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This paper considers the downlink of an ultra-reliable low-latency communication (URLLC) system in which a base station (BS) serves multiple single-antenna users in the short (finite) blocklength (FBL) regime with the assistance of a reconfigurable intelligent surface (RIS). In the FBL regime, the users' achievable rates are complex functions of the beamforming vectors and of the RIS's programmable reflecting elements (PREs). We propose the joint design of the transmit beamformers and PREs to maximize the geometric mean (GM) of these rates (GM-rate) and show that this approach provides fair rate distribution and thus reliable links to all users. A novel computational algorithm is developed, which is based on closed forms to generate improved feasible points. Simulations show the merit of our solution. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has been regarded as a promising and revolutionary technology for future wireless communication systems owing to its capability of tailoring signal propagation environment in an energy/spectrum/ hardware-efficient manner. However, most existing studies on IRS optimizations are based on a simple and ideal reflection model that is impractical in hardware implementation, which thus leads to severe performance loss in realistic wideband/multi-band systems. To deal with this problem, in this paper we first propose a more practical and more tractable IRS reflection model that describes the difference of reflection responses for signals at different frequencies. Then, we investigate the joint transmit beamforming and IRS reflection beamforming design for an IRS-assisted multi-cell multi-band system. Both power minimization and sum-rate maximization problems are solved by exploiting popular second-order cone programming (SOCP), Riemannian manifold, minimization-majorization (MM), weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results illustrate the significant performance improvement of our proposed joint transmit beamforming and reflection design algorithms based on the practical reflection model in terms of power saving and rate enhancement. [ABSTRACT FROM AUTHOR]

Caching popular contents at edge networks (such as small-cell base stations) has been proposed to deal with the ever-growing mobile traffic. At the meantime, recommendation system is able to shape user demands for further prompting caching gain. In this paper, we study a multi-antenna multi-cell edge network employing transmit beamforming with caching-aware recommendation and user association. We first establish a framework for the joint problem of beamforming, user association, content caching and recommendation to minimize the content transmission delay of mobile users, by specifying a set of necessary conditions for all four component functions of the network. The resulting optimization problem corresponds to a non-convex, multi-timescale, and mixed-integer programming problem, which is hard to handle. To deal with the difficulty in solving the joint optimization problem by the direct formulation, we equivalently decompose it into three sub-problems. Then, we develop a computationally-efficient iterative algorithm to obtain the sub-optimal solution, where the three subproblems are tackled iteratively. Simulation results are conducted to demonstrate that the proposed algorithm can obtain lower transmission delay than baseline schemes. [ABSTRACT FROM AUTHOR]

As the number of antennas increases, the massive multiple-input multiple-output (MIMO) system can precisely point to user equipments (UEs) with narrow beams. Accurate and timely channel state information (CSI) feedback is crucial to keep UEs in service. Mobile UEs, however, may suffer from the narrow beam nature of the massive MIMO system since UEs can move out of the beam coverage. When intelligent reflecting surface (IRS) is applied to the massive MIMO system, the adjustment of the IRS cannot be frequent as the IRS is controlled remotely by the base station (BS). Limiting the number of CSI feedback and the number of both BS and IRS adjustments significantly reduces the overhead of transmission and computation to the system. In this paper, we consider the UEs’ mobility adaptation problem in an IRS assisted multiuser massive MIMO downlink system with infrequent CSI feedback. We propose a beam control method that adapts to UEs’ mobility. The problem is constructed as a sum rate problem where both the BS and IRS are taken into account to jointly optimize the beamforming matrices. Simulation results show that our proposed algorithm can converge quickly and provide satisfactory performance for mobile UEs. At the same time, our proposed algorithm reduces the frequency of updating the beamforming matrices effectively both at the BS and at the IRS. [ABSTRACT FROM AUTHOR]

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]

