Search

Next-generation satellite systems require more flexibility in resource management such that available radio resources can be dynamically allocated to meet time-varying and non-uniform traffic demands. Considering potential benefits of beam hopping (BH) and non-orthogonal multiple access (NOMA), we exploit the time-domain flexibility in multi-beam satellite systems by optimizing BH design, and enhance the power-domain flexibility via NOMA. In this paper, we investigate the synergy and mutual influence of beam hopping and NOMA. We jointly optimize power allocation, beam scheduling, and terminal-timeslot assignment to minimize the gap between requested traffic demand and offered capacity. In the solution development, we formally prove the NP-hardness of the optimization problem. Next, we develop a bounding scheme to tightly gauge the global optimum and propose a suboptimal algorithm to enable efficient resource assignment. Numerical results demonstrate the benefits of combining NOMA and BH, and validate the superiority of the proposed BH-NOMA schemes over benchmarks. [ABSTRACT FROM AUTHOR]

Nowadays, Ambient Backscatter Communication (AmBC) systems have emerged as a green communication technology to enable massive self-sustainable wireless networks by leveraging Radio Frequency (RF) Energy Harvesting (EH) capability. A Full-duplex Ambient Backscatter Communication (FAmBC) network with a Full-duplex Access Point (AP), a dedicated Legacy User (LU), and several Backscatter Devices (BDs) is considered in this study. The AP with two antennas transfers downlink Orthogonal Frequency Division Multiplexing (OFDM) information and energy to the dedicated LU and several BDs, respectively, while receiving uplink backscattered information from BDs at the same time. One of the key aims in AmBC networks is to ensure fairness among BDs. To address this, we propose the Multi-objective Lexicographical Optimization Problem (MLOP), which aims to maximize the minimum BD’s throughput while enhancing overall BDs’ throughput, subject to the AP’s subcarrier power, BDs’ reflection coefficients, and backscatter time allocation. Owe to the MLOP is non-convex, we propose Difference Convex Algorithm (DCA) using Exterior Penalty Function Method (EPFM)—an inventive non-convex optimization method— to reach the optimal solution. The most critical advantage of applying this proposed approach is finding the globally optimal solution. The effectiveness of the proposed method supported by theoretical analysis confirms its superiority compared to some of the investigated suboptimal algorithms with the same computational complexity. [ABSTRACT FROM AUTHOR]

Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA) and Received Signal Strength (RSS) based. Since the Cramér-Rao lower bounds (CRLB) of these methods depend on the sensor geometry explicitly, sensor placement becomes a crucial issue in source localization applications. In this paper, we consider finding the optimal sensor placements for the TOA, TDOA, AOA and RSS based localization scenarios. We first unify the three localization models by a generalized problem formulation based on the CRLB-related metric. Then a unified optimization framework for optimal sensor placement (UTMOST) is developed through the combination of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) techniques. Unlike the majority of the state-of-the-art works, the proposed UTMOST neither approximates the design criterion nor considers only uncorrelated noise in the measurements. It can readily adapt to to different design criteria (i.e. A, D and E-optimality) with slight modifications within the framework and yield the optimal sensor placements correspondingly. Extensive numerical experiments are performed to exhibit the efficacy and flexibility of the proposed framework. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) is a key technology for improving the spectral and energy efficiency in 5G-and-beyond wireless networks. For a tractable analysis, most of the previous works on Massive MIMO have been focused on the system performance with complex Gaussian channel impulse responses under rich-scattering environments. In contrast, this paper investigates the uplink ergodic spectral efficiency (SE) of each user under the double scattering channel model. We derive a closed-form expression of the uplink ergodic SE by exploiting the maximum ratio (MR) combining technique based on imperfect channel state information. We further study the asymptotic SE behaviors as a function of the number of antennas at each base station (BS) and the number of scatterers available at each radio channel. We then formulate and solve a total energy optimization problem for the uplink data transmission that aims at simultaneously satisfying the required SEs from all the users with limited data power resource. Notably, our proposed algorithms can cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of the closed-form ergodic SE over Monte-Carlo simulations. Besides, the system can still provide the required SEs to many users even under congestion. [ABSTRACT FROM AUTHOR]

