeated until convergence. Specifically, given the UAVs’ trajectories and data rates, we solve the NOMA user pairing, and user-UAV association subproblem optimally by exploiting its special structure. Then, we describe how to optimize the users’ data rates and tackle the UAV trajectory optimization in the second and third subproblems, respectively, by using the successive convex approximation (SCA) method. Numerical results show that our proposed algorithm can provide efficient active-inactive schedules (by setting user’s transmit powers to zero), and lower energy consumption compared to an existing baseline, and an OMA-based resource allocation and UAV-trajectory optimization strategy.
Combining non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAVs) could achieve better performance for wireless networks. However, effective resource allocation for quality of service (QoS) provision among all users still remains as a great challenge for multi-cluster NOMA-UAV networks. In this paper, we propose a NOMA-UAV scheme, where a UAV is deployed as the mobile base station to serve ground users. To meet the QoS requirements of all users with limited resource, the user clustering and optimal routing are first developed by the K-means algorithm and genetic algorithm, respectively. Then, the sum throughput is maximized by jointly optimizing the transmission power, hovering locations and transmission duration of UAV. To solve this non-convex problem with coupled variables, we decompose it into three subproblems. Among them, the power and location optimizations are also non-convex, which can be transformed into convex ones by successive convex approximation. The duration optimization is a linear programming which can be solved directly. Then, we propose an iterative algorithm to solve these three subproblems alternately. Finally, simulation results are presented to show the effectiveness of the proposed scheme.
For emergency communications in an internet of thing (IoT) network, a large number of gateways are distributed to gather the data traffic. Considering the practical difficulty of deploying multiple territorial base stations (TBSs) in a wide range, unmanned aerial vehicle base station (UAV-BS) can fly to a specific point and hover above there to collect data traffic from gateways. In this paper, we aim to maximize the UAV-BS energy efficiency under the constraints of total serving delay, UAV-BS flying speed, and the maximum available transmitting power of gateways,
etc
. Firstly, we propose a distributed gateway cluster (GC) algorithm to group gateways into multiple GCs based on the distances among gateways. Next, the UAV-BS flies and hovers above each GC, where the gateways in the GC simultaneously transmit data to the UAV-BS by non-orthogonal multiple access (NOMA). By analyzing the NOMA feature, we propose theorems optimizing the UAV-BS hovering height to minimize the transmitting power of the gateway with the maximum transmitting power among the gateways in a GC. Based on the proposed theorems, we formulate the joint optimization problem to maximize the UAV-BS energy efficiency with only the variables of UAV-BS flying speed and the serving time for each GC. The optimization problem is effectively solved by the geometric programming (GP) method. Finally, we verify the effectiveness of the proposed algorithms by extensive simulation results.
In this paper, we investigate the joint user pairing and power coefficient allocation for unmanned aerial vehicle (UAV) systems which employ non-orthogonal multiple access (NOMA) to communicate with multiple ground users. Aiming to maximize achievable sum rate and ensure the users' Quality-of-Service (QoS) requirements, we formulate an optimization problem which relies on reinforcement learning (RL) from Multi-Armed Bandit (MAB) framework to propose a solution based on Upper Confidence Bound (UCB) approach. The proposed solution can successfully identify the best action and selects it more often, which leads to maximum system throughput. The attained results show that the proposed scheme finds the best-performing action fast, while the others methods spend a lot of time exploring non-ideal user pairs. As a result, the proposed method accumulates less regret and achieves satisfactory results in terms of system throughput when compared to other user pairing strategies and power allocation (PA) policies.
Mobile Edge Computing (MEC) is a viable solution in response to the growing demand for broadband services in the new-generation heterogeneous systems. The dense deployment of small cell networks is a key feature of next-generation radio access networks aimed at providing the necessary capacity increase. Nonetheless, the problem of green networking and service computing will be of great importance in the downlink, because the uncontrolled installation of too many small cells may increase operational costs and emit more carbon dioxide. In addition, given the resource and computational limitation of the user layer, energy efficiency (EE) and fairness assurance are critical issues in MEC-based cellular systems. Considering the user fairness criteria, this paper proposes a dynamic optimization model which maximizes the total UL/DL EE along with satisfying the necessary QoS constraints. Based on the non-convex characteristics of the EE maximization problem, the mathematical model can be divided into two separate subproblems, i.e., computational carrier scheduling and resource allocation. So that, a subgradient method is applied for the computational resource allocation and also successive convex approximation (SCA) and dual decomposition methods are adopted to solve the max-min fairness problem. The simulation results exhibit considerable EE improvement for various traffic models in addition to guaranteeing the fairness requirements. It also proved that the proposed computational partitioning scheme managed to significantly improve the total throughput for mobile computing services.
