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An electromagnetic map (EM) can provide movable base stations with information to optimize the communication quality of temporary communication systems in disaster areas, such as areas suffering from earthquakes. In this paper, a low-cost and effective communication optimization system model that uses sensing nodes and EMs is proposed. Moreover, two EM reconstruction methods, which serve as the keys to the model, are proposed to reconstruct EMs to provide information for communication optimization according to different demands. One method is KGA-GAN-SBI, which combines the kriging algorithm, generative adversarial network and split bregman iterations, and the other is APCA-KGA-DPA, which combines the affinity propagation clustering algorithm, kriging algorithm and domain mapping algorithm. The former can satisfy the demand of high-accuracy EMs. The latter can reconstruct an EM faster than KGA-GAN-SBI. The simulation results show that both low-cost methods can reconstruct more accurate EMs than several existing algorithms; they can provide an effective way to optimize the communication quality of temporary communication and fill the gap in the study of EM applications. [ABSTRACT FROM AUTHOR]

Aerial base stations (ABSs) are expected to be important supplementary components for the 5G-and-beyond communication systems to achieve global Internet of Everything. To fully exploit the advantages of the ABSs seamless connection, in this paper, we propose a high altitude platform (HAP) and low altitude platforms (LAPs) cooperated network architecture. Specifically, the HAP can extend the network coverage and LAPs can act as relays to improve the transmission performance for hot spots. In the considered network, we investigate the sum rate maximization problem by optimizing the downlink and the uplink transmissions, respectively. In the downlink, we adopt the orthogonal frequency division multiple access technique and take the basic rate requirement into account. In addition, we optimize the height of LAPs and the spectrum allocation between the HAP and LAPs, and between the LAP and users. Then, we decouple the downlink sum rate maximation problem as three subproblems. An alternating optimization framework is designed to deal with these non-convex optimization problems, where the height and spectrum allocation are tackled in turn. In the uplink, we adopt the non-orthogonal multiple access technique, and consider the decoding threshold of the successive interference cancellation technique. Afterward, we optimize the power allocation of each user, and the spectrum allocation between users and the LAP, and between LAPs and the HAP. Finally, simulation results show the effects of spectrum allocation, height optimization and power allocation on network performance, which verify that the proposed scheme can achieve higher sum rate on both downlink and uplink in comparison with the current works. [ABSTRACT FROM AUTHOR]

Providing femto access points (FAPs) with computational capabilities will allow (either total or partial) offloading of highly demanding applications from smartphones to the so-called femto-cloud. Such offloading promises to be beneficial in terms of battery savings at the mobile terminal (MT) and/or in latency reduction in the execution of applications. However, for this promise to become a reality, the energy and/or the time required for the communication process must be compensated by the energy and/or the time savings that result from the remote computation at the FAPs. For this problem, we provide in this paper a framework for the joint optimization of the radio and computational resource usage exploiting the tradeoff between energy consumption and latency. Multiple antennas are assumed to be available at the MT and the serving FAP. As a result of the optimization, the optimal communication strategy (e.g., transmission power, rate, and precoder) is obtained, as well as the optimal distribution of the computational load between the handset and the serving FAP. This paper also establishes the conditions under which total or no offloading is optimal, determines which is the minimum affordable latency in the execution of the application, and analyzes, as a particular case, the minimization of the total consumed energy without latency constraints. [ABSTRACT FROM PUBLISHER]

This paper considers optimal joint time, frequency, and power allocation for energy-efficient device-to-device (D2D) communications in an overlay cellular network, where multiple D2D transmitters communicate with their corresponding D2D receivers while utilizing the energy harvested from a cellular base station (BS). Due to channel estimation error and feedback delays, the channel state information (CSI) available at the transmitters is usually imperfect, which can significantly deteriorate the energy efficiency (EE) of the considered system. To mitigate the impact of imperfect CSI, we formulate a non-convex robust optimization problem for the maximization of the EE (in bits/Joule) of the system while ensuring quality of service (QoS) requirements on the minimum data rata and the minimum amount of harvested energy for cellular and D2D users, respectively. We reveal that the optimal energy-efficient D2D transmission should exhaust all the harvested energy of each D2D transmitter. Exploiting this fact, we propose an efficient iterative algorithm for obtaining the optimal solution. To strike a balance between performance and computational complexity, we further propose a low-complexity suboptimal algorithm. Simulation results show that compared with several baseline schemes, both the proposed optimal and suboptimal algorithms can significantly improve the EE of wireless-powered D2D communications under imperfect CSI. Moreover, the proposed suboptimal algorithm is close-to-optimal when the number of cellular users are small or the channel estimation error is large. [ABSTRACT FROM AUTHOR]

Proximate cloud computing enables computationally intensive applications on mobile devices, providing a rich user experience. However, remote resource bottlenecks limit the scalability of offloading, requiring optimization of the offloading decision and resource utilization. To this end, in this paper, we leverage the variability in capabilities of mobile devices and user preferences. Our system utility metric is a measure of quality of experience (QoE) based on task completion time and energy consumption of a mobile device. We propose a heuristic offloading decision algorithm (HODA), which is semidistributed and jointly optimizes the offloading decision, and communication and computation resources to maximize system utility. Our main contribution is to reduce the problem to a submodular maximization problem and prove its NP-hardness by decomposing it into two subproblems: 1) optimization of communication and computation resources solved by quasiconvex and convex optimization and 2) offloading decision solved by submodular set function optimization. HODA reduces the complexity of finding the local optimum to O(K^3), where $K$ is the number of mobile users. Simulation results show that HODA performs within 5% of the optimal on average. Compared with other solutions, HODA's performance is significantly superior as the number of users increases. [ABSTRACT FROM PUBLISHER]

To support ultra-reliable and low-latency communication (URLLC) in vehicular networks, the virtual cells, where multiple access points (APs) cooperatively serve one mobile node, have been proposed to reduce the end-to-end latency in the downlink. The latency can be further reduced by eliminating the need for retransmission and feedback control, i.e., open-loop communications. However, it is difficult to achieve a high reliability of the uplink via virtual cells, because of multiple access interference and collisions from other virtual cells nearby. In this paper, we formulate the proactive radio resource allocation in the open-loop uplink of vehicular networks as a stochastic optimization problem with the objective to maximize the uplink reliability while ensuring the network stability. The optimal resource allocation policies are obtained solving the optimization problem using the Lyapunov optimization technique in a distributed manner. To reduce the computational and to improve the challenges of the Lyapunov optimization, we propose a virtual resource slicing algorithm that maps radio resource units to virtual resource blocks. Simulation results exhibit that both ultra-low latency and ultra high reliability are guaranteed in the open-loop uplink of vehicular networks. Based on the theoretical performance analysis and simulations, the uplink radio access procedure is summarized for the URLLC in vehicular networks. [ABSTRACT FROM AUTHOR]