Multiuser techniques play a central role in the fifth-generation (5G) and beyond 5G (B5G) wireless networks that exploit spatial diversity to serve multiple users simultaneously in the same frequency resource. It is well known that a multi-antenna base station (BS) can efficiently serve a number of users not exceeding the number of antennas at the BS via precoding design. However, when there are more users than the number of antennas at the BS, conventional precoding design methods perform poorly because inter-user interference cannot be efficiently eliminated. In this paper, we investigate the performance of a highly-loaded multiuser system in which a BS simultaneously serves a number of users that is larger than the number of antennas. We propose a dynamic bandwidth allocation and precoding design framework and apply it to two important problems in multiuser systems: i) User fairness maximization and ii) Transmit power minimization, both subject to predefined quality of service (QoS) requirements. The premise of the proposed framework is to dynamically assign orthogonal frequency channels to different user groups and carefully design the precoding vectors within every user group. Since the formulated problems are non-convex, we propose two iterative algorithms based on successive convex approximations (SCA), whose convergence is theoretically guaranteed. Furthermore, we propose a low-complexity user grouping policy based on the singular value decomposition (SVD) to further improve the system performance. Finally, we demonstrate via numerical results that the proposed framework significantly outperforms existing designs in the literature. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) communication has emerged as a prominent technology for emergency communications (e.g., natural disaster) in the Internet of Things (IoT) networks to enhance the ability of disaster prediction, damage assessment, and rescue operations promptly. A UAV can be deployed as a flying base station (BS) to collect data from time-constrained IoT devices and then transfer it to a ground gateway (GW). In general, the latency constraint at IoT devices and UAV’s limited storage capacity highly hinder practical applications of UAV-assisted IoT networks. In this paper, full-duplex (FD) radio is adopted at the UAV to overcome these challenges. In addition, half-duplex (HD) scheme for UAV-based relaying is also considered to provide a comparative study between two modes (viz., FD and HD). Herein, a device is considered to be successfully served if its data is collected by the UAV and conveyed to GW timely during flight time. In this context, we aim to maximize the number of served IoT devices by jointly optimizing bandwidth, power allocation, and the UAV trajectory while satisfying each device’s requirement and the UAV’s limited storage capacity. The formulated optimization problem is troublesome to solve due to its non-convexity and combinatorial nature. Towards appealing applications, we first relax binary variables into continuous ones and transform the original problem into a more computationally tractable form. By leveraging inner approximation framework, we derive newly approximated functions for non-convex parts and then develop a simple yet efficient iterative algorithm for its solutions. Next, we attempt to maximize the total throughput subject to the number of served IoT devices. Finally, numerical results show that the proposed algorithms significantly outperform benchmark approaches in terms of the number of served IoT devices and system throughput. [ABSTRACT FROM AUTHOR]

The use of a large-scale antenna array (LSAA) has become an important characteristic of multi-antenna communication systems to achieve beamforming gains such as in designing millimeter-wave (mmWave) systems to combat severe propagation losses. In such applications, each antenna element has to be driven by a radio frequency (RF) chain for the implementation of fully-digital beamformers, significantly increasing the hardware cost, complexity, and power consumption. Therefore, constant-modulus analog beamforming (CMAB) becomes a viable solution. In this paper, we consider the scaled analog beamforming (SAB) or constant-modulus analog beamforming (CMAB) architecture and design the system parameters by solving two variants of beampattern matching problem. In the first case, both the magnitude and phase of the beampattern are matched to the given desired beampattern whereas in the second case, only the magnitude of the beampattern is matched. Both the beampattern matching problems are cast as a variant of the constant-modulus least-squares (CLS) problem. We provide efficient algorithms based on the alternating majorization-minimization (AMM) framework that combines the alternating minimization and the MM frameworks and the conventional-cyclic coordinate descent (C-CCD) algorithms to solve the problem in each case. We also propose algorithms based on a new modified-CCD (M-CCD) based approach. For all the developed algorithms we prove convergence to a Karush-Kuhn-Tucker (KKT) point (or a stationary point). Numerical results demonstrate that the proposed algorithms converge faster than the state-of-the-art solutions. Among all the algorithms, the M-CCD-based algorithms have faster convergence when evaluated in terms of the number of iterations and the AMM-based algorithms offer lower complexity. [ABSTRACT FROM AUTHOR]

In this paper, the problem of joint transmit waveform and receive filter design for dual-function radar-communication (DFRC) systems is studied. The considered system model involves a multiple antenna base station (BS) of a cellular system serving multiple single antenna users on the downlink. Furthermore, the BS simultaneously introduces sensing capabilities in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. A novel framework based on constrained optimization problems is proposed for the joint design of the transmit waveform and the radar receive filter such that different constraints related to the power amplifiers and the radar waveform are satisfied. In contrast to the existing approaches in the DFRC systems’ literature, the proposed approach does not require the knowledge of a predetermined radar beampattern in order to optimize the performance of the radar part through its approximation. Instead, a beampattern is generated by maximizing the radar receive signal-to-interference ratio (SINR) thus, enabling a more flexible design. Moreover, the radar receive filter processing and its optimization is considered for the first time on DFRC systems, enabling the effective exploitation of the available degrees of freedom in the radar receive array. Efficient algorithmic solutions with guaranteed convergence are developed for the defined constrained nonconvex optimization problems. The effectiveness of the proposed solutions is verified via numerical results. [ABSTRACT FROM AUTHOR]

This paper presents novel numerical approaches to finding the secrecy capacity of the multiple-input multiple-output (MIMO) wiretap channel subject to multiple linear transmit covariance constraints, including sum power constraint, per antenna power constraints and interference power constraint. An analytical solution to this problem is not known and existing numerical solutions suffer from slow convergence rate and/or high per-iteration complexity. Deriving computationally efficient solutions to the secrecy capacity problem is challenging since the secrecy rate is expressed as a difference of convex functions (DC) of the transmit covariance matrix, for which its convexity is only known for some special cases. In this paper we propose two low-complexity methods to compute the secrecy capacity along with a convex reformulation for degraded channels. In the first method we capitalize on the accelerated DC algorithm which requires solving a sequence of convex subproblems, for which we propose an efficient iterative algorithm where each iteration admits a closed-form solution. In the second method, we rely on the concave-convex equivalent reformulation of the secrecy capacity problem which allows us to derive the so-called partial best response algorithm to obtain an optimal solution. Notably, each iteration of the second method can also be done in closed form. The simulation results demonstrate a faster convergence rate of our methods compared to other known solutions. We carry out extensive numerical experiments to evaluate the impact of various parameters on the achieved secrecy capacity. [ABSTRACT FROM AUTHOR]