Intelligent reflecting surface (IRS) is deemed as a promising solution to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we consider a wireless network where multiple base stations (BSs) serve their respective users with the aid of distributed IRSs in the downlink communication. Specifically, each IRS assists in the transmission from its associated BS to user via passive beamforming, while in the meantime, it also randomly scatters the signals from other co-channel BSs, thus resulting in additional signal as well as interference paths in the network. As such, a new IRS-user/BS association problem arises pertaining to optimally balance the passive beamforming gains from all IRSs among different BS-user communication links. To address this new problem, we first derive a tractable lower bound of the average signal-to-interference-plus-noise ratio (SINR) at the receiver of each user, termed average-signal-to-average-interference-plus-noise ratio (ASAINR), based on which two ASAINR balancing problems are formulated to maximize the minimum ASAINR among all users by optimizing the IRS-user associations without and with BS transmit power control, respectively. We also characterize the scaling behavior of user ASAINRs with the increasing number of IRS reflecting elements to investigate the different effects of IRS-reflected signal versus interference power. Moreover, to solve the two ASAINR balancing problems that are both non-convex optimization problems, we propose an optimal solution to the problem without BS power control and low-complexity suboptimal solutions to both problems by applying the branch-and-bound method and exploiting new properties of the IRS-user associations, respectively. Numerical results verify our performance analysis and also demonstrate significant performance gains of the proposed solutions over benchmark schemes. [ABSTRACT FROM AUTHOR]

This paper considers an intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmitter. The proposed design framework exploits the cost-effective IRS to establish favorable communication environment to improve the fair energy efficient. In particular, we study the max-min energy efficiency (EE) of the system by jointly designing the transmit information and energy beamforming at the base station (BS), phase shifts at the IRS, as well as the power splitting (PS) ratio at all users subject to the minimum rate, minimum harvested energy, and transmit power constraints. The formulated problem is non-convex and thus challenging to be solved. We propose two algorithms namely penalty-based and inner approximation (IA)-based to handle the non-convexity of the optimization problem. As such, we divide the original problem into two sub-problems and apply the alternating optimization (AO) algorithm for both proposed algorithms to handle it iteratively. In particular, in the penalty-based algorithm for the first sub-problem, the semi-definite relaxation (SDR) technique, difference of convex functions (DC) programming, majorization-minimization (MM) approach, and fractional programming theory are exploited to transform the non-convex optimization problem into a convex form that can be addressed efficiently. For the second sub-problem, a penalty-based approach is proposed to handle the optimization on the phase shifts introduced by the IRS with the proposed algorithms. For the IA-based method, we jointly optimize beamforming vectors and phase shifts while the PS ratio is solved optimally in the first sub-problem. Simulation results verify the effectiveness of the IRS, which can significantly improve the system EE as compared to conventional benchmark schemes and also unveil a trade-off between convergence and performance gain for the two proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we focus on intelligent reflecting surface (IRS) assisted multi-antenna communications with transceiver hardware impairments encountered in practice. In particular, we aim to maximize the received signal-to-noise ratio (SNR) taking into account the impact of hardware impairments, where the source transmit beamforming and the IRS reflect beamforming are jointly designed under the proposed optimization framework. To circumvent the non-convexity of the formulated design problem, we first derive a closed-form optimal solution to the source transmit beamforming. Then, for the optimization of IRS reflect beamforming, we obtain an upper bound to the optimal objective value via solving a single convex problem. A low-complexity minorization-maximization (MM) algorithm was developed to approach the upper bound. Simulation results demonstrate that the proposed beamforming design is more robust to the hardware impairments than that of the conventional SNR maximized scheme. Moreover, compared to the scenario without deploying an IRS, the performance gain brought by incorporating the hardware impairments is more evident for the IRS-aided communications. [ABSTRACT FROM AUTHOR]

Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency. In this paper, we focus on an IRS-assisted NOMA network and propose an energy-efficient algorithm to yield a good tradeoff between the sum-rate maximization and total power consumption minimization. We aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming at the BS and the reflecting beamforming at the IRS. Specifically, the transmit beamforming and the phases of the low-cost passive elements on the IRS are alternatively optimized until the convergence. Simulation results demonstrate that the proposed algorithm in IRS-NOMA can yield superior performance compared with the conventional OMA-IRS and NOMA with a random phase IRS. [ABSTRACT FROM AUTHOR]