In connected vehicular networks, it is vital to have vehicular nodes that are capable of sensing surrounding environments and exchanging messages with each other for automating and coordinating purposes. Towards this end, integrated sensing and communication (ISAC), combining both sensing and communication systems to jointly utilize their resources and pursue mutual benefits, emerges as a new cost-effective solution. In ISAC, the hardware and spectrum co-sharing leads to a fundamental tradeoff between sensing and communication performance, which is not well understood except for very simple cases with the same sensing and channel states, and perfect channel state information at the receiver (CSIR). In this paper, a general point-to-point ISAC model is proposed to account for the scenarios where the sensing state is different from but correlated with the channel state, and the CSIR is not necessarily perfect. For the model considered, the optimal tradeoff is characterized by a capacity-distortion function that quantifies the best communication rate for a given sensing distortion constraint requirement. An iterative algorithm is proposed to compute such tradeoff, and a few non-trivial examples are constructed to demonstrate the benefits of ISAC as compared to the separation-based approach. [ABSTRACT FROM AUTHOR]

Combining powerful error-correction codes (ECCs) with multipulse pulse-position modulation (MPPM) has great potential to realize high-throughput and high-reliability transmissions in free-space optical (FSO) communication systems. This paper conducts an insightful investigation on protograph low-density parity-check (PLDPC)-coded MPPM systems over Poisson channels. To be specific, we first put forward a novel type of constellations, referred to as irregular pulse-distribution (IPD) constellations, for MPPM. We also conceive a bit-level rate-adaptive (RA) scheme, which can exploit the structural property of the IPD constellations to significantly improve the convergence performance at the price of transmission-rate loss. Furthermore, with the aid of a modified protograph extrinsic information transfer (PEXIT) algorithm, we construct three enhanced PLDPC codes with code rates $1/2, 2/3$ and $3/4$ that exhibit excellent decoding thresholds over Poisson channels. The proposed PLDPC-coded MPPM systems with the use of the IPD constellations, RA scheme, and enhanced PLDPC codes are superior to the state-of-the-art counterparts. [ABSTRACT FROM AUTHOR]

This paper studies a secure multiuser multiple-input single-output (MISO) communication system aided by an intelligent reflecting surface (IRS), where multiple colluding eavesdroppers (EVEs) coexist. We aim to maximize the sum secrecy rate (SSR) via jointly optimizing the beamforming vectors, the artificial noise (AN) and the phase shifts at the IRS subject to the maximum transmit power constraint and unit modulus constraints. To address the non-convex optimization problem, we first propose an alternating optimization (AO) algorithm based on semidefinite relaxation (SDR) and obtain a high-quality sub-optimal solution. In order to reduce the high computational complexity, a low-complexity alternating optimization (LC-AO) algorithm is developed, in which the beamforming vectors, AN and the IRS phase shifts are optimized alternately by the generalized power iteration (GPI) and the Riemannian manifold conjugate gradient (RMCG) algorithm, respectively. Simulation results show the advantages of deploying the IRS in improving the system secrecy performance. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS)-enhanced wireless communication is one of the critical technologies of the sixth generation mobile communication, which has recently emerged as an innovative solution to the realization of intelligent transportation. It can meet the increasing demand for high-speed railway (HSR) communications services. In this paper, we consider a train-ground TDD wireless mobile communication paradigm to deploy two IRSs on the roadside and the train roof to assist the wireless transmission for the HSR communication system. To achieve the more significant performance gain, in the uplink transmission phase, we propose the practical channel estimation framework of the MIMO system aided by double IRSs to acquire reliable channel state information (CSI) of each channel. To facilitate the processing of the three-dimension tensor, we leverage the trilinearity of the parallel factor decomposition to decompose the received signal into a two-dimensional matrix. The Doppler frequency offset compensating scheme and channel tracking scheme based on Kalman-Filter are proposed to further decrease the pilot overheads. Then, in the downlink phase, a block coordinate descent (BCD) algorithm is proposed to handle the data rate maximization problem of double IRSs communication system with the estimated CSI. We reformulate the original problem and decouple the double IRSs phase optimization problem into two single IRS phase optimization non-convex problems under the unit-modulus constraint. The Majorization-Minimization (MM) algorithm is proposed to approximate the optimal solution of the two optimization subproblems. Simulation results corroborate the effectiveness of the proposed channel estimation and phase shifts optimization scheme. [ABSTRACT FROM AUTHOR]

In this paper, the ergodic sum rate of a fading cognitive multiple-access channel (C-MAC) is studied, where a secondary network (SN) with multiple secondary users (SUs) transmitting to a secondary base station shares the spectrum band with a primary user (PU). An interference power constraint (IPC) is imposed on the SN to protect the PU. Under such a constraint and the individual transmit power constraint (TPC) imposed on each SU, we investigate the power allocation strategies to maximize the ergodic sum rate of a fading C-MAC without successive interference cancelation (SIC). In particular, this paper considers two types of constraints: 1) average TPC and average IPC and 2) peak TPC and peak IPC. For the first case, it is proved that the optimal power allocation is dynamic time-division multiple access (D-TDMA), which is exactly the same as the optimal power allocation to maximize the ergodic sum rate of the fading C-MAC with SIC under the same constraints. For the second case, it is proved that the optimal solution must be at the extreme points of the feasible region. It is shown that D-TDMA is optimal with high probability when the number of SUs is large. Moreover, we show that when the SUs can be sorted in a certain order, an algorithm with linear complexity can be used to find the optimal power allocation. [ABSTRACT FROM PUBLISHER]