In this paper, we investigate hybrid beamforming, user scheduling, and resource allocation optimization based on spectrum coexisting forward transmission in integrated terrestrial-satellite network (ITSN) with the purpose of improving system sum rate and energy efficiency. Considering the limitation of on-board beamforming, a hybrid analog-digital beamforming scheme is designed under the scenario of millimeter wave (mmWave) coexisting in the ITSN framework. Besides, in order to further mitigate intra-beam and inter-beam interference, we propose an adaptive user scheduling scheme, which first determines the cluster center based on adaptive threshold, and then selects users with less channel correlation into a scheduling cluster. Moreover, we model system sum rate maximization problem that incorporates maximum power constrains and minimum data rate requirements. Combined with the aforementioned hybrid beamforming and user scheduling strategy, we formulate the sum rate maximizing problem to a pure power allocation issue. In view of the non-convexity and high complexity, we propose a feasible optimization method based on the minimum mean square error (MMSE) criterion and logarithmic linearization to optimize the power allocation for each user terminal (UT). Simulation results show that our proposed joint beamforming and resource allocation optimization scheduling scheme can achieve an attractive gain in system sum rate and energy efficiency compared with conservative beamforming and allocations. [ABSTRACT FROM AUTHOR]

Precoding has stood out as a promising multi-user multi-antenna transmission technique to meet the emerging throughput demand of satellite communication systems while awaiting the technological maturity for exploiting higher bands. Precoding enables the reduction of interference among co-channel beams while improving spectral efficiency. Satellite systems offer multitude of system and service configurations, resulting in different precoder design methodologies. This letter explores the motivation for the introduction of precoding, offers an insight to their theoretical development in diverse scenarios and presents some avenues for future development. [ABSTRACT FROM AUTHOR]

Next–generation of satellite communication (SatCom) networks are expected to support extremely high data rates for a seamless integration into future large satellite-terrestrial networks. In view of the coming spectral limitations, the main challenge is to reduce the cost (satellite launch and operation) per bit, which can be achieved by enhancing the spectral efficiencies. In addition, the capability to quickly and flexibly assign radio resources according to the traffic demand distribution has become a must for future multibeam broadband satellite systems. This letter presents the radio resource management problems encountered in the design of future broadband SatComs and provides a comprehensive overview of the available techniques to address such challenges. Firstly, we focus on the demand-matching formulation of the power and bandwidth assignment. Secondly, we present the scheduling design in practical multibeam satellite systems. Finally, a number of future challenges and the respective open research topics are described. [ABSTRACT FROM AUTHOR]

This letter tackles the problem of optimizing the parameters of a symbol-level precoder for downlink multi-antenna multi-user systems in the finite block length regime. Symbol-level precoding (SLP) is a non-linear technique for multi-user wireless networks, which exploits constructive interference among co-channel links. Current SLP designs, however, implicitly assume asymptotically infinite blocks, since they do not take into account that the design rules for finite and especially short blocks might significantly differ. This letter fills this gap by introducing a novel SLP design based on discrete constellation rotations. The rotations are the added degree of freedom that can be optimized for every block to be transmitted, for instance, to save transmit power. Numerical evaluations of the proposed method indicate substantial power savings, which might be over 99% compared to the traditional SLP, at the expense of a single additional pilot symbol per block for constellation de-rotation. [ABSTRACT FROM AUTHOR]

Cost-optimization through the minimization of hardware and processing costs with minimal loss in performance is an interesting design paradigm in evolving and emerging Multiple-Input-Multiple-Output (MIMO) radar systems. This optimization is a challenging task due to the increasing Radio Frequency (RF) hardware complexity as well as the signal design algorithm complexity in applications requiring high angular resolution. Towards addressing these, the paper proposes a low-complexity signal design framework, which incorporates a generalized time multiplex scheme for reducing the RF hardware complexity with a subsequent discrete phase modulation. The scheme further aims at achieving simultaneous transmit beamforming and maximum virtual MIMO aperture to enable better target detection and discrimination performance. Furthermore, the paper proposes a low-complexity signal design scheme for beampattern matching in the aforementioned setting. The conducted performance evaluation indicates that the listed design objectives are met. [ABSTRACT FROM AUTHOR]

This paper studies the joint design of user grouping, scheduling (or admission control) and precoding to optimize energy efficiency (EE) for multigroup multicast scenarios in single-cell multiuser MISO downlink channels. Noticing that the existing definition of EE fails to account for group sizes, a new metric called multicast energy efficiency (MEE) is proposed. In this context, the joint design is considered for the maximization of MEE, EE, and scheduled users. Firstly, with the help of binary variables (associated with grouping and scheduling) the joint design problem is formulated as a mixed-Boolean fractional programming problem such that it facilitates the joint update of grouping, scheduling and precoding variables. Further, several novel optimization formulations are proposed to reveal the hidden difference of convex/ concave structure in the objective and associated constraints. Thereafter, we propose a convex-concave procedure framework based iterative algorithm for each optimization criteria where grouping, scheduling, and precoding variables are updated jointly in each iteration. Finally, we compare the performance of the three design criteria concerning three performance metrics namely MEE, EE, and scheduled users through Monte-Carlo simulations. These simulations establish the need for MEE and the improvement from the system optimization. [ABSTRACT FROM AUTHOR]

