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This paper considers the downlink of a cognitive radio (CR) network formed by multiple primary and secondary transmitters, where each multiantenna transmitter serves a preknown set of single-antenna users. This paper assumes that the secondary and primary transmitters can simultaneously transmit their data over the same frequency bands to achieve high system spectrum efficiency. This paper considers the downlink balancing problem of maximizing the minimum signal-to-interference-plus-noise ratio (SINR) of the secondary transmitters subject to both the total power constraint of the secondary transmitters and the maximum interference constraint at each primary user due to secondary transmissions. This paper proposes solving the problem using the alternating direction method of multipliers, which leads to a distributed implementation through limited information exchange across the coupled secondary transmitters. This paper additionally proposes a solution that guarantees feasibility at each iteration. Simulation results demonstrate that the proposed solution converges to the centralized solution in a reasonable number of iterations. [ABSTRACT FROM AUTHOR]

In concurrent spectrum sharing scenarios between radars and wireless communication systems, one strict approach to protect the radar from undesired interference is locating the base stations outside a guard (or exclusion) zone of the radar. As a result, users inside that zone experience bad communication coverage. This paper uses Reconfigurable Intelligent Surfaces (RISs) to improve the base station coverage of users in the radar exclusion zone. The paper shows, through analysis and simulation, that joint optimization of base station and RIS locations improves the probability of coverage in the exclusion zone over the shared spectrum without disturbing the radar operation. [ABSTRACT FROM AUTHOR]

This paper studies the performance of two traditional schedulers, namely, proportional fair (PF) and round robin (RR), in the context of relay-enhanced Long-Term Evolution Advanced (LTE-A) networks. These two schedulers are natural candidates for implementation in relay nodes (RNs), and following the results obtained in single-hop networks, mobile operators could be tempted to adopt PF because of the good tradeoff it offers between cell capacity and fairness. Based on a statistical throughput evaluation model, we show that this is not necessarily the right option. The number of RNs, their locations in the cell, and backhaul link quality have a decisive influence on scheduler choice. In some scenarios, it is even not desirable to deploy relays as they degrade the network performance compared with the no-relay case. For the purpose of performance evaluation, we develop a realistic and computationally tractable statistical network model that takes into account fast fading, multiple interferers, cell range expansion bias, backhaul link quality, and traffic load. We also propose optimization of the radio frame structure and a suboptimal RN placement scheme to fairly compare RR and PF. [ABSTRACT FROM AUTHOR]

Caching is a promising approach to address the backhaul traffic congestion problem and boost throughput in fog radio access networks. In this correspondence paper, we investigate a proactive probabilistic caching optimization in wireless fog radio access network where multiple users request different files from multiple base stations (BSs). To assess the performance, we first derive the analytical results of successful transmission probability (STP) using tools of stochastic geometry. With the derived closed-form STP expressions, our objective is to optimize the proactive probabilistic caching distribution to maximize the STP. To reduce the computational complexity, we specially discuss the optimization in high signal-to-noise ratio (SNR). We propose a projection gradient method to get a local optimal solution. Simulation results show that the caching placement optimized by our proposed algorithm increases the STP by about $18\%$ over the best existing caching distribution when ${\rm SNR}=10$ dB with user density $\lambda _u=0.09$ , BS density $\lambda _b=0.1$ and file number $N=100$. [ABSTRACT FROM AUTHOR]

In this paper, the energy efficiency (EE) of multihop relaying over Nakagami- $m$ fading channels is investigated. The “consumption factor” (CF), which is adopted as a metric to evaluate the EE, is derived for both amplify-and-forward and decode-and-forward relaying strategies. Then, based on the obtained expressions, we propose a power-allocation (PA) strategy maximizing the CF. In addition, a suboptimal low-complexity PA algorithm is proposed and analyzed, and the obtained PA scheme is compared in terms of EE with other PA schemes from the literature. Analytical and simulation results confirm the accuracy of our derivations and assess the performance gains of the proposed approach. [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]

In this paper, we propose a cost-efficient beamforming optimization algorithm for multi-user wireless communication systems associated with the intelligent reflecting surface (IRS). From the baseline successive refinement algorithm, which gives a sub-optimal solution for the power minimization problem under the signal-to-interference-plus-noise-ratio (SINR) constraint at each user, several optimization techniques are proposed to reduce the computation complexity while maintaining the algorithm-level performance. To reduce the number of required multiply-accumulate (MAC) operations, we first simplify the complicated matrix inversion by utilizing the channel hardening effect. We also present the two-phase refinement process for the group-level optimization of phase-shift elements, further relaxing the computation complexity as well as the processing latency. Applying the proposed optimization techniques, as a result, numerical results show that the fully-optimized algorithm can reduce the computational costs by up to 89.4% while showing less than 1 dB power loss, leading to the practical solution for the next-generation IRS-aided communication. [ABSTRACT FROM AUTHOR]