The full-duplex (FD) single-user multiple-input–multiple-output (SU-MIMO) sum-rate maximization problem leads to a nonconvex optimization problem. In this paper, we introduce a maximum self-interference threshold (SIT) constraint to the sum-rate maximization problem to transform the problem into a convex optimization problem and develop the SIT-based FD precoding (FDP-SIT) algorithm. In particular, the FDP-SIT algorithm consists of an inner loop that assumes fixed threshold values and an outer loop that optimizes each node's threshold values. Illustrative examples show how the proposed FDP-SIT algorithm can provide a tradeoff between the separate and joint FDP algorithms. As well, the measured data-based results suggest that the FDP-SIT algorithm can offer increased sum rates when low cancelation power is used. [ABSTRACT FROM PUBLISHER]

From the fourth generation (4G) of cellular systems onward, wireless personal devices (WPDs) support multi-input multi-output (MIMO) communication. However, the impact of MIMO communication on the electromagnetic field (EMF) of WPD users has yet to be fully understood and analyzed at the system level. In this paper, we first provide a generic model for assessing the individual exposure dose of multi-antenna WPD users in a multi-user multi-carrier communication system. An optimization framework for minimizing this exposure dose is then developed based on our exposure model. This framework helps us to identify a new criterion, i.e., the ratio between the normalized exposure dose and the channel to noise ratio (CNR), as the main system level criterion for minimizing the individual exposure dose of multi-antenna WPD users. This criterion is further integrated in the design of two novel centralized resource allocation schemes that take advantage of the multiple antennas at the WPD to minimize the per-user exposure dose, when full or limited knowledge of each user channel is available. Our new schemes can significantly reduce the individual exposure dose of WPD users (by approximately 80%) in comparison with the most relevant existing resource allocation schemes. Our results also provide insights into the logarithmic relationship between the per-user exposure dose and the number of receive antennas (or the number of time slots), and how such a parameter can be exploited to further reduce the exposure and/or provide a higher SE while maintaining a low exposure dose. [ABSTRACT FROM AUTHOR]

Inspired by the promising potentialof reconfigurable intelligent surface (RIS)-based transmission, this paper investigates the RIS-aided vehicle-to-everything (V2X) communication system with the social trust, where multiple vehicle-to-infrastructure (V2I) links share the spectrum with vehicle-to-vehicle (V2V) links. Subject to the reliability constraint of V2V links, the sum V2I link capacity is maximized, through jointly optimizing the power allocation, RIS reflection coefficients and spectrum allocation. The formulated mix-integer and non-convex problem is effectively converted into three subproblems, which can be iteratively solved by our proposed three-stage joint resource allocation and RIS optimization (JRARO) algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm, and that the RIS can significantly improve the quality of vehicular communications in terms of the sum V2I capacity. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an intelligent reflecting surface (IRS) aided millimeter wave (mmWave) communication system using hybrid precoding of subarray-connected structure at the base station. To maximize the received signal power of the user, we jointly optimize the active beamforming at the BS and passive beamforming at the IRS, respectively. Exploiting the rank-one property of mmWave channels, we transform the original problem into an approximate optimization problem and give the closed-form solution of the approximated problem. Simulation results show that our proposed scheme can effectively improve the received signal quality of the user with a high energy efficiency. [ABSTRACT FROM AUTHOR]

Due to its simple implementation and higher spectral efficiency (SE), the direct current (DC) biased optical orthogonal frequency division multiplexing (DCO-OFDM) scheme has been extensively employed in visible light communication (VLC) system. In this paper, the tradeoff between energy efficiency (EE) and SE in DCO-OFDM system is investigated. Specifically, the achievable SE of DCO-OFDM system with power and bit loading is derived, and the closed-form expression of the EE-SE tradeoff is obtained, on the basis of which, the optimal SE to maximize EE is derived, and the optimal parameters of the DCO-OFDM system, e.g., the horizontal and vertical distances of optical transmission network, the number of subcarriers, to maximize the EE-SE tradeoff are theoretically derived. Moreover, to derive the optimal transmitted power, a Pareto optimal set based multi-objective optimization (MOO) is formulated, which is successfully transformed into a single-objective optimization (SOO), with the help of a proposed EE-SE tradeoff metric. Finally, simulation results are provided to verify the presented EE-SE tradeoff, the derived optimal SE, the optimal vertical distance of optical transmission network and the optimal transmitted power. [ABSTRACT FROM AUTHOR]

The increasing demand for rich multimedia services and the emergence of the Internet of Things (IoT) pose challenging requirements for the next-generation vehicular networks. Such challenges are largely related to high spectral efficiency and low latency requirements in the context of massive content delivery and increased connectivity. In this respect, caching and non-orthogonal multiple access (NOMA) paradigms have been recently proposed as potential solutions to effectively address some of these key challenges. In this paper, we introduce cache-aided NOMA as an enabling technology for vehicular networks. In this context, we first consider the full file caching case, where each vehicle caches and requests entire files using the NOMA principle. Without loss of generality, we consider a two-user vehicular network communication scenario under double Nakagami $\text{-}m$ fading conditions and propose an optimum power allocation policy. To this end, an optimization problem that maximizes the overall probability of successful decoding of files at each vehicle is formulated and solved. Furthermore, we consider the case of split file caching, where each file is divided into two parts. A joint power allocation optimization problem is formulated, where power allocation across vehicles and cached split files is investigated. The offered analytic results are corroborated by extensive results from computer simulations and interesting insights are developed. Indicatively, it is shown that the proposed caching-aided NOMA outperforms the conventional NOMA technique. [ABSTRACT FROM AUTHOR]

In Multiple-input multiple-output (MIMO) broadcast channels (BCs), the transmitter simultaneously broadcasts signals to multiple receivers at same frequency band, resulting in that the communication capacity is affected by both interference, channel fading and random noise. Although channel fading can be mitigated by transceivers, the signal-to-noise-ratio (SNR) of the practical communication system is still dynamically changing due to the randomly changing noise. Traditional MIMO transceiver optimization algorithms can not flexibly adapt to the dynamic changes of SNR, resulting in large performance degradation. In this paper, we comprehensively consider signal power and signal space dimensions of the received signal in MIMO BCs, and propose two transceiver optimization algorithms which can dynamically adapt to the variance of SNRs. In the proposed algorithms, SNR is adopted to be an adjustment factor to cope with its variance. When SNR is low, i.e, large noise, the algorithm parameters are automatically adjusted so that the signal power is preserved as much as possible to combat the loss of communication capacity caused by large random noise. Correspondingly, under the condition of high SNR environment, the algorithm parameters are adjusted automatically to effectively compress the inter-user-interference (IUI) and intra-user-inter-stream-interference (ISI) by optimizing signal space dimensions. Simulation results show that in different SNR environments, the proposed algorithms can automatically adjust the focus of optimization, so that the optimization of signal power and signal space dimensions can automatically adapt to different SNRs. Compared with traditional transceiver optimization algorithms, the proposed algorithms can improve the communication capacity within a large dynamic range of SNR. [ABSTRACT FROM AUTHOR]