In this paper, we design the UAV trajectory to minimize the total energy consumption while satisfying the requested timeout (RT) requirement and energy budget, which is accomplished via jointly optimizing the path and UAV's velocities along subsequent hops. The corresponding optimization problem is difficult to solve due to its non-convexity and combinatorial nature. To overcome this difficulty, we solve the original problem via two consecutive steps. Firstly, we propose two algorithms, namely heuristic search, and dynamic programming (DP) to obtain a feasible set of paths without violating the GU's RT requirements based on the traveling salesman problem with time window (TSPTW). Then, they are compared with exhaustive search and traveling salesman problem (TSP) used as reference methods. While the exhaustive algorithm achieves the best performance at a high computation cost, the heuristic algorithm exhibits poorer performance with low complexity. As a result, the DP is proposed as a practical trade-off between the exhaustive and heuristic algorithms. Specifically, the DP algorithm results in near-optimal performance at a much lower complexity. Secondly, for given feasible paths, we propose an energy minimization problem via a joint optimization of the UAV's velocities along subsequent hops. Finally, numerical results are presented to demonstrate the effectiveness of our proposed algorithms. The results show that the DP-based algorithm approaches the exhaustive search's performance with a significantly reduced complexity. It is also shown that the proposed solutions outperform the state-of-the-art benchmarks in terms of both energy consumption and outage performance. [ABSTRACT FROM AUTHOR]

In-band full-duplex (FD) operation is practically more suited for short-range communications such as WiFi and small-cell networks, due to its current practical limitations on the self-interference cancellation. In addition, cell-free massive multiple-input multiple-output (CF-mMIMO) is a new and scalable version of MIMO networks, which is designed to bring service antennas closer to end user equipments (UEs). To achieve higher spectral and energy efficiencies (SE-EE) of a wireless network, it is of practical interest to incorporate FD capability into CF-mMIMO systems to utilize their combined benefits. We formulate a novel and comprehensive optimization problem for the maximization of SE and EE in which power control, access point-UE (AP-UE) association and AP selection are jointly optimized under a realistic power consumption model, resulting in a difficult class of mixed-integer nonconvex programming. To tackle the binary nature of the formulated problem, we propose an efficient approach by exploiting a strong coupling between binary and continuous variables, leading to a more tractable problem. In this regard, two low-complexity transmission designs based on zero-forcing (ZF) are proposed. Combining tools from inner approximation framework and Dinkelbach method, we develop simple iterative algorithms with polynomial computational complexity in each iteration and strong theoretical performance guaranteed. Furthermore, towards a robust design for FD CF-mMIMO, a novel heap-based pilot assignment algorithm is proposed to mitigate effects of pilot contamination. Numerical results show that our proposed designs with realistic parameters significantly outperform the well-known approaches (i.e., small-cell and collocated mMIMO) in terms of the SE and EE. Notably, the proposed ZF designs require much less execution time than the simple maximum ratio transmission/combining. [ABSTRACT FROM AUTHOR]

Mobile edge caching (MEC) has received much attention as a promising technique to overcome the stringent latency and data hungry requirements in future generation wireless networks. Meanwhile, full-duplex (FD) transmission can potentially double the spectral efficiency by allowing a node to receive and transmit in the same time/frequency block simultaneously. In this paper, we investigate the delivery time performance of full-duplex enabled MEC (FD-MEC) systems, in which the users are served by distributed edge nodes (ENs), which operate in FD mode and are equipped with a limited storage memory. Firstly, we analyse the FD-MEC with different levels of cooperation among the ENs and take into account a realistic model of self-interference cancellation. Secondly, we propose a framework to minimize the system delivery time of FD-MEC under both linear and optimal precoding designs. Thirdly, to deal with the non-convexity of the formulated problems, two iterative optimization algorithms are proposed based on the inner approximation method, whose convergence is analytically guaranteed. Finally, the effectiveness of the proposed designs are demonstrated via extensive numerical results. It is shown that the cooperative scheme mitigates inter-user and self interference significantly better than the distributed scheme at an expense of inter-EN cooperation. In addition, we show that minimum mean square error (MMSE)-based precoding design achieves the best performance-complexity trade-off, compared with the zero-forcing and optimal designs. [ABSTRACT FROM AUTHOR]

In this paper, we investigate energy optimization in multi-cell and multi-carrier non-orthogonal multiple access (NOMA) networks. We apply a load-coupling model for NOMA networks to capture the coupling relation of mutual interference among cells. With this analytical tool, we formulate an energy minimization problem in a NOMA-based load-coupled system, where optimizing load-rate-power allocation, and determining decoding order and user grouping are the key aspects. Theoretically, we prove that the minimum consumed energy can be achieved by using all the time-frequency resources in each cell to deliver users’ demand, and allowing all the users to share resource units. From a practical perspective, we consider three types of NOMA grouping schemes, i.e., all-user grouping, partitioned and non-partitioned grouping. We develop tailored solutions for each grouping scheme to enable efficient load-rate-power optimization. These three algorithmic components are embedded into a power-adjustment framework to provide energy-efficient solutions for NOMA networks. Numerical results demonstrate promising energy-saving gains of NOMA over orthogonal multiple access in large-scale cellular networks, in particular for high-demand and resource-limited scenarios. The results also show fast convergence of the proposed algorithms and demonstrate the effectiveness of the solutions. [ABSTRACT FROM AUTHOR]