For clustered wireless networks, the conventional dual-hop cooperation technique is inefficient since relays with imbalanced channels to the source and the destination may become the bottleneck of the overall transmission. In this paper, a novel cooperative framework called the three-stage relaying (TSR) scheme is proposed to address this problem. In TSR, relays are divided into two clusters/groups, and a virtual multiple-input–multiple-output (MIMO) antenna array is formed by introducing the transmission between the two relay groups. This “relay-to-relay” communication enables transmissions over shorter distances, which naturally breaks the bottleneck emerging in the dual-hop cooperation scheme. An optimization problem is formulated to deal with the relay selection for these two groups, aiming at maximizing the received signal-to-noise ratio (SNR) at the destination, subject to the number-of-relay constraint. This problem turns out to be nontrivial due to the coupling of the relays between two groups. We tackle this challenging problem by decomposing it into two subproblems: One deals with the selection of transmitting relays, and the other deals with the selection of receiving relays. We then propose two heuristic algorithms that achieve the tradeoff between the optimality of the solution and the computational complexity. Through extensive numerical simulations, we show the superiority of TSR over the dual-hop cooperation schemes in both the clustered and centered wireless networks in terms of symbol error rate and throughput. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate multiuser scheduling with joint user pairing, resource allocation, and power control (PC) in Long-Term Evolution (LTE) uplink cellular networks. We first derive the received signal-to-interference-plus-noise ratio (SINR) in spatial multiuser single-carrier frequency-division multiple-access (SC-FDMA) systems with frequency-domain minimum mean square error (MMSE) equalization. Based on this, we formulate an optimization problem as joint multiuser pairing, resource allocation, and PC to maximize the weighted throughput of the system. We then divide it into a two-step optimization problem, which is suboptimal but significantly reduces the complexity. The two-step problem first performs optimal frequency resource allocation and user pairing for a given transmit power value and then adjusts the transmit power to maximize the weighted throughput when frequency resources are allocated and users are paired. With the help of high-interference indicators exchanged among multiple base stations (BSs), we develop an iterative distributed and low-complexity algorithm to further simplify the two-step problem. System-level simulation results show that the proposed algorithm can effectively mitigate interference and significantly outperforms the existing algorithms. [ABSTRACT FROM PUBLISHER]

Cooperative communications via multiple relay nodes is known to provide the benefits of increase diversity and coverage. Simultaneous transmission via multiple relays, however, requires strong coordination between nodes either in terms of slot-based transmission or distributed space–time (ST) code implementation. Dynamically selecting a single best relay out of multiple relays and then using it alone for cooperative transmission alleviates the need for this strong coordination while still reaping the benefits of increased diversity and coverage. In this paper, we consider the design of relay selection (RS) under an imperfect knowledge of long-term channel state information (CSI) at the relay nodes, and we pursue minimax optimization to arrive at a robust RS approach that promises the best guarantee on the worst-case end-to-end signal-to-noise ratio (SNR). We provide some intuitive examples and extensive simulation results, not only in terms of worst-case SNR performance but also in terms of average bit-error-rate (BER) performance, to demonstrate the benefits of the proposed minimax robust RS scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a cellular downlink multiple-input-single-output (MISO) nonorthogonal multiple access (NOMA) secure transmission system, where users are grouped as multiple clusters. Each cluster consists of a central user and a cell-edge user. The central user is an entrusted user, and the cell-edge user is a potential eavesdropper. We focus on the secure beamforming and power allocation design optimization problem which maximizes the sum achievable secrecy rate of central users subject to the transmit power constraint at the base station and transmission rate requirements at cell-edge users. The problem is nonconvex because of coupling optimization variables in the considered fractional quadratically constrained quadratic programming. We propose an alternating optimization-based solution and a constrained concave–convex procedure-based solution to the considered problem. Simulation results demonstrate that our proposed NOMA schemes outperform the conventional orthogonal multiple access scheme. [ABSTRACT FROM AUTHOR]