The rapid development of maritime activities has led to the emergence of more and more computation-intensive applications. In order to meet the huge demand for wireless communications in maritime environment, mobile edge computing (MEC) is considered as an effective solution to provide powerful computing capabilities for maritime terminals of resource scarcity or latency sensitive. A basic technology to implement MEC is virtual machine (VM) multiplexing, through which multi-task parallel computing on a server is realized. In this paper, a two-layer unmanned aerial vehicles (UAVs) maritime communication network with a centralized top-UAV (T-UAV) and a group of distributed bottom-UAVs (B-UAVs) is established and MEC is used on T-UAV. We aim to solve the latency minimization problem for both communication and computation in this maritime UAV swarm mobile edge computing network. We reformulate this problem into a Markov decision process (MDP), since it is a non-convex and multiply constrained but has the characteristics of MDP. Based on this MDP model, we take deep reinforcement learning (DRL) as our tool to propose a deep Q-network (DQN) and a deep deterministic policy gradient (DDPG) algorithms to optimize the trajectory of T-UAV and configuration of virtual machines (VMs). Using these two proposed algorithms, we can minimize the system latency. Simulation results show that the given solutions are valid and effective. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an unmanned aerial vehicle (UAV) communication system, where the trajectories of multi-UAVs are designed for the data collection mission of IoT nodes. We aim at minimizing the mission time with constraints of UAV’s maximum speed and acceleration, the collision avoidance, and communication interference among UAVs. We propose a three-step approach to solve this problem, which is based on the K-means algorithm, and Deep Reinforcement Learning (DRL) with a distributed manner and a centralized manner. The mutual influences like collision avoidance and interference among UAVs are explicitly expressed in our algorithm. Numerical results show the advantage of our proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, an aerial heterogeneous wireless network consists of a high altitude platform (HAP) and low altitude platforms (LAPs) as aerial base stations (ABSs) in a downlink orthogonal division multiple access (OFDMA) transmission is investigated. Two types of spectrum sharing, including spectrum underlay access (SUA) and spectrum overlay access (SOA) are employed to evaluate the inter-tier interference. Distributed problem formulation and solving method are used to reduce the computational complexity, communication overhead and system latency. Subcarrier assignment and power allocation in spectral efficiency (SE), energy efficiency (EE) and transmission power optimization problems are accomplished by the proposed two-layer algorithm. In the proposed algorithm, first, subcarrier assignment based on constraints of the optimization problems is carried out to cover both SUA and SOA with low computational complexity. Second, the proposed algorithm performs power allocation. The simulation results show the relation between spectrum reuse and inter-tier interference, such that system SE, EE and the number of served users are maximized when both spectrum reuse and inter-tier interference management are performed together. [ABSTRACT FROM AUTHOR]

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless powered communication network, where a UAV serves multiple energy constrained wireless sensor nodes (WSNs). In such a network, a UAV transmits wireless power to a group of WSNs which exploits the harvested power to transfer their own information towards the UAV in the uplink. For practical scenarios, a nonlinear energy harvesting saturation model is considered at WSNs. We aim to design resource allocation which maximizes the achievable sum rate in the uplink and minimizes the downlink transmit power simultaneously. The design is formulated as a multi-objective optimization problem (MOOP) which optimizes the UAV's 3D position, its transmit power, the time splitting ratio, the uplink transmission time and the beamwidth angle of the UAV. The formulated problem is a non-convex problem which is generally intractable. To address the MOOP, a weighted Tchebycheff method is proposed. The numerical results show a trade-off between the minimum power in the downlink and the maximum rate as well as energy efficiency in the uplink. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) communication enables ultra-high rate data transmission in wireless environments, but its performance depends on blockages between the transmitter and receiver significantly. To overcome the blockage problem, we consider two approaches: multi-connectivity and network densification. The multi-connectivity opens up a new paradigm for user centric communication, where each user autonomously chooses multiple base stations (BSs) to attain high quality services. The network densification allows network operators to deploy many BSs in a limited area, thereby providing qualified communication services to users. In this paper, we study user equipment (UE) association control in user centric mmWave communication environments. When a UE arrives, the network provides the UE with information on the resource status of adjacent BSs, and the UE generates a set of candidate BSs. For access control, we formulate two optimization problems that aim to minimize peak load and sum load, respectively. They are non-convex and combinatorial, so their computational complexity grows exponentially with the problem size. As a solution, we develop an access pricing and peak load limit control scheme. Through numerical experiments, we investigate its impact on the performance of mmWave communication systems. The results show that our proposed scheme successfully provides high quality services to users and allocates the network load fairly over the network. [ABSTRACT FROM AUTHOR]

In this paper, we consider the secure transmission problem in an unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) assisted mmWave networks in the presence of an eavesdropper. The UAV base station (UAV-BS) and IRS are deployed to overcome the blockages. Artificial noise (AN) is exploited against the eavesdropper. Aiming to maximize the secrecy rate, we jointly design the positions and beamforming of UAV-BS and IRS, where the positions here represent the deployed position of the UAV-BS and the activation position of the IRS. Meanwhile, the maximum transmits power, minimum height constraint for UAV-BS, and the legitimate receiver minimum rate constraint are required. To obtain the optimal UAV-BS position, the elevation angle-dependent probabilistic LoS air-to-ground channel is exploited in this network. To tackle the formulated non-convex problem, we divide it into two subproblems: (1) design UAV-BS and IRS positions; (2) design UAV-BS and IRS beamforming. Moreover, to reduce the complexity, we propose an ideal beamforming model to find the near-optimal positions of UAV-BS and IRS. Then, semidefinite relaxation (SDR) is utilized to cope with the highly coupled and high-dimensional variable vectors. Finally, we obtain a sub-optimal solution by an efficient and low-complexity alternating optimization algorithm. Numerical results demonstrate the improvement of secrecy rate by jointly designing the positions as well as the significant performance gains achieved by our proposed schemes over benchmark schemes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are anticipated to be widely deployed in future wireless communications, due to their advantages of high mobility and easy deployment. However, the broadcast nature of air-to-ground line-of-sight wireless channels brings a new challenge to the information security of UAV-ground communication. This paper tackles such a challenge in the physical layer by exploiting the mobility of UAV via its trajectory design. We consider a UAV-ground communication system with multiple potential eavesdroppers on the ground, where the information on the locations of the eavesdroppers is imperfect. We formulate an optimization problem, which maximizes the average worst case secrecy rate of the system by jointly designing the robust trajectory and transmit power of the UAV over a given flight duration. The nonconvexity of the optimization problem and the imperfect location information of the eavesdroppers make the problem difficult to be solved optimally. We propose an iterative suboptimal algorithm to solve this problem efficiently by applying the block coordinate descent method, $\mathcal {S}$ -procedure, and successive convex optimization method. Simulation results show that the proposed algorithm can improve the average worst case secrecy rate significantly, as compared to two other benchmark algorithms without robust design. [ABSTRACT FROM AUTHOR]