In this paper, we consider the (global and sum) energy efficiency optimization problem in downlink multi-input multi-output multi-cell systems, where all users suffer from multi-user interference. This is a challenging problem due to several reasons: First, it is a nonconvex fractional programming problem; second, the transmission rate functions are characterized by (complex-valued) transmit covariance matrices; and third the processing-related power consumption may depend on the transmission rate. We tackle this problem by the successive pseudoconvex approximation approach, and we argue that pseudoconvex optimization plays a fundamental role in designing novel iterative algorithms, not only because every locally optimal point of a pseudoconvex optimization problem is also globally optimal but also because a descent direction is easily obtained from every optimal point of a pseudoconvex optimization problem. The proposed algorithms have the following advantages: First, fast convergence as the structure of the original optimization problem is preserved as much as possible in the approximate problem solved in each iteration; second, easy implementation as each approximate problem is suitable for parallel computation and its solution has a closed-form expression; and third, guaranteed convergence to a stationary point or a Karush–Kuhn–Tucker point. The advantages of the proposed algorithm are also illustrated numerically. [ABSTRACT FROM AUTHOR]

We investigate the resource allocation and relay selection in a two-hop relay-assisted multi-user orthogonal frequency division multiple access (OFDMA) network, where the end-nodes support the simultaneous wireless information and power transfer (SWIPT) employing a power splitting (PS) technique. Our goal is to optimize the end-nodes’ PS ratios as well as the relay, carrier, and power assignment so that the sum-rate of the system is maximized subject to harvested energy and transmitted power constraints. Such joint optimization with mixed-integer non-linear programming structure is combinatorial in nature. Due to the complexity of this problem, we propose to solve its dual problem, which guarantees asymptotic optimality and less execution time compared to a highly-complex exhaustive search approach. Furthermore, we also present a heuristic method to solve this problem with lower computational complexity. The simulation results reveal that the proposed algorithms provide significant performance gains compared to a semi-random resource allocation and relay selection approach and is close to the optimal solution when the number of OFDMA sub-carriers is sufficiently large. [ABSTRACT FROM AUTHOR]

In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed framework is based on a nontrivial combination of the majorization–minimization framework and the successive convex approximation framework proposed in literature for a convex regularization function. The proposed framework has several attractive features, namely, first, flexibility, as different choices of the approximate function lead to different types of algorithms; second, fast convergence, as the problem structure can be better exploited by a proper choice of the approximate function and the stepsize is calculated by the line search; third, low complexity, as the approximate function is convex and the line search scheme is carried out over a differentiable function; fourth, guaranteed convergence to a stationary point. We demonstrate these features by two example applications in subspace learning, namely the network anomaly detection problem and the sparse subspace clustering problem. Customizing the proposed framework by adopting the best-response type approximation, we obtain soft-thresholding with exact line search algorithms for which all elements of the unknown parameter are updated in parallel according to closed-form expressions. The attractive features of the proposed algorithms are illustrated numerically. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the symbol-level precoding (SLP) design problem in the downlink of a multiuser multiple-input single-output channel. We consider generic two-dimensional constellations with any shape and size, and confine ourselves to one of the main categories of constructive interference regions (CIR), namely distance preserving CIR (DPCIR). We provide a comprehensive study of DPCIRs and derive several properties for these regions. Using these properties, we first show that any signal in a given DPCIR has a norm greater than or equal to the norm of the corresponding constellation point if and only if the convex hull of the constellation contains the origin. It is followed by proving that the power of the noise-free received signal in a DPCIR is a monotonic strictly increasing function of two parameters relating to the infinite Voronoi edges. Using the convex description of DPCIRs and their characteristics, we formulate two design problems, namely the SLP power minimization with signal-to-interference-plus-noise ratio (SINR) constraints, and the SLP SINR balancing problem under max–min fairness criterion. The SLP power minimization based on DPCIRs can straightforwardly be written as a quadratic programming. We derive a simplified reformulation of this problem, which is less computationally complex. The SLP max–min SINR, however, is non-convex in its original form, and hence difficult to tackle. We propose alternative optimization approaches, including semidefinite programming formulation and block coordinate descent optimization. We discuss and evaluate the loss due to the proposed alternative methods through extensive simulation results. [ABSTRACT FROM AUTHOR]