In emergency networks, it is significant to jointly consider users’ priorities and communication delays for post-disaster users. To this end, we propose an essential metric by integrating these two factors, namely
prioritized delay
. Moreover, we formulate a min-max prioritized delay problem for non-orthogonal multiple access (NOMA)-based multi-unmanned aerial vehicle (UAV) emergency networks, where the powers of all users are concurrently optimized to balance prioritized delay among users while suppressing multi-user interference introduced by NOMA and multiple UAVs. The problem is found to be non-convex, and we propose the
substitution-decomposition-scaling-block successive upper bound minimization
(SDS-BSUM) algorithm to address this issue. Specifically, the originally-formulated problem is first transformed into a standard form through variable
substitution
, followed by problem
decomposition
into single-UAV subproblems through exploiting its separable structure, and the
scaling
of non-convex constraints into tight convex ones. Finally, the converted problem is fitted into
BSUM
for solving. Simulation results reveal that compared with the existing schemes, the proposed SDS-BSUM not only reduces the prioritized delay by up to 70
, but also achieves an excellent delay fairness commensurate with users’ priorities.
In this article, we address the multi-unmanned aerial vehicle (UAV) deployment problem in the presence of co-channel interference (CCI) for non-orthogonal multiple access (NOMA) enabled wireless networks. Considering UAV as aerial base station for downlink communication, we aim to maximize the energy efficiency and coverage rate by jointly optimizing the power allocation of users and the locations of UAVs. Initially, the ground users are divided into clusters by K-means clustering, where each UAV serves a cluster. Then, the clusters are further divided into multiple sub-clusters, each having a pair of near and far users. Orthogonal multiple access (OMA) is applied among sub-clusters, and NOMA is applied to intra sub-cluster users. Lastly, to tackle the formulated multi-objective non-convex problem, we propose an improved multi-objective grey wolf optimizer (IMOGWO) to maintain an optimal trade-off between energy efficiency and coverage rate. Simulation results demonstrate that the proposed algorithm outperforms the benchmarks.
The dense deployment of small-cell networks is a
key feature of the next-generation mobile networks employed to
provide the necessary capacity increase.The small cells are installed
in the areas covered by macro base stations (eNBs) to supply the
required local capacity based on the known concept of the hierarchical
HetNets. Moreover, small-cell networks use high-capacity
backhaul links on millimeter-wave bands to develop multihop
topologies to mitigate the costs of data transmission. Nonetheless,
green networking gains great importance for the uncontrolled
installation of too many small cells may escalate operational costs
and emit more carbon dioxide. This article proposes a dynamic
optimization model to minimize the overall energy consumption
of fifth-generation (5G) heterogeneous networks and provide the
essential coverage and capacity. Optimizing carrier allocation and
power utilization, the proposed model determines when to turn
ON or OFF small cells to meet the quality of service constraints of
users with the highest level of energy efficiency. We also proposed
a multihop backhauling strategy to effectively use the existing
infrastructure of small-cell networks for simultaneous dual-hop
transmissions. The numerical results indicated considerable rates
of power saving in different traffic models while guaranteeing the
throughput requirements for uniform and hotspot user equipment
distribution patterns. Also, according to the simulation results, energy
efficiency and system data rates can significantly be improved.
Owing to the advantages of better air-ground channel and higher flexibility, unmanned aerial vehicle (UAV) has been widely applied in the field of Internet of Things (IoT) to increase the communication coverage. However, the UAV with limited energy is facing severe energy shortage when serving more and more IoT devices. In this paper, we propose a non-orthogonal multiple access (NOMA) based green multi-UAV assisted IoT system to increase user capacity while improving the energy utilization of each UAV. Considering the limited energy budget of each UAV, we formulate a fair energy-efficient resource optimization problem under the constraints of maximum transmit power of each UAV, minimum communication rate requirement of each user and UAV mobility. By alternately optimizing the communication scheduling, transmit power allocation and UAV trajectory with the Dinkelbach method and the successive convex approximation (SCA), the energy-efficient fairness can be achieved between the UAVs by maximizing the minimum energy efficiency of the UAVs. The simulation results indicate the fair energy efficiency between the UAVs can be obtained through the alternative resource optimization, and the proposed multi-NOMA-UAV assisted IoT can get higher energy efficiency than the traditional orthogonal multiple access (OMA)-UAV assisted IoT.