Fractional programming (FP) refers to a family of optimization problems that involve ratio term(s). This two-part paper explores the use of FP in the design and optimization of communication systems. Part I of this paper focuses on FP theory and on solving continuous problems. The main theoretical contribution is a novel quadratic transform technique for tackling the multiple-ratio concave–convex FP problem—in contrast to conventional FP techniques that mostly can only deal with the single-ratio or the max-min-ratio case. Multiple-ratio FP problems are important for the optimization of communication networks, because system-level design often involves multiple signal-to-interference-plus-noise ratio terms. This paper considers the applications of FP to solving continuous problems in communication system design, particularly for power control, beamforming, and energy efficiency maximization. These application cases illustrate that the proposed quadratic transform can greatly facilitate the optimization involving ratios by recasting the original nonconvex problem as a sequence of convex problems. This FP-based problem reformulation gives rise to an efficient iterative optimization algorithm with provable convergence to a stationary point. The paper further demonstrates close connections between the proposed FP approach and other well-known algorithms in the literature, such as the fixed-point iteration and the weighted minimum mean-square-error beamforming. The optimization of discrete problems is discussed in Part II of this paper. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) that enables the control of wireless propagation environment has recently emerged as a promising cost-effective technology for boosting the spectral and energy efficiency of future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming full signal reflection by each of its elements regardless of the phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper a practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection design. Based on the proposed model and considering an IRS-aided multiuser system with one IRS deployed to assist in the downlink communications from a multi-antenna access point (AP) to multiple single-antenna users, we formulate an optimization problem to minimize the total transmit power at the AP by jointly designing the AP transmit beamforming and the IRS reflect beamforming, subject to the users’ individual signal-to-interference-plus-noise ratio (SINR) constraints. Iterative algorithms are proposed to find suboptimal solutions to this problem efficiently by utilizing the alternating optimization (AO) as well as penalty-based optimization techniques. Moreover, to draw essential insight, we analyze the asymptotic performance loss of the IRS-aided system that employs practical phase shifters but assumes the ideal phase shift model for beamforming optimization, as the number of IRS elements goes to infinity. Simulation results unveil substantial performance gains achieved by the proposed beamforming optimization based on the practical phase shift model as compared to the conventional ideal model. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of downlink (DL) sum rate maximization in codebook based multiuser (MU) multiple input multiple output (MIMO) systems. The user equipments (UEs) estimate the DL channels using pilot symbols sent by the access point (AP) and feedback the estimates to the AP over a control channel. We present a closed form expression for the achievable sum rate of the MU-MIMO broadcast system with codebook constrained precoding based on the estimated channels, where multiple data streams are simultaneously transmitted to all users. Next, we present novel, computationally efficient, minorization-maximization (MM) based algorithms to determine the selection of beamforming vectors and power allocation to each beam that maximizes the achievable sum rate. Our solution involves multiple uses of MM in a nested fashion. Based on this approach, we propose and contrast two algorithms, which we call the square-root-MM (SMM) and inverse-MM (IMM) algorithms. The algorithms are iterative and converge to a locally optimal beamforming vector selection and power allocation solution from any initialization. We evaluate the performance and complexity of the algorithms for various values of the system parameters, compare them with existing solutions, and provide further insights into how they can be used in system design. [ABSTRACT FROM AUTHOR]

Traditional beamforming (BF) methods can be used to suppress the Gaussian interferers in underwater acoustic communication systems. However, multiple unsteady sub-Gaussian interferers may exist that degrade the performance of BF. In this paper, a new beamformer is proposed for the suppression of multiple unsteady sub-Gaussian interferers (SMUSGI). The proposed BF combines the multiplier and the quasi-Newton methods to obtain the optimal weight vector. Simulation experiments are conducted to validate the performance of the proposed beamformer. In addition, the proposed beamformer is compared with the traditional methods including the MVDR, Subspace, Bartlett, MDDR, LCMD and LCMV beamformers. The obtained results demonstrate that the proposed beamformer can effectively suppress multiple unsteady sub-Gaussian interferers with a higher output signal-to-interference-plus-noise ratio (SINR), and faster convergence compared with the traditional methods. [ABSTRACT FROM AUTHOR]

The emergence of interference alignment (IA) as a degrees-of-freedom optimal strategy motivates the need to investigate whether IA can be leveraged to aid conventional network utility maximization algorithms, which are typically only capable of finding locally optimal solutions. To test the usefulness of IA in this context, this paper proposes a two-stage optimization framework for the downlink of a $G$ -cell multi-antenna network with $K$ users/cell. The first stage of the proposed framework focuses on nulling interference from a set of dominant interferers using IA, while the second stage optimizes transmit and receive beamformers to maximize a network-wide utility using the IA solution as the initial condition. Further, this paper establishes a set of new feasibility results for partial IA that can be used to guide the number of dominant interferers to be nulled in the first stage. Through simulations on specific topologies of a cluster of base stations, it is observed that the impact of IA depends on the choice of the utility function and the presence of out-of-cluster interference. In the absence of out-of-cluster interference, the proposed framework outperforms straightforward optimization when maximizing the minimum rate, while providing marginal gains when maximizing sum rate. However, the benefit of IA is greatly diminished in the presence of significant out-of-cluster interference. [ABSTRACT FROM AUTHOR]