This paper studies the energy efficiency and sum rate tradeoff for coordinated beamforming in multicell multiuser multigroup multicast multiple-input single-output systems. We first consider a conventional network energy efficiency maximization (EEmax) problem by jointly optimizing the transmit beamformers and antennas selected to be used in transmission. We also account for per-antenna maximum power constraints to avoid nonlinear distortion in power amplifiers and user-specific minimum rate constraints to guarantee certain service levels and fairness. To be energy efficient, transmit antenna selection is employed. It eventually leads to a mixed-Boolean fractional program. We then propose two different approaches to solve this difficult problem. The first solution is based on a novel modeling technique that produces a tight continuous relaxation. The second approach is based on sparsity-inducing method, which does not require the introduction of any Boolean variable. We also investigate the tradeoff between the energy efficiency and sum rate by proposing two different formulations. In the first formulation, we propose a new metric, that is, the ratio of the sum rate and the so-called weighted power. Specifically, this metric reduces to EEmax when the weight is 1, and to sum rate maximization when the weight is 0. In the other method, we treat the tradeoff problem as a multiobjective optimization for which a scalarization approach is adopted. Numerical results illustrate significant achievable energy efficiency gains over the method where the antenna selection is not employed. The effect of antenna selection on the energy efficiency and sum rate tradeoff is also demonstrated. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of the interference between multiple co-channel transmissions in the downlink of a multi-antenna wireless system. In this framework, symbol-level precoding (SLP) is a promising technique which is able to constructively exploit the multi-user interference and to transform it into useful power at the receiver side. While previous works on SLP were focused on exploiting the multi-user interference, in this paper, we extend this concept by jointly handling the interference both in the spatial dimension (multi-user interference) and in the temporal dimension (inter-symbol interference). Accordingly, we propose a novel precoding method, referred to as spatio-temporal SLP. In this new precoding paradigm, faster-than-Nyquist (FTN) signaling can be applied over multi-user MISO systems, and the inter-symbol interference can be tackled at the transmitter side, without additional complexity for the user terminals. While applying FTN signaling, the proposed optimization strategies perform a sum power minimization with quality-of-service constraints. Numerical results are presented in a comparative fashion to show the effectiveness of the proposed techniques, which outperform the state-of-the-art SLP schemes in terms of symbol error rate, effective rate, and energy efficiency. [ABSTRACT FROM AUTHOR]

Optimizing non-orthogonal multiple access (NOMA) in multi-cell scenarios is much more challenging than the single-cell case because inter-cell interference must be considered. Most papers addressing NOMA consider a single cell. We take a significant step in analyzing NOMA in multi-cell scenarios. We explore the potential of NOMA networks in achieving optimal resource utilization with arbitrary topologies. Towards this goal, we investigate a broad class of problems consisting of optimizing power allocation and user pairing for any cost function that is monotonically increasing in time-frequency resource consumption. We propose an algorithm that achieves global optimality for this problem class. The basic idea is to prove that solving the joint optimization problem of power allocation, user pair selection, and time-frequency resource allocation amounts to solving a so-called iterated function without a closed form. We prove that the algorithm approaches optimality with fast convergence. Numerically, we evaluate and demonstrate the performance of NOMA for multi-cell scenarios in terms of resource efficiency and load balancing. [ABSTRACT FROM AUTHOR]

Edge-caching has received much attention as an efficient technique to reduce delivery latency and network congestion during peak-traffic times by bringing data closer to end users. Existing works usually design caching algorithms separately from physical layer design. In this paper, we analyze edge-caching wireless networks by taking into account the caching capability when designing the signal transmission. Particularly, we investigate multi-layer caching where both base station (BS) and users are capable of storing content data in their local cache and analyze the performance of edge-caching wireless networks under two notable uncoded and coded caching strategies. First, we calculate backhaul and access throughputs of the two caching strategies for arbitrary values of cache size. The required backhaul and access throughputs are derived as a function of the BS and user cache sizes. Second, closed-form expressions for the system energy efficiency (EE) corresponding to the two caching methods are derived. Based on the derived formulas, the system EE is maximized via precoding vectors design and optimization while satisfying a predefined user request rate. Third, two optimization problems are proposed to minimize the content delivery time for the two caching strategies. Finally, numerical results are presented to verify the effectiveness of the two caching methods. [ABSTRACT FROM PUBLISHER]

This paper considers coordinated multicast beamforming in a multicell multigroup multiple-input single-output system. Each base station (BS) serves multiple groups of users by forming a single beam with common information per group. We propose centralized and distributed beamforming algorithms for two different optimization targets. The first objective is to minimize the total transmission power of all the BSs while guaranteeing the user-specific minimum quality-of-service targets. The semidefinite relaxation (SDR) method is used to approximate the nonconvex multicast problem as a semidefinite program (SDP), which is solvable via centralized processing. Subsequently, two alternative distributed methods are proposed. The first approach turns the SDP into a two-level optimization via primal decomposition. At the higher level, intercell interference powers are optimized for fixed beamformers, whereas the lower level locally optimizes the beamformers by minimizing BS-specific transmit powers for the given intercell interference constraints. The second distributed solution is enabled via an alternating direction method of multipliers, where the intercell interference optimization is divided into a local and a global optimization by forcing the equality via consistency constraints. We further propose a centralized and a simple distributed beamforming design for the signal-to-interference-plus-noise ratio (SINR) balancing problem in which the minimum SINR among the users is maximized with given per-BS power constraints. This problem is solved via the bisection method as a series of SDP feasibility problems. The simulation results show the superiority of the proposed coordinated beamforming algorithms over traditional noncoordinated transmission schemes, and illustrate the fast convergence of the distributed methods. [ABSTRACT FROM PUBLISHER]