Replacing base stations with unmanned aerial vehicles (UAVs) to serve the communication of ground users has attracted a lot of attention recently. In this paper, we study the joint resource allocation and UAV trajectory optimization for maximizing the total energy efficiency in UAV-based non-orthogonal multiple access (NOMA) downlink wireless networks with the quality of service (QoS) requirements. To handle the user scheduling problem, a heuristic algorithm based on matching and swapping theory is proposed first to allocate users that access UAV in each subperiod, then the transmit power allocation problem which considers the maximum transmit power and minimum user date rate is transformed to a convex optimization problem using logarithmic approximation. Meanwhile, the successive convex optimization is used in UAV trajectory optimization problem and a joint optimization algorithm is presented with the algorithm's convergence and computational complexity. Finally, numerical results are provided to support the rationality of the proposed algorithm.
An effective sensor network with an appropriate sensor configuration is the first step of model updating to obtain the actual structural response. However, sensor placements based on inherent structural characteristics (such as mode shapes) alone or their optimizations only with deterministic data are unlikely to provide very good results. Therefore, using a non-probabilistic theory to characterize the uncertainty in the uncertainty propagation process for model updating, this study proposes a time-dependent, reliability-based method for the optimal load-dependent sensor placement considering multi-source uncertainties. Due to the limitations of the uncertain parameters obtained using probabilistic or statistical methods, the uncertainties tackled in this study that includes those from structural properties and measurement processes are regarded as interval variables. Using the first-passage theory in the overall time history, different crossing situations of the reduced time history responses (that is, the modal coordinates) with respect to the full ones are constructed. The difference between the modal coordinates of the reduced and the full models is defined as the objective of the optimization, which indicates the matching level. Based on the time-dependent reliability-based index and the errors of deterministic modal coordinates between the reduced and full models, the multi-objective optimization is solved using NSGA-II. A detailed flowchart of the proposed method is given, and its effectiveness is verified by two simulated engineering examples for model updating.
A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. The optimization problem of joint three-dimensional (3D) trajectory design and power allocation is formulated for maximizing the throughput. Since ground mobile users are considered as roaming continuously, the UAVs need to be re-deployed timely based on the movement of users. In an effort to solve this pertinent dynamic problem, a K-means based clustering algorithm is first adopted for periodically partitioning users. Afterward, a mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs. In contrast to the conventional deep Q-network (DQN) algorithm, the MDQN algorithm enables the experience of multi-agent to be input into a shared neural network to shorten the training time with the assistance of state abstraction. Numerical results demonstrate that: 1) the proposed MDQN algorithm is capable of converging under minor constraints and has a faster convergence rate than the conventional DQN algorithm in the multi-agent case; 2) The achievable sum rate of the NOMA enhanced UAV network is 23% superior to the case of orthogonal multiple access (OMA); 3) By designing the optimal 3D trajectory of UAVs with the MDON algorithm, the sum rate of the network enjoys 142% and 56% gains than invoking the circular trajectory and the 2D trajectory, respectively.
Unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) are considered as an efficient tool to enhance the capacity of future mobile networks and to facilitate the communication in emergency cases. These benefits are, however, conditioned by an efficient control of the FlyBSs and management of radio resources. In this paper, we propose a novel solution jointly selecting the optimal clusters of an arbitrary number of the users served at the same time-frequency resources by means of non-orthogonal multiple access (NOMA), allocating the optimal transmission power to each user, and determining the position of the FlyBS. This joint problem is constrained with the FlyBS’s propulsion power consumed for flying and with a continuous guarantee of a minimum required capacity to each mobile user. The goal is to enhance the duration of a communication coverage in NOMA defined as the time interval within which the FlyBS always provides the minimum required capacity to all users. The proposed solution clusters the users and allocates the transmission power of the FlyBS to the users efficiently so that the communication coverage provided by the FlyBSs is extended by 67%–270% comparing to existing solutions while the propulsion power is not increased.