Phase-only beamforming (POB) plays an important role in modern radar and communication systems. The NP-hard nature of POB makes it difficult to be solved for large-scale antenna arrays. This paper studies two fast and simple GP algorithms for the problem of POB. First we give a new interpretation of the GP method under the framework of sequential quadratic programming (SQP). Then we prove that the GP operation gives a strict global minimizer of the subproblem of SQP. Moreover, the larger the step size in GP operation, the smaller the minimum value of the subproblem. Based on this observation we design two variable step size GP (VSGP) algorithms that improve the convergence speed of the GP method significantly. Furthermore, the VSGP algorithms have a simple structure, making them easy to be implemented in chips. Finally, we derive a necessary and sufficient condition for the Karush-Kuhn-Tucker (KKT) points of the POB problem. We also prove that, for any initialization, all limit points of the iterates generated by the VSGP algorithms converge to the KKT points. Simulation results show that the proposed algorithms outperform the state-of-the-art algorithms in terms of convergence speed and computational cost. [ABSTRACT FROM AUTHOR]

Both the jammer and the eavesdropper pose severe threat to wireless communications due to the broadcast nature of wireless channels. In this paper, an intelligent reflecting surface (IRS) assisted secure communication system is considered, where a base station (BS) wishes to reliably convey information to a user, in the presence of both a jammer and an eavesdropper whose transmit informations are not completely known. Specifically, with the imperfect third-party node's channel state information (CSI) and no knowledge of the jammer's transmit beamforming, we aim to maximize the system achievable rate by jointly designing the BS's transmit beamforming and the IRS's reflect beamforming, while limiting the information leakage to the potential eavesdropper. Due to the non-convexity and intractability of the original problem induced by the incompleted information, we utilize the auxiliary variables, Cauchy-Schwarz inequality, and General Sign-Definiteness transformation to convert the original optimization problem into a tractable convex optimization problem, and then obtain the high-quality optimal solution by using the successive convex approximation and penalty convex concave procedure. Numerical simulations demonstrate the superiority of our proposed optimization algorithm compared with existing approaches, and also reveal the impact of key parameters on the achievable system performance. [ABSTRACT FROM AUTHOR]

To increase the feasibility of wireless powered communication networks (WPCNs), the synergy of efficient multiple access protocols and diverse types of resources, e.g., multiple antennas, memories, and energy storage devices is of paramount importance. To this end, in this paper, we introduce the use of buffered sources in WPCNs with uplink non-orthogonal multiple access, in which a multi-antenna hybrid access point can both transmit energy and receive information. The proposed scheme aims at maximizing the long-term utility function, which is determined by the achieved average data rate and fairness among sources. The formulated long-term time-average optimization problem is converted into instantaneous ones by exploiting the Lyapunov optimization framework. The derived analytical solutions reveal the impact of data buffers’ length on the decoding order of sources’ messages. Finally, it is shown that the proposed scheme achieves superior long-term utility compared to the considered baseline schemes with non-buffered sources and buffered sources with orthogonal multiple access. Additionally, it is demonstrated that our proposed scheme can ensure fairness without reducing the average data rate. [ABSTRACT FROM AUTHOR]

Hybrid beamforming design for OFDM systems is hugely challenging since their analog precoders and combiners are shared among all subcarriers. This paper proposes a novel two-stage joint hybrid precoder and combiner design for maximizing the average achievable sum-rate of frequency-selective millimeter-wave massive MIMO-OFDM systems. In the proposed approach, the analog precoder and combiner design is formulated as a constrained Tucker2 tensor decomposition problem, which allows maximizing the sum of the effective baseband gains over every subcarrier while suppressing inter-user and intra-user interferences. The solution is obtained by a projected alternate least-square-based algorithm, which is suitable for both single-user (SU) and multiuser (MU) systems. The digital precoder and combiner for SU systems is obtained from the effective baseband channel’ SVD. In contrast, for MU systems, it is obtained from the regularized channel diagonalization method, which balances multiuser interference and noise suppression. Numerical simulation results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper aims at solving two classes of energy efficiency (EE) maximization problems in multiple channels wireless communication systems. First, the EE maximization problem with sum power constraint is solved based on the geometric water-filling approach; and second, the approach is extended into the EE maximization problem with additional least throughput requirement constraint. Our proposed algorithms make use of the water-filling structure of the optimal solution and provide exact and computation efficient solution to the energy-efficient power allocation problems. The proposed algorithms also have excellent scalability, which is applicable for large-scale wireless communication systems. Optimality of the proposed algorithms is strictly proved, and the proposed algorithms only require low-degree polynomial computational complexity. Numerical results are presented to demonstrate the efficiency of the proposed algorithms. To the best of our knowledge, no prior algorithms in the existing literature could provide such solutions to the EE maximization problems under the merit of exactness and the efficiency. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communication enables exchanging information between vehicles by broadcasting safety and beacon messages. Safety applications based on a so-called dedicated short-range communication, which are one of the main applications of V2V, require a very strict network performance for safety messages. In this paper, we investigate the effect of the beacon rate on the network performance of safety messages and develop an optimization problem to recommend optimal beacon rates based on a utility maximization framework. The message utility is constructed to account for the reliability requirements of safety messages and maintain the accuracy of neighborhood information collected by beacons. Specifically, we obtain the relationship between the optimal beacon rate and the system parameters. Our results show that the optimal beacon rates can meet the requirements of safety applications and improve the network performance up to 40%. [ABSTRACT FROM PUBLISHER]