This paper studies the information-theoretic metric of outage probability for a half-duplex asymmetric two-way amplify-and-forward (AF) relaying. Unlike current research activities, our main purpose is to examine the impact of traffic asymmetry on system outage probability. First, an exact but two-integral-form outage probability is derived over Rayleigh fading channels. To provide more insight, a closed-form asymptotic outage probability is then obtained. It is shown that the outage probability in the system is determined only by the one-way channel or related to the two-way links, depending on the asymmetric level of the two senders' target rates and the terminals' average transmission powers. Following this analysis, optimum power allocation and opportunistic relay selection are studied to optimize the overall performance of the considered AF scenario. Next, with the aid of traffic knowledge, novel power allocation and relay selection criteria are proposed, followed by an analysis of the outage probability with the relay selection. Simulation experiments are performed to validate the proposed studies in this paper. The results show that the proposed power allocation and relay selection methods can achieve significant performance gains in terms of outage probability, regardless of symmetric or asymmetric traffic and channels. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a dual-hop amplify-and-forward (AF) multiple-input–multiple-output (MIMO) relay network, where the source, relay, and destination are each equipped with multiple antennas. The relay is untrusted if it is willing to forward the signal to the destination and, at the same time, acts as a potential eavesdropper to interpret the message from the source. Since there exists no direct link between the source and the destination, a positive secrecy rate cannot be obtained. Addressing this issue, we propose a joint destination-aided cooperative jamming and precoding at both the source and the relay scheme: joint source, relay, and destination precoding (JP). We target at maximizing the secrecy rate by jointly designing the source, relay, and destination precoding matrices and propose an alternating iterative optimization algorithm to tackle the nonconvex problem. Then, a comprehensive study on the asymptotic performance is conducted in a high signal-to-noise ratio (SNR) regime. In particular, we present simple closed-form expressions for the two key performance parameters in the asymptotic secrecy rate, i.e., the high-SNR slope and the high-SNR power offset. Finally, numerical results are conducted to demonstrate the validity of the proposed secure scheme and its performance analysis. [ABSTRACT FROM PUBLISHER]

In heterogeneous networks (HetNets), the load between macrocell base stations (MBSs) and small-cell base stations (SBSs) is imbalanced due to their different transmission powers and locations. This load imbalance significantly impacts system performance and affects the experience of mobile users (MUs) with different priorities. In this paper, we aim to distributively optimize the user association in HetNets with various user priorities to solve the load balancing problem. Since the user association is a binary matching problem, which is NP-hard, we propose a distributed belief propagation (BP) algorithm to approach the optimal solution. We first develop a factor graph model, using the network topology, to represent this user association problem. With this factor graph, we propose a novel distributed BP algorithm by adopting the proportional fairness as the objective. Next, we theoretically prove the existence of the fixed point in our BP algorithm. To be more practical, we develop an approximation method to significantly reduce the computational and communication complexity of the BP algorithm. Furthermore, we analyze some properties of the factor graph relevant to the performance of the BP algorithm using the stochastic geometry. Simulation results show that 1) the proposed BP algorithm well approaches the optimal system performance and achieves a much better performance compared with other association schemes and that 2) the analytical results on the average degree distribution and sparsity of the factor graph match with those obtained from the Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