This paper investigates the energy efficiency (EE) optimization in a wireless communication network where multiple UAVs serve different types of devices, namely, information receivers (IRs) and energy receivers (ERs). The UAVs transmit power signals towards the ERs, and then enable data transmission to IRs on the downlink and from ERs on the uplink with non-orthogonal multiple access (NOMA). The optimization problem to maximize the overall EE is formulated and solved using Lagrangian optimization and gradient-descent methods. The optimization is decomposed into two sub-problems. Firstly, by connecting the path loss of the devices’ channels with their rate demands, the UAVs’ optimal positions are obtained. Then, based on the obtained UAVs’ optimal positions and a closed-form expression for the EE, a resource allocation aiming to maximize EE is developed. For the simulations, two main scenarios for single and multiple UAVs are considered. Numerical results and comparisons are provided. In particular, for the single-UAV scenario, the results show an enhancement in EE for the operation with NOMA compared with OMA. For the multiple-UAV scenario, several cases depending on different combinations of the devices’ rate requirements are considered. The results show the superiority of NOMA over OMA in all use cases. The results also reveal the effect of considering the devices’ rate requirements on the EE, where the case with equal rate requirements has the best performance.
This paper considers the problem of an unmanned aerial vehicle (UAV)-enabled cloud network under partial computation offloading scenario, where multiple UAV-mounted aerial base stations are employed to serve a group of remote Internet of Things ground-based smart devices (ISDs). The main objective of this work is to maximize energy efficiency by minimizing the number of needed drones while minimizing the cost associated with serving the ISDs under some realistic quality of service constraints. To that end, we aim to jointly optimize the three-dimensional (3D) UAV placements, transmit power, and cloud resources. This represents a challenging, non-convex and NP-hard optimization problem. In this work, we decompose the optimization problem into three separate subproblems namely, two-dimensional (2D) UAV positioning, UAV altitude optimization, and UAV-cloud resource association. These subproblems are solved using a modified global K-means, successive convex approximation, and successive linear programming techniques. A comprehensive simulation study and comparative evaluation against the state-of-the-art (SOTA) algorithms are conducted to demonstrate the utility of the proposed approach and its benefits in applications of interest.
Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.
The multi-access edge computing (MEC)-based wireless-powered backscatter communication networks (WP-BackComNets) allow wireless devices (WDs) to offload computation resources to lightweight and widely deployed MEC servers with the assistance of backscatter devices (BDs), which have substantial application prospects for the emerging Internet-of-Things applications. However, the limited battery capacity of WDs is one of the bottlenecks restricting its further development. Reducing the energy consumption and the computation burden of WDs while ensuring the quality-of-service requirements is an urgent issue. To this end, a joint computation offloading and radio resource allocation problem is formulated to minimize the total energy consumption of WDs for an MEC-based WP-BackComNet by jointly optimizing user association, the transmit power and transmission time of WDs, the computational offload-ing coefficient of each task, and the reflection coefficients of BDs, where the circuit power consumption of BDs, the computational capabilities of WDs, and the task execution delay budgets are considered. To handle this non-convex problem, we propose an efficient algorithm to obtain a suboptimal solution. Simulation results demonstrate that the proposed scheme can effectively decrease the energy consumption compared with the benchmarks.
In this article, we study the application of unmanned aerial vehicle (UAV) for data collection with wireless charging, which is crucial for providing seamless coverage and improving system performance in the next-generation wireless networks. To this end, we propose a reinforcement learning-based approach to plan the route of UAV to collect sensor data from sensor devices scattered in the physical environment. Specifically, the physical environment is divided into multiple grids, where one spot for UAV hovering as well as the wireless charging of UAV is located at the center of each grid. Each grid has a spot for the UAV to hover, and moreover, there is a wireless charger at the center of each grid, which can provide wireless charging to UAV when it is hovering in the grid. When the UAV lacks energy, it can be charged by the wireless charger at the spot. By taking into account the collected data amount as well as the energy consumption, we formulate the problem of data collection with UAV as a Markov decision problem, and exploit
Q
-learning to find the optimal policy. In particular, we design the reward function considering the energy efficiency of UAV flight and data collection, based on which
Q
-table is updated for guiding the route of UAV. Through extensive simulation results, we verify that our proposed reward function can achieve a better performance in terms of the average throughput, delay of data collection, as well as the energy efficiency of UAV, in comparison with the conventional capacity-based reward function.