Power allocation (PA) is an efficient approach to enhance the system performance of an analog network coding (ANC)-based two-way relay channel (A-TWRC). In previous works, optimal PAs, in the sense of minimizing the outage probability and maximizing the achievable sum rate for A-TWRCs, were studied, respectively. In this paper, by virtue of the Lagrange method, we theoretically prove that optimal PA problems in the sense of minimizing the outage probability and maximizing the achievable sum rate are equivalent to each other. Meanwhile, a unified optimal PA scheme for A-TWRCs is proposed. We show that some previous optimal PA schemes are special cases of the proposed unified scheme under certain conditions. Simulation results show that the proposed unified PA scheme obtains the optimal outage probability and achievable sum-rate performance, whereas previous works approach the proposed unified PA scheme on either outage or achievable sum-rate performance. [ABSTRACT FROM PUBLISHER]

This paper studies multi-agent deep reinforcement learning (MADRL) based resource allocation methods for multi-cell wireless powered communication networks (WPCNs) where multiple hybrid access points (H-APs) wirelessly charge energy-limited users to collect data from them. We design a distributed reinforcement learning strategy where H-APs individually determine time and power allocation variables. Unlike traditional centralized optimization algorithms which require global information collected at a central unit, the proposed MADRL technique models an H-AP as an agent producing its action based only on its own locally observable states. Numerical results verify that the proposed approach can achieve comparable performance of the centralized algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a wireless powered communication network where a hybrid access point can wirelessly activate the power of source nodes, and receive their information. Considering the time-switching protocol that implies energy harvesting, and energy usage are separated by time, a novel graphics-based multi-epoch power adjustment (GB-MPA) strategy is proposed to maximize the sum-throughput over a finite time horizon consisting of multiple transmission epochs. The proposed strategy relies on graphical constraint conditions, and allows the elimination of restrictions corresponding to the objective of throughput maximization. Moreover, the proposed GB-MPA strategy can be combined with the time-switching factor optimization by employing an alternating optimization procedure for performance enhancement. Simulation results show the accuracy of the proposed strategy, reduced complexity compared with the conventional convex optimization method, and the existing multi-epoch power adjustment strategy, and the compatibility with the alternating optimization procedure for developing the joint power, and time allocation scheme. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of utility maximization in the uplink of vehicle-to-infrastructure communication systems. The studied scenarios consider four practical aspects of mobile radio communication links: i) Interference between adjacent channels, ii) interference between roadside units along the way, iii) fast and slow channel fadings, and iv) Doppler shift effects. We present first the system model for the IEEE 802.11p standard, which considers a communication network between vehicles and roadside infrastructure. Next, we formulate the problem of utility maximization in the network, and propose a distributed optimization scheme. This distributed scheme is based on a two-loop feedback configuration, where an outer-loop establishes the optimal signal to interference-noise ratio (SINR) that maximizes the utility function per vehicle and defines a quality-of-service objective. Meanwhile, inner-control loops adjust the transmission power to achieve this optimal SINR reference in each vehicle node regardless of interference, time-varying channel profiles and network latency. The computation complexity of the distributed utility maximization scheme is analyzed for each feedback loop. Simulation results indicate that the proposed scheme reaches the objective SINRs that maximize utility and improve energy efficiency in the network with a low time cost. The results also show that the maximum utility is consistently achieved for different propagation scenarios inside the vehicular communication network. [ABSTRACT FROM AUTHOR]

In this paper, we consider an unmanned aerial vehicle (UAV) assisted communications system, including two cooperative UAVs, a wireless-powered ground destination node leveraging simultaneous wireless information and power transfer (SWIPT) technique, and a terrestrial passive eavesdropper. One UAV delivers confidential information to destination and the other sends jamming signals to against eavesdropping and assist destination with energy harvesting. Assuming UAVs have partial information about eavesdropper's location, we propose two transmission schemes: friendly UAV jamming (FUJ) and Gaussian jamming transmission (GJT) for the cases when jamming signals are known and unknown a priori at destination, respectively. Then, we formulate an average secrecy rate maximization problem to jointly optimize the transmission power and trajectory of UAVs, and the power splitting ratio of destination. Being non-convex and hence difficult to solve the formulated problem, we propose a computationally efficient iterative algorithm based on block coordinate descent and successive convex approximation to obtain a suboptimal solution. Finally, numerical results are provided to substantiate the effectiveness of our proposed multiple-UAV schemes, compared to other existing benchmarks. Specifically, we find that the FUJ demonstrates significant secrecy performance improvement in terms of the optimal instantaneous and average secrecy rate compared to the GJT and the conventional single-UAV counterpart. [ABSTRACT FROM AUTHOR]

In this paper, we study a dual-hop wireless powered communication network (DH-WPCN), where the two-way communication between the users and the access point (AP) is assisted by a relay. Assuming a piece-wise linear energy harvesting (EH) model for the users and the relay, we investigate the problem of total throughput maximization for both amplify-and-forward (AF) and decode-and-forward (DF) relaying modes. To deal with the non-convexity of the throughput maximization problem, we apply the block coordinate descent (BCD) technique and alternately optimize different blocks of optimization variables until convergence is achieved. Numerical results demonstrate the substantial performance gain of the proposed scheme with piece-wise linear EH model as compared to the case with oversimplified linear EH model. This is due to the fact that our proposed scheme takes into consideration the saturation effect of practical EH circuits in optimizing the resource allocation. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a UAV-assisted cooperative communication system based on simultaneous wireless information and power transfer (SWIPT), where the UAV serves as a relay and its transmission capability is partly powered by radio signal from the source via the time-sharing mechanism. We study the end-to-end cooperative throughput maximization problem by optimizing the UAV's decision profile, power profile and trajectory for both amplify-and-forward (AF) and decode-and-forward (DF) protocols. The problem is decomposed into three optimization subproblems for decision profile, power profile and trajectory, and solved through alternating optimization, by which each of the subproblems is solved with the other two fixed. A binarization algorithm is further proposed to make the decision profile feasible. We show that the proposed solution outperforms not only two SWIPT-based strategies, but also a similar solution from an existing work without consideration for SWIPT. In addition, results indicate that the proposed algorithm performs efficiently in both optimality and convergence. [ABSTRACT FROM AUTHOR]