This paper investigates advanced energy-efficient wireless systems in orthogonal frequency-division multiple access (OFDMA) downlink networks using coordinated multipoint (CoMP) transmissions between the base stations (BSs) in a heterogeneous network (HetNet), which is adopted by Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE)-Advanced to meet International Mobile Telecommunications-Advanced targets. HetNet CoMP has received significant attention as a way of achieving spectral efficiency (SE) and energy efficiency (EE). Usually, in the literature, the total network power consumption is restricted to the sum of the power consumption of all BSs. The significance of the power consumption of the backhaul links in wireless networks is normally omitted for its trivial effect with respect to that of the radio BSs. For SE and EE analysis of HetNet CoMP, the energy and bandwidth consumption of the backhaul is considered, without which, the investigation remains incomplete. However, SE and EE are design criteria in conflict with each other, and a careful study of their tradeoff is mandatory for designing future wireless communication systems. The EE is measured as “throughput (bits) per joule,” whereas the power consumption model includes RF transmit (radiated), circuit, and backhaul power. Furthermore, a nonideal backhaul model such as a microwave link is also investigated within intra-HetNet-CoMP (inside one cell), where an implementing fiber is not feasible. An intercell interference (ICI) coordination method is also studied to mitigate ICI. At the end, a novel resource allocation algorithm is proposed—modeled as an optimization problem—which takes into account the total power consumption, including radiated, circuit, and backhaul power, and the minimum required data rate to maximize EE. Given an SE requirement, the EE optimization problem is formulated as a constrained optimization problem. The considered optimization problem is transformed into a convex optimization problem by redefining the constraint using cubic inequality, which results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using dual decomposition with a projected gradient method. Simulations results demonstrate how backhaul has a significant impact on total power consumption and the effectiveness of the studied schemes. In addition, the results demonstrate that the proposed iterative resource allocation algorithm converges within a small number of iterations and illustrate the fundamental tradeoffs between SE and EE. Our analytical results shed light on future “green” network planning in advanced OFDMA wireless systems like those envisioned for a fifth-generation (5G) system. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) is a promising technique for interference management in wireless networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR) levels since IA mainly concentrates on mitigating the interference, instead of improving the quality of desired signal. Moreover, most of the previous works focused on improving spectrum efficiency, but the energy efficiency (EE) aspect is largely ignored. In this paper, an adaptive energy-efficient IA algorithm is proposed through power allocation (PA) and transmission-mode adaptation for green IA-based wireless networks. The PA problem for IA is first analyzed; then, we propose a PA scheme that optimizes the EE of IA-based wireless networks. When the SNR is low, the transmitted power of some users may become zero. Thus, the users with low transmitted power are turned into the sleep mode in our scheme to save energy. The transmitted power and transmission mode of the remaining active users are adapted again to further improve the EE of the network. To guarantee the interests of all the users, fairness among users is also considered in the proposed scheme. Simulation results are presented to show the effectiveness of the proposed algorithm in improving the EE of IA-based wireless networks. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of coordinated beamforming across a group of base stations (BSs) and frequency slots in the downlink of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA) cellular network. Three figures of merit are considered for system design under a per-BS power constraint: 1) the weighted sum of the rates (WSR) on the frequency slots of the coordinated BSs; 2) the global energy efficiency (GEE), defined as the ratio between the network sum rate and the corresponding consumed power; and 3) the weighted sum of the energy efficiencies (WSEE) on the frequency slots of the coordinated BSs. The Karush–Kuhn–Tucker (KKT) conditions of the considered optimization problems are first derived to gain insight into the structure of the optimal beamformers. Then, we propose a suboptimal design method that can be applied to all considered figures of merit. Numerical results are provided to assess the performance of the proposed beamforming strategies. [ABSTRACT FROM PUBLISHER]

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 is concerned with the problem of relay assignment in cooperative wireless networks having multiple sources, multiple relays, and a single destination. With the objective of maximizing the minimum capacity among all sources in the network, a mixed-integer linear programming (MILP) problem is formulated, which can be solved by standard branch-and-bound algorithms. To reduce computational complexity, a greedy solution in the form of a lexicographic bottleneck assignment algorithm is proposed. Simulation results obtained for the IEEE 802.16j uplink scenarios show that the greedy algorithm performs very close to the optimal solution but at a much lower computational cost. The proposed greedy solution can also be tailored to provide further improvements on other network performance criteria. [ABSTRACT FROM PUBLISHER]

In this paper, we consider cooperative spectrum sensing when two secondary users (SUs) collaborate via the relaying scheme. We investigate two cooperative sensing strategies, i.e., SUs exchange data information locally, and SUs relay information to a central controller. The relaying scheme at each SU is optimized via functional analysis with either the average or peak power constraints. For the local cooperative sensing strategy, the optimal relaying schemes look like amplify-and-forward (AF) in the low-signal-to-noise-ratio (SNR) region and behave like decode-and-forward (DF) in the high-SNR region. The fundamental performance limit using local cooperative sensing is discussed. For the global cooperative sensing strategy, we propose both coherent and noncoherent sensing, depending on whether SUs are synchronized. In the coherent case, a decentralized approach is designed, and each SU optimizes its relaying function locally. In the noncoherent case, we use linear energy combination detector to decouple the relaying function from weight coefficient optimization. Simulation results demonstrate that the proposed protocols achieve much better performance over the existing protocols. [ABSTRACT FROM PUBLISHER]