Constant envelope (CE) precoding can efficiently control the peak-to-average power ratio (PAPR) and improve the power efficiency of power amplifiers in large-scale antenna array systems. Antenna subset selection (ASS), combined with CE precoding, can further improve power efficiency by using a part of antennas to combine the desired signal. However, due to the inherent nonlinearity, the joint optimization of CE precoding and ASS is very challenging and satisfactory solutions are yet not available. In this paper, we present new methods for CE precoding and ASS optimization from a geometric perspective. First, we show the equivalence between the CE precoder design and a polygon construction problem in the complex plane, thus transforming the algebraic problem into a geometric problem. Aiming to minimize the computational complexity, we further transform the CE precoder design into a triangle construction problem, and propose a novel algorithm to achieve the optimal CE precoder with only linear complexity in the number of used antennas. Then, we investigate the joint optimization of ASS and CE precoding to minimize the total transmit power while satisfying the QoS requirement. Based on the geometric interpretation, we develop an efficient ASS algorithm, which, using only addition and comparison operations, is guaranteed to find the globally optimal solution and provides robustness to channel uncertainty. The complexity of the proposed ASS algorithm is at most quadratic in the number of antennas in the worst case. The optimality and superiority of the proposed geometric methods are demonstrated via numerical results. [ABSTRACT FROM PUBLISHER]

We consider the problem of sum-rate maximization in multiple-input multiple-output (MIMO) amplify-and-forward relay networks with multi-operator. The aim is to design the MIMO relay amplification matrix (i.e., the relay beamformer) to maximize the achievable communication sum rate through the relay. The design problem for the case of single-antenna users can be cast as a non-convex optimization problem, which, in general, belongs to a class of NP-hard problems. We devise a method based on the minorization–maximization technique to obtain quality solutions to the problem. Each iteration of the proposed method consists of solving a strictly convex unconstrained quadratic program. This task can be done quite efficiently, such that the suggested algorithm can handle the beamformer design for relays with up to $\sim 70$ antennas within a few minutes on an ordinary personal computer. Such a performance lays the ground for the proposed method to be employed in medium-scale (or lower regime massive) MIMO scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate joint beamforming and power allocation in multicell multiple-input single-output (MISO) downlink networks. Our goal is to maximize the utility function defined as the ratio between the system weighted sum rate and the total power consumption subject to the users’ quality of service requirements and per-base-station (BS) power constraints. The considered problem is nonconvex and its objective is in a fractional form. To circumvent this problem, we first resort to an virtual uplink formulations of the the primal problem by introducing an auxiliary variable and applying the uplink-downlink duality theory. By exploiting the analytic structure of the optimal beamformers in the dual uplink problem, an efficient algorithm is then developed to solve the considered problem. Furthermore, to reduce further the exchange overhead between coordinated BSs in a large-scale antenna system, an effective coordinated power allocation solution only based on statistical channel state information is reached by deriving the asymptotic optimization problem, which is used to obtain the power allocation in a long-term timescale. Numerical results validate the effectiveness of our proposed schemes and show that both the spectral efficiency and the energy efficiency can be simultaneously improved over traditional downlink coordinated schemes, especially in the middle-high transmit power region. [ABSTRACT FROM AUTHOR]

The present work focuses on the forward link of a broadband multibeam satellite system that aggressively reuses the user link frequency resources. Two fundamental practical challenges, namely the need to frame multiple users per transmission and the per-antenna transmit power limitations, are addressed. To this end, the so-called frame-based precoding problem is optimally solved using the principles of physical layer multicasting to multiple co-channel groups under per-antenna constraints. In this context, a novel optimization problem that aims at maximizing the system sum rate under individual power constraints is proposed. Added to that, the formulation is further extended to include availability constraints. As a result, the high gains of the sum rate optimal design are traded off to satisfy the stringent availability requirements of satellite systems. Moreover, the throughput maximization with a granular spectral efficiency versus SINR function, is formulated and solved. Finally, a multicast-aware user scheduling policy, based on the channel state information, is developed. Thus, substantial multiuser diversity gains are gleaned. Numerical results over a realistic simulation environment exhibit as much as 30% gains over conventional systems, even for 7 users per frame, without modifying the framing structure of legacy communication standards. [ABSTRACT FROM PUBLISHER]

Multibeam satellite systems offer flexibility that aims at efficiently reusing the available spectrum. To fully exploit the flexibility advantages, the payload resources—transmit power and bandwidth—must be efficiently allocated among multiple beams. This paper investigates the resource optimization problem in multibeam satellites. The NP-hardness and inapproximability of the problem are demonstrated motivating the use of metaheuristics. A systematic approach accomplishing the best traffic match is carried out. The additional requirement of minimizing the total power consumption is then considered, giving rise to a multi-objective optimization approach. The solutions to the a priori accomplished traffic matching optimization are used to enhance the efficiency of the multi-objective metaheuristic method proposed and, consequently, of the multibeam satellite system. The optimized performance is represented by the Pareto front, which provides trade-off points between total power consumption and rate achieved. This allows the decomposition of the problem into independent color-based sub-problems rendering the proposed two-stage optimization framework suitable for dimensioning the next generation multispot satellite systems. [ABSTRACT FROM PUBLISHER]