This paper studies joint source and relay beamforming for a wireless powered downlink multirelay multiuser network. Considering nonlinear energy harvesting and imperfect channel state information, we aim to minimize the total transmit power at the base station by jointly optimizing the source beamforming and the relay beamforming weights under the energy causality constraints at the relays and the signal-to-noise ratio constraints at the users. The formulated problem is highly nonconvex, and thus, it is difficult to solve. To solve the problem, we first transform it into a worst-case optimization, and then, an iterative algorithm is developed to solve this worst-case optimization. Numerical results show the advantage of the proposed robust scheme. [ABSTRACT FROM AUTHOR]

This paper investigates a new beamforming approach for interference exploitation, which has recently attracted interest as an alternative to conventional interference-avoidance beamforming for the downlink of multiple-input multiple-output systems. Contrary to existing interference exploitation approaches that focus on signal-to-noise ratio performance, we adopt an approach based on the detection region of the signal constellation. Focusing on quality of service, we then formulate the optimization for minimizing the error probability (EP) for the worst user, subject to power constraints. We do this by employing the knowledge of channel state information at the transmitter, along with all downlink users’ data that are readily available at the base station during downlink transmission. In this context, we also show that the detection-region-based beamforming and the worst user EP downlink beamforming are equivalent problems. Finally, we further propose a sum EPs approach and provide an analytic bound of average symbol error rate performance. Our simulations verify that the proposed techniques provide significantly improved performance over conventional downlink beamforming techniques. [ABSTRACT FROM AUTHOR]

Impulsive processes frequently occur in many fields, such as radar, sonar, communications, audio and speech processing, and biomedical engineering. In this paper, we propose a nonconvex linear regression (NLR) based minimum dispersion beamforming technique for impulsive signals to achieve significant performance improvement over the conventional minimum variance beamformer. The proposed beamformer minimizes the \ellp-norm of the output with p<1 subject to a linear distortionless response constraint, resulting in a difficult nonconvex and nonsmooth optimization problem. The constrained optimization problem is first reduced to a multivariate linear regression via constraint elimination. As a major contribution of this paper, a coordinate descent algorithm (CDA) is devised for solving the resultant NLR problem of \ellp-minimization with p<1 at a computational complexity of \cal O(MN^2), where M is the number of sensors and N is the sample size. At each inner iteration of the CDA, an efficient algorithm is designed to find the global minimum of each subproblem of univariate linear regression. The convergence of the CDA is analyzed. The NLR beamformer with a single constraint is further generalized to the case of multiple linear constraints, which is robust against model mismatch. Simulation results demonstrate the superior performance of nonconvex optimization based beamformer. [ABSTRACT FROM AUTHOR]

We investigate the performance of multiuser multiple-input-single-output (MU-MISO) downlink in the presence of the mutual coupling effect at the transmitter. Contrary to traditional approaches that aim at eliminating this effect, in this paper we propose a joint analog–digital (AD) beamforming scheme that exploits this effect to further improve the system performance. A jointly optimal AD beamformer is first obtained by iteratively maximizing the minimum received signal-to-interference-plus-noise ratio in the digital domain, followed by an optimization on the load impedance of each antenna element in the analog domain. We further introduce a decoupled low-complexity approach, with which existing closed-form beamforming schemes can also be efficiently applied. For the consideration of hardware imperfections in practice, we study the case where the analog load values are quantized, and propose a sequential search scheme based on greedy algorithm to efficiently obtain the desired quantized load values. Moreover, we also investigate the imperfect channel state information (CSI) scenarios, where we prove the optimality for closed-form beamformers, and further propose the robust schemes for two typical CSI error models. Simulation results show that with the proposed schemes the mutual coupling effect can be exploited to further improve the performance for both perfect CSI and imperfect CSI. [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]

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]