Fifth-generation (5G) wireless networks are expected to achieve 1000 times higher capacity compared with fourth-generation wireless networks. Thus, improving the spectrum efficiency (SE) is a crucial problem, which must be considered. Cognitive radio (CR) is considered an effective approach to alleviate the spectrum scarcity problem. In this paper, based on the location information of the primary transmitter (PT) and the CR network, we estimate the distance between the PT and the secondary transmitter (ST) and then propose a joint spectrum sensing and power allocation (JSS-PA) scheme to improve the SE of the CR network. In the JSS-PA scheme, we focus on jointly optimizing the sensing parameters and the transmit power of the secondary user (SU) such that the SE is maximized, whereas the primary user (PU) outage constraint is satisfied. When cooperative spectrum sensing is employed to detect the PU's status, we analyze two cooperative strategies, i.e., soft information fusion (SIF) and hard information fusion (HIF). Under the SIF strategy, the optimization of sensing and power (S-OSP) algorithm is proposed to maximize the SE. Under the HIF strategy, the optimization of thresholds (H-OT) algorithm is proposed, and then, the optimization of sensing and power (H-OSP) algorithm is proposed to find the optimal duration of local sensing, the optimal transmit power of the SU, and the optimal final decision threshold. Finally, we present the simulation results to evaluate the performance of the proposed JSS-PA scheme and discuss the effects of the optimal parameters on different schemes under SIF and HIF strategies. [ABSTRACT FROM PUBLISHER]

The maximization of the ergodic capacity for single-stream beamforming, which is a (constrained) transmission scheme referred to as “optimum beamforming,” has been extensively addressed in the open literature for multiple-input–single-output (MISO) Rayleigh fading channels and spatially uncorrelated MISO Rician fading channels with a unit transmit covariance matrix, and closed-form solutions have been derived for these cases. However, optimum beamforming for spatially correlated or uncorrelated MISO Rician fading channels with a nonunit transmit covariance matrix has received less attention and remains a complex multidimensional optimization problem. This paper first proves that this convex constrained optimization problem can be reduced to only one dimension; hence, it can be solved very fast using standard 1-D search algorithms. Then, simulations mainly performed for linear equispaced antenna arrays demonstrate that: 1) the proposed method for the calculation of the optimum beamformer has significantly lower computational complexity compared with other currently used multidimensional algorithms; and 2) the optimum beamformer further improves capacity compared with the (single-stream) beamforming transmission that maximizes the signal-to-noise ratio (SNR) at the receiver, whereas in some operational environments, it achieves ergodic capacity that is very close or equal to the maximum ergodic capacity. [ABSTRACT FROM PUBLISHER]

A fundamental aspect in performance engineering of wireless networks is optimizing the set of links that can be concurrently activated to meet given signal-to-interference-plus-noise ratio (SINR) thresholds. The solution of this combinatorial problem is the key element in scheduling and cross-layer resource management. In this paper, we assume multiuser decoding (MUD) receivers, which can cancel strongly interfering signals. As a result, in contrast to classical spatial reuse, links being close to each other are more likely to be active concurrently. Our focus is to gauge the gain of successive interference cancelation (SIC) and the simpler, yet instructive, case of parallel interference cancelation (PIC) in the context of optimal link activation (LA). We show that both problems are NP-hard, and we develop compact integer linear programming (ILP) formulations to approach global optimality. We provide an extensive numerical performance evaluation, indicating that, for low to medium SINR thresholds, the improvement is quite substantial, particularly with SIC, whereas for high SINR thresholds, the improvement diminishes, and both schemes perform equally well. [ABSTRACT FROM PUBLISHER]

For multisource single amplify-and-forward (AF) relay networks, the optimality of power allocation is revisited from different solution aspect, and a universal optimization framework is proposed. The proposed framework can be used to solve problems that have some common specific structure. As a baseline of the optimization framework, two theorems are proposed to obtain the extreme value of the problem, which is general to realize the optimal resource allocation under some system-specific constraints. Then, based on the proposed theorems, for multiuser single-AF-relay cooperative networks without direct links between sources and destinations, optimal power allocation strategies with low computational complexity are proposed to minimize sum-source-power consumption and sum-system-power consumption. When the number of mobile user is $n$, the overall complexity of both power allocation schemes is \mbox{O}(n) , which is lower than or same as the existing strategies. While comparing with the traditional methods to solve this type of problem, extensive numerical simulation has been conducted for different system setups to justify the effectiveness and efficacy of our proposed power allocation schemes. [ABSTRACT FROM PUBLISHER]