Device to device (D2D) communication is a promising paradigm to support data sharing in the fifth generation (5G) small cell networks. However, D2D communication is more vulnerable to security and privacy threats due to direct wireless connection between the proximity devices. In this paper, we exploit blockchain technology to propose a new distributed and secure data sharing framework named D2D blockchain, where we deploy a set of Access Points (APs) to verify end users’ transactions. Besides, in order to enable the end users to receive the required data on time in the D2D blockchain, we propose two new schemes for the transaction confirmation procedure including a relay-assisted transaction relaying scheme and a Delegated Proof-of-Stake (DPoS) based lightweight block verification scheme. Furthermore, to incentivize the relay devices to help in relaying transactions in congested areas, and to encourage the verifiers to contribute their resources in block verification, we design a two-stage contract theory based joint optimization scheme. To measure the quality of the relayed transaction and the verified block, we formulate two models: value of transaction relaying (VoTR) and value of block verification (VoBV), and then design the optimal contract for each relay device and each verifier. Security analysis and numerical results illustrate that the proposed D2D blockchain and the designed joint optimization scheme are secure and efficient for data sharing among end users. [ABSTRACT FROM AUTHOR]

This paper tackles a power minimization problem of interleave-division multiple-access (IDMA) systems over a fading multiple-access channel. The problem is minimizing the total power received by the receiver while keeping the bit error rates (BERs) of all users below a predefined value. The original formulation of the problem has highly nonlinear and implicitly defined functions, which render most existing optimization methods incapable. A new formulation is proposed in this paper, whose solution can effectively be obtained by a branch-and-bound (B&B) technique. An algorithm is devised based on B&B, and its effectiveness is also demonstrated by numerical experiments of systems with a moderate numbers of users. [ABSTRACT FROM AUTHOR]

The placement of Unmanned Aerial Vehicle (UAV) based aerial Base station (BS) has emerged as a viable solution to the contemplated high traffic demands of the future wireless networks. Nevertheless, the practical realization of UAVs for wireless coverage provisioning demands efficient utilization of its limited onboard energy reserves. This paper studies the power-domain Non-Orthogonal Multiple Access (NOMA) scheme for energy-efficient placement of the UAV-BS. The objective of maximized information bits per unit energy consumption of the UAV-BS is developed assuming a multi-Quality of Service (QoS) scenario. Also, the efficiency analysis is carried out considering the composite of energies consumed for the signal transmission and hover operation of the UAV-BS. The formulated Non-Linear Fractional Problem (NLFP) as a function of transmission power and altitude is solved by employing an alternating optimization technique based on a nested Dinkelbach structure. An investigation into the user pairing scheme also sheds new light on its effect upon the constrained feasible altitude range of the UAV-BS. Consequently, a computationally efficient approach is devised to render solution space based on the identified subset of user pairs. The numerical analyses of the proposed scheme signify a substantial increase in the energy efficiency of the NOMA based UAV-BS in comparison to the baseline scheme of Orthogonal Multiple Access (OMA). Thus, the improved energy efficiency enables enhanced coverage for the UAV-BS. [ABSTRACT FROM AUTHOR]

In wireless mobile communication systems, radio spectrum is a limited resource. However, efficient use of available channels has been shown to improve the system capacity. The role of a channel assignment scheme is to allocate channels to cells or mobiles in such a way as to minimize call blocking or call dropping probabilities, and also to maximize the quality of service. Channel assignment is known to be an NP-hard optimization problem. In this paper, we have developed an evolutionary strategy (ES) which optimizes the channel assignment. The proposed ES approach uses an efficient problem representation as well as an appropriate fitness function. Our paper deals with a novel hybrid channel assignment based scheme called D-ring. Our D-ring method yields a faster running time and simpler objective function. We also propose a novel way of generating the initial population which reduces the number of channels reassignments and therefore yields a faster running time and may generate a possibly better initial parent. We have obtained better results (as well as faster running time) than a similar approach in literature. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impact of outdated channel estimates on the overall performance of a two-way multirelay system that employs a three-phase (3P) analog network coding (ANC)-based opportunistic relay selection (ORS) scheme under Rayleigh fading. Specifically, considering the imperfect estimation and the time variation of the fading channels, we formulate the 3P ANC-based ORS strategy for the best relay among multiple relays in terms of both outdated and imperfect channel state information (CSI). With such a practical CSI modeling, we derive a tight lower bound expression for the overall outage probability (OOP) and an upper bound expression for the ergodic sum-rate (ESR) of the considered scheme. In addition, by deriving an asymptotic outage behavior in the high signal-to-noise ratio (SNR) regime, we highlight the impact of outdated and imperfect CSI on the achievable diversity order. Moreover, based on the asymptotic analysis, we investigate three optimization problems, namely, optimal relay power allocation for fixed relay location, optimal relay location for fixed power allocation, and jointly optimal relay power allocation and location to minimize the OOP and to maximize the ESR of the considered system. Our results reveal that the optimal relay power allocation has a more significant impact on the performance when the CSI is imperfect, whereas the optimal relay location has a more noticeable impact when the CSI is outdated and unbalanced over the two hops. Furthermore, we show that the imperfect CSI can degrade system performance more severely than the outdated CSI, and it can even reduce the system diversity order to zero. The numerical and simulation results corroborate our theoretical analysis and findings and demonstrate that the 3P ANC-based ORS scheme can achieve better performance than its two-phase counterpart, depending upon the quality of the direct link, particularly in the medium-to-high-SNR regime. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of power control for sum-rate maximization on multiple interfering links [transmitter–receiver pairs (Tx–RX)] under a sum power constraint. We consider a single-frequency network, where all pairs are operating in the same frequency band, thereby creating interference with each other. We study the power-allocation problem for sum-rate maximization with and without quality-of-service (QoS) requirements on individual links. When the objective is only sum-rate maximization without QoS guarantees, we develop an analytic solution to decide optimal power allocation for a two-TX–RX-pair problem. We also develop a low-complexity iterative algorithm for a three-TX–RX-pair problem. For a generic N>\3 TX–RX pair problem, we develop two low-complexity suboptimal power-allocation algorithms. The first algorithm is based on the idea of making clusters of two or three TX–RX pairs and then leveraging the power-allocation results obtained for the two- and three-TX–RX-pair problems. The second algorithm is developed by using a high signal-to-interference-plus-noise ratio (SINR) approximation, and this algorithm can be also implemented in a distributed manner by individual TXs. We then consider the same problem but with additional QoS guarantees for individual links. We again develop an analytic solution for the two-TX–RX-pair problem and a distributed algorithm for N>\2 TX–RX pairs. [ABSTRACT FROM PUBLISHER]