Non-orthogonal multiple access (NOMA) significantly improves the connectivity opportunities and enhances the spectrum efficiency (SE) in the Fifth Generation and beyond (B5G) wireless communications. Meanwhile, emerging B5G services demand of higher SE in the NOMA based wireless communications. However, traditional ground-to-ground (G2G) communications are hard to satisfy these demands, especially for the cellular uplinks. To solve these challenges, this paper proposes a multiple unmanned aerial vehicles (UAVs) aided uplink NOMA method. In detail, multiple hovering UAVs relay data for a half of ground users (GUs) and share the spectrums with the other GUs that communicate with the base station (BS) directly. Furthermore, this paper proposes a K-means clustering based UAV deployment scheme and location-based user pairing scheme to optimize the transceiver association for the multiple UAVs aided NOMA uplinks. Finally, a sum power minimization based resource allocation problem is formulated with the lowest quality of service (QoS) constraints. We solve it with the message-passing algorithm and evaluate the superior performances of the proposed scheduling and paring schemes on SE and energy efficiency (EE). Extensive simulations are conducted to compare the performances of the proposed schemes with those of the single UAV aided NOMA uplinks, G2G based NOMA uplinks, and the proposed multiple UAVs aided uplinks with a facility location framework based UAV deployment. Simulation results demonstrate that the proposed multiple UAVs deployment and user pairing-based NOMA scheme significantly improves the EE and the SE of the cellular uplinks at the cost of only a little relaying power consumption of UAVs.
This article proposes an optimization framework for power and time resource allocation during time sharing non-orthogonal multiple access (TS-NOMA) transmissions performed by an unmanned aerial vehicle (UAV) in the context of a large-scale scenario. The objective of the proposed UAV-TS-NOMA system and optimization framework is to jointly maximize the energy efficiency (EE) and the downlink throughput fairness among users within the UAV communication range. The idea behind is to propose a communication system that:
i)
merges the advantages of UAV communications with the ones offered by the TS-NOMA paradigm and
ii)
maximizes the EE and the downlink fairness among users. The resulting model finds applicability in performing energy efficient and throughput fair transmissions into power-constrained communication scenarios. Performance investigations regarding the proposed framework in finding the optimal set of resources which maximizes jointly the above mentioned network metrics, have shown the advantage of the proposed two-step optimization framework in finding the optimal configuration of both power and time resources, respecting both the power constraints at the transmitter and the quality-of-service requirement of the users. In addition, it is shown how under particular conditions the proposed framework jointly optimizes the aforementioned network metrics in only one step.
Unmanned aerial vehicles (UAVs) facilitate information collection greatly in Internet of Things (IoT) systems due to their superior flexibility and mobility. On the other hand, non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G networks. The integration of NOMA into UAV-assisted wireless networks shows great potential, but how to determine the user grouping and power allocation in NOMA according to the high mobility of UAV is challenging. In this paper, we propose a general NOMA-enabled UAV-assisted data collection (NUDC) protocol to maximize the sum rate of a wireless sensor network (WSN) where the location of UAV, sensor grouping, and power control are jointly considered. Moreover, a joint signal-to-interference-ratio (SIR) hypergraph-based grouping and power control (SHG-PC) NOMA scheme is provided to obtain the appropriate sensor grouping and the optimal power control solutions efficiently, in which the hypergraph and the greedy coloring algorithm are exploited to find out the optimized group relationships. Extensive simulation results demonstrate the efficiency of our proposed protocol.
The sixth generation (6G) communication requires supporting massive Internet of Things (IoT) devices and extremely differentiated IoT applications for the air-space-ground integrated network. Relying on the aerial superiority, unmanned aerial vehicle (UAV) is capable of acting as aerial base station (BS) and supporting IoT deployment in remote and disaster areas. A UAV-supported clustered non-orthogonal multiple access (C-NOMA) system is put forward in the paper. Specifically, the UAV provides services to IoT terminals as an aerial BS based on wireless powered communication (WPC) technique. According to this system, we propose a synergetic scheme for UAV trajectory planning and subslot allocation. Our goal is to maximize the uplink average achievable sum rate of IoT terminals by synergistically planning UAV trajectory and subslot duration, while guaranteeing the uplink achievable sum rate and the UAV mobility constraints. As the formulated problem suffers non-convexity and complication, an efficient iterative algorithm is proposed to address it. Firstly, for fixed UAV trajectory, all the terminals are clustered and a subslot allocation algorithm based on Lagrange Multiplier and bisection method is proposed. Then, for fixed clustering state and subslot duration, we optimize the UAV trajectory. Finally, we solve these two subproblems alternatively until the objective function converges. The effectiveness of the proposed scheme in the UAV-supported C-NOMA system is verified by the numerical results.