A multi-user multiple-input single-output simultaneous wireless information and power transfer full-duplex system with imperfect channel state information is considered. Under the assumption that the unknown channel estimation error is within a certain range, the self-interference cannot be suppressed completely at the base station and at the multi-user nodes. The proposed design problem is formulated as maximizing the average harvested energy of multi-user nodes and maintaining the signal-to-interference-plus-noise ratio requirement, which is proved to be non-convex. Utilizing semidefinite relaxation and S-procedure methods, a convex optimization problem of optimal beamforming vector for the multi-antenna transmitter is obtained. Moreover, semidefinite relaxation is proved to be tight by showing the existence of rank-one optimal solutions. Simulation results are presented to verify the robustness and effectiveness of the proposed algorithm compared with the non-robust design. [ABSTRACT FROM AUTHOR]

We consider a cooperative non-orthogonal multiple access system with radio frequency energy harvesting, in which a user with good channel harvests energy from its received signal and serves as a decode-and-forward relay for enhancing the performance of a user with poor channel. We here aim at maximizing the weighted sum rate of the system by optimizing the power allocation coefficient used at the source and the power splitting coefficient used at the user with good channel. By exploiting the specific structure of the considered problem, we propose a low-complexity one-dimensional search algorithm, which can provide optimal solution to the problem. As a benchmark comparison, we derive analytic expressions and simple high signal-to-noise ratio (SNR) approximations of the ergodic rates achieved at the two users and their weighted sum with fixed values of the power allocation and the power splitting coefficients, from which the scaling of the weighted sum in the high SNR region is revealed. Finally, we provide representative numerical results to demonstrate the validity of our results. [ABSTRACT FROM AUTHOR]

Fifth generation new radio aims to facilitate new use cases in wireless communications. Some of these new use cases have highly demanding latency requirements; many of the powerful forward error correction codes deployed in current systems, such as the turbo and low-density parity-check codes, do not perform well when the low-latency requirement does not allow iterative decoding. As such, there is a rejuvenated interest in noniterative/one-shot decoding algorithms. Motivated by this, we propose a signal-to-noise ratio-adaptive convolutionally coded system with optimized constellations designed specifically for a particular set of convolutional code parameters. Numerical results show that significant performance improvements in terms of bit-error-rate and spectral efficiency can be obtained compared to the traditional adaptive modulation and coding systems in low-latency communications. [ABSTRACT FROM AUTHOR]

An interference alignment (IA) transceiver design scheme for multiple-input–multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI). [ABSTRACT FROM AUTHOR]

A game-theoretic method for transmit power control across multi-source–destination distributed wireless networks is proposed, which is viable for any number of source–destination pairs, with any number of players (or sources). A dynamic noncooperative repeated game is proposed to optimize both packet delivery ratio (PDR) and transmit power considering a realistic signal-to-interference-plus-noise ratio (SINR) model of the wireless channel. Here, the sources, which are players, transmit concurrently and, thus, have imperfect information about the actions of other players. The game accounts for a limited set of discrete values for transmit power, and the game can be applied in static, quasi-static, and slow-fading channels. If the SINR is feasible, each game stage has a subgame perfect equilibrium, and the game requires fewer iterations to converge to a Pareto-efficient outcome than other appropriate techniques such as SINR discrete power balancing and multiobjective power optimization. In this context, a novel accurate PDR model is given in terms of a compressed exponential function of inverse SINR, which is a function that is realistic for many IEEE 802.11-type implementations of various packet sizes and data rates, and facilitates a tractable analysis and implementation of this dynamic game. [ABSTRACT FROM PUBLISHER]

A robust coded scheme for progressive multimedia transmission is proposed for additive white Gaussian noise, flat Rayleigh fading channels, and frequency-selective channels using different unequal error protection methods in combination. Hierarchical modulation is coupled with a packetization/combining strategy and an efficient channel encoder consisting of a cyclic redundancy check outer coder concatenated with an inner rate-compatible punctured convolutional coder. Distortion-optimal hierarchical parameters are jointly chosen with the set of channel coding parameters on a packet-switched wireless network with fixed length packets. A lower bound for the performance improvement of the proposed system is derived and shown to give significant gains at lower packet sizes and higher transmission rates. The proposed system is also shown to outperform several existing schemes for realistic wireless channels. [ABSTRACT FROM PUBLISHER]

A new forwarding strategy is proposed for the wireless diamond relay network under slow fading. The key feature is that it can adaptively switch between decode–forward and compress–forward according to the instantaneous received signal strength. It has four control parameters, which can be optimized by an alternating optimization procedure. Its outage performance is compared with two lower bounds. Analytically, it is proven to achieve the outage capacity to within 1 bit and within 50% for any signal-to-noise ratio (SNR). Empirically, it is shown to be nearly optimal for some fading scenarios. [ABSTRACT FROM PUBLISHER]