In cooperative communication networks, owing to the nodes' arbitrary geographical locations and individual oscillators, the system is fundamentally asynchronous. Such a timing mismatch may cause rank deficiency of the conventional space–time codes and, thus, performance degradation. One efficient way to overcome such an issue is the delay-tolerant space–time codes (DT-STCs). The existing DT-STCs are designed assuming that the transmitter has no knowledge about the channels. In this paper, we show how the performance of DT-STCs can be improved by utilizing some feedback information. A general framework for designing DT-STC with limited feedback is first proposed, allowing for flexible system parameters such as the number of transmit/receive antennas, modulated symbols, and the length of codewords. Then, a new design method is proposed by combining Lloyd's algorithm and the stochastic gradient-descent algorithm to obtain optimal codebook of STCs, particularly for systems with linear minimum-mean-square-error receiver. Finally, simulation results confirm the performance of the newly designed DT-STCs with limited feedback. [ABSTRACT FROM PUBLISHER]

This paper considers a multiuser simultaneous wireless information and power transfer system with a nonlinear energy harvesting model, in which a multiantenna base station estimates the downlink channel state information (CSI) via uplink pilots. Each single-antenna user is equipped with a power splitter. Three crucial issues on resource management for this system include: 1) power-efficient improvement, 2) user-fairness guarantee, and 3) nonideal channel reciprocity effect mitigation. Potentially, a resource allocation scheme to address jointly such issues can be devised by using the framework of multiobjective optimization. However, the resulting problem might be complex to solve since the three issues hold different characteristics. Therefore, we propose a novel method to design the resource allocation scheme. In particular, the principle of our method relies on structuralizing mathematically the issues into a cross-layer multilevel optimization problem. On this basis, we then devise solving algorithms and closed-form solutions. Moreover, to instantly adapt the CSI changes in practice while reducing computational burdens, we propose a closed-form suboptimal solution to tackle the problem. Finally, we provide numerical results to show the achievable performance gains using the optimal and suboptimal solutions, and then validate the proposed resource allocation scheme. [ABSTRACT FROM AUTHOR]

Ultradense heterogeneous networks (HetNets) are emerging as an inevitable approach to tackle the capacity crunch in cellular networks. However, imbalanced load among small and macrocells and poor resource utilization as a consequence in HetNets remains a long-standing problem. This paper addresses this problem by presenting a solution for maximization of coverage and capacity while minimizing load imbalance among macro and small cells. Most recent studies on the topic focus on either optimization of coverage, capacity or load, or a combination of two of these three intertwined objectives. We formulate the optimization problem as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity, and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization (CCO) and load balancing (LB) self-organizing network (SON) functions. In the presented joint CCO-LB solution, a conflict free operation of CCO and LB is ensured by designing a novel load aware user association methodology and resolving the effects of shadowing on coverage probability using stochastic approximation. The problem is proven to be nonconvex and is solved using genetic algorithm, sequential quadratic programming, and pattern search algorithms. The proposed CCO-LB solution is compared against two recently proposed CCO and CCO-LB solutions in the literature. Results show that the proposed solution can yield significant gain in terms of throughput, spectral efficiency, and load distribution. [ABSTRACT FROM AUTHOR]

This paper considers a heavy loaded full-duplex (FD) small cell consisting of one FD base station and multiple half-duplex users where the number of subchannels is less than the number of uplink and downlink users. With the objective of maximizing the total sum-rate, a joint power allocation, and user pairing problem is formulated. To solve this problem, a novel decomposition method is proposed, which decouples the power allocation and user pairing problem into two subproblems. It is proven that the optimal solution of subproblems is the optimal solution of the original problem. Then, optimal algorithms are developed for the two subproblems. Numerical results show that the proposed algorithm achieves better performance compared with the baselines of the greedy algorithm and random pairing algorithm. [ABSTRACT FROM AUTHOR]

Traffic offloading via device-to-device communications is expected to play a major role to meet the exponential data traffic growth in wireless networks. In this paper, we focus on the problem of user capacity maximization in ultra-dense heterogeneous networks with device-to-device cooperation, where a large number of users in a given geographical area request common data content, such as video on demand streaming, with strict quality of service guarantees. We aim at finding the best strategy for delivering the content either over long range connectivity from the access points or short range connectivity from peer mobile terminals while meeting a target rate per served user. We formulate the multiuser resource management problem as an optimization problem including traffic offloading and channel allocation considering a very high user density reaching up to 1.75 users per square meter. We extend the system model to include mobile terminals acting as content owners by having the content cached locally, in order to further enhance the network capacity and coverage. We then propose an iterative resource management solution, which first solves the optimization subproblem of traffic offloading with orthogonal channels, and then optimally allocates channels to the transmitters with minimized interference due to channel reuse. We also propose efficient suboptimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra-dense networks. For performance evaluation, we consider a realistic scenario consisting of a stadium topology with thousands of mobile terminals active simultaneously. We generate results as a function of a wide range of system parameters, and demonstrate that the proposed algorithms achieve near-optimal performance with notably low time complexity. [ABSTRACT FROM AUTHOR]

Motivated by the increasing interest in energy-efficient communication systems, the relation between energy efficiency (EE) and spectral efficiency (SE) for multiple-input–multiple-output (MIMO) systems is investigated in this paper. To provide insights into the design of practical MIMO systems, we adopt a realistic power model and consider both independent Rayleigh fading and semicorrelated fading channels. We derived a novel and closed-form upper bound (UB) for the system EE as a function of SE. This UB exhibits great accuracy for a wide range of SE values and, thus, can be utilized for explicit assessment of the influence of SE on EE and for analytically addressing the EE optimization problems. Using this tight EE UB, our analysis unfolds two EE optimization issues: Given the number of transmit and receive antennas, an optimum value of SE is derived, such that the overall EE can be maximized, and given a specific value of SE, the optimal number of antennas is derived for maximizing the system EE. [ABSTRACT FROM AUTHOR]