We consider performance optimization in the uplink of a multiuser multiantenna communication system. Each user multiplexes data onto several independently encoded data streams, which are spatially precoded and conveyed over a fading narrowband multiple-input multiple-output (MIMO) channel. All users' data streams are decoded successively at the receiving base station using zero-forcing decision feedback equalization (ZF-DFE). We target the joint optimization of a decoding order and linear precoders for all users based on long-term channel information. For a class of general MIMO channel models, including the separable-correlation and double-scattering models, we show that the choice of precoder for a certain user does not affect the performance of the others. This leads to a particularly straightforward characterization of general user utility regions as a polyblock, or a convex polytope if time-sharing is allowed. We formulate the decoding-ordering problem under transmit-correlated Rayleigh fading as a linear assignment problem, enabling the use of existing efficient algorithms. Combining decoding ordering with single-user precoder optimization by means of alternating optimization, we propose an efficient iterative scheme that is verified numerically to converge fast and perform close to optimally, successfully reaping the benefits of both precoding and ordering in the MIMO uplink. [ABSTRACT FROM PUBLISHER]

We consider a multiuser multiple-input single-output interference channel where the receivers are characterized by both quality-of-service (QoS) and radio-frequency (RF) energy harvesting (EH) constraints. We consider the power splitting RF-EH technique where each receiver divides the received signal into two parts a) for information decoding and b) for battery charging. The minimum required power that supports both the QoS and the RF-EH constraints is formulated as an optimization problem that incorporates the transmitted power and the beamforming design at each transmitter as well as the power splitting ratio at each receiver. We consider both the cases of fixed beamforming and when the beamforming design is incorporated into the optimization problem. For fixed beamforming we study three standard beamforming schemes, the zero-forcing (ZF), the regularized zero-forcing (RZF) and the maximum ratio transmission (MRT); a hybrid scheme, MRT-ZF, comprised of a linear combination of MRT and ZF beamforming is also examined. The optimal solution for ZF beamforming is derived in closed-form, while optimization algorithms based on second-order cone programming are developed for MRT, RZF and MRT-ZF beamforming to solve the problem. In addition, the joint-optimization of beamforming and power allocation is studied using semidefinite programming (SDP) with the aid of rank relaxation. [ABSTRACT FROM PUBLISHER]

We consider the beamformer design for multiple-input multiple-output (MISO) broadcast channels (MISO BCs) using zero-forcing dirty paper coding (ZF-DPC). Assuming a sum power constraint (SPC), most previously proposed beamformer designs are based on the QR decomposition (QRD), which is a natural choice to satisfy the ZF constraints. However, the optimality of the QRD-based design for ZF-DPC has remained unknown. In this paper, first, we analytically establish that the QRD-based design is indeed optimal for any performance measure under a SPC. Then, we propose an optimal beamformer design method for ZF-DPC with per-antenna power constraints (PAPCs), using a convex optimization framework. The beamformer design is first formulated as a rank-1-constrained optimization problem. Exploiting the special structure of the ZF-DPC scheme, we prove that the rank constraint can be relaxed and still provide the same solution. In addition, we propose a fast converging algorithm to the beamformer design problem, under the duality framework between the BCs and multiple access channels (MACs). More specifically, we show that a BC with ZF-DPC has the dual MAC with ZF-based successive interference cancellation (ZF-SIC). In this way, the beamformer design for ZF-DPC is transformed into a power allocation problem for ZF-SIC, which can be solved more efficiently. [ABSTRACT FROM PUBLISHER]

This paper addresses coordinated downlink beamforming optimization in multicell time division duplex (TDD) systems where a small number of parameters are exchanged between cells but with no data sharing. With the goal to reach the point on the Pareto boundary with max-min rate fairness, we first develop a two-step centralized optimization algorithm to design the joint beamforming vectors. This algorithm can achieve a further sum-rate improvement over the max-min optimal performance, and is shown to guarantee max-min Pareto optimality for scenarios with two base stations (BSs) each serving a single user. To realize a distributed solution with limited intercell communication, we then propose an iterative algorithm by exploiting an approximate uplink-downlink duality, in which only a small number of positive scalars are shared between cells in each iteration. Simulation results show that the proposed distributed solution achieves a fairness rate performance close to the centralized algorithm while it has a better sum-rate performance, and demonstrates a better tradeoff between sum-rate and fairness than the Nash Bargaining solution especially at high signal-to-noise ratio. [ABSTRACT FROM PUBLISHER]

Multibeam satellite systems have been employed to provide interactive broadband services to geographical areas under-served by terrestrial infrastructure. In this context, this paper studies joint multiuser linear precoding design in the forward link of fixed multibeam satellite systems. We provide a generic optimization framework for linear precoding design to handle any objective functions of data rate with general linear and nonlinear power constraints. To achieve this, an iterative algorithm which optimizes the precoding vectors and power allocation alternatingly is proposed and most importantly, the proposed algorithm is proved to always converge. The proposed optimization algorithm is also applicable to nonlinear dirty paper coding. As a special case, a more efficient algorithm is devised to find the optimal solution to the problem of maximizing the proportional fairness among served users. In addition, the aforementioned problems and algorithms are extended to the case that each terminal has multiple co-polarization or dual-polarization antennas. Simulation results demonstrate substantial performance improvement of the proposed schemes over conventional multibeam satellite systems, zero-forcing and regularized zero-forcing precoding schemes in terms of meeting the traffic demand, e.g., using real beam patterns, over twice higher throughput can be achieved compared with the conventional scheme. The performance of the proposed linear precoding scheme is also shown to be very close to the dirty paper coding. [ABSTRACT FROM PUBLISHER]