In this paper, we investigate a max–min weighted signal-to-interference-plus-noise-ratio (SINR) problem in a full-duplex multiuser multiple-input-multiple-output system, where a full-duplex-capable base station (BS) equipped with multiple antennas communicates with multiple half-duplex downlink and uplink users under the same system resources. Specifically, we consider a practical scenario where the downlink minimum weighted SINR is maximized under specific SINR constraints for uplink users. Moreover, the optimization is conducted by jointly considering the BS transmit power, the transmit power of uplink users, and BS transmit and receive beamforming. This optimization problem is, therefore, subject to multiple uplink SINR constraints and multiple transmit power constraints. Due to the SINR constraints, negative matrix components arise and hence the optimization problem cannot be directly solved by the standard approach, i.e., Perron–Frobenius theory. We have provided an explicit iterative scheme to solve this joint optimization problem with a strict proof. The proposed algorithm is proved to converge to Karush–Kuhn–Tucker points. Simulation results show that our proposed algorithm has a fast convergence rate and leads to a better performance compared with other optimization techniques that do not jointly consider all parameters. [ABSTRACT FROM PUBLISHER]

A full-duplex (FD) two-way relay channel (TWRC) with multiple antennas is considered. For this three-node network, the beamforming design needs to suppress self-interference (SI). While a traditional way is to apply zero forcing (ZF) for SI mitigation, it may harm the desired signals. In this paper, a design that reserves a fraction of SI is proposed by solving a quality-of-service (QoS) constrained beamforming design problem. Since the problem is challenging due to the loop SI, a convergence-guaranteed alternating optimization (AO) algorithm is proposed to jointly design the relay–user beamformers. Numerical results show that the proposed scheme outperforms ZF method and achieves a transmit power value close to the ideal case. [ABSTRACT FROM PUBLISHER]

In this paper, we present a joint antenna beamforming and power allocation technique to maximize the multiuser sum rate in an underlying microcellular system which reuses the same spectrum of a macrocellular system. One challenge in this kind of hierarchical cognitive radio (HCR) systems is to manage the interference between the macrocell and the microcell. The key contribution of this paper is to develop an optimization technique for antenna beamforming that can maximize the achievable sum rate of the underlying cognitive radio (CR) microcellular system and control the interference between the macrocell and the microcell with a satisfaction level. The proposed technique optimizes the sum rate performance by maximizing its lower bound and transfers the original non-convex problem into a convex optimization problem by introducing auxiliary variables to confine the intra-user interference power among the secondary system. Next, an iterative sum rate maximization (ISM) algorithm is developed to find the beamforming weights and the allocated power for each secondary user to simultaneously maximize system sum rate, coverage, and concurrent multiuser transmission probability in the HCR system. The developed joint design methodology provides valuable insights into the design of an optimal HCR system for various numbers of users as well as cell coverage, and can quantitatively optimize the performance tradeoffs in the hierarchical multiuser CR systems for current and future wireless communication applications. [ABSTRACT FROM PUBLISHER]

The cellular communications scenario involving the coexistence of the one-hop direct transmission and the two-hop relaying is studied in this paper. In contrast to conventional cellular systems featuring orthogonal information flows served by decoupled channel resources via, e.g., time division, we propose a novel protocol in which two information flows are served simultaneously via the shared channel resource, thus constituting a spectral-efficient solution for cellular communications. On the other hand, however, an inevitable issue associated with the proposed protocol is the interflow interference, which may lead to serious deterioration on both information flows. To tackle this issue, we develop an overhearing-based protocol that utilizes the overheard interference as useful side information in the receiver design. Specifically, depending on the interference levels, an adaptive linear minimum mean squared error (MMSE) and nonlinear MMSE successive interference cancellation (SIC) receiver exploiting the overheard interference at the direct mobile terminal is developed. To balance between the two information flows, we develop the asymptotically optimum superposition coding at the base station (BS) in the high-power regime. Furthermore, the optimum relay beamforming matrix maximizing the bottleneck of the achievable rates of the two information flows is developed subject to a finite power constraint. Finally, simulations demonstrate a remarkable throughput gain over the conventional cellular systems. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal design of limited-feedback downlink multiuser spatial multiplexing systems. A multiple-antenna base-station is assumed to serve multiple single-antenna users, who quantize and feed back their channel state information (CSI) through a shared rate-limited feedback channel. The optimization problem is cast in the form of minimizing the average transmission power at the base-station subject to users' target signal-to-interference-plus-noise ratios (SINR) and outage probability constraints. The goal is to derive the feedback bit allocations among the users and the corresponding channel magnitude and direction quantization codebooks in a high-resolution quantization regime. Toward this end, this paper develops an optimization framework using approximate analytical closed-form solutions, the accuracy of which is then verified by numerical results. The results show that, for channels in the real space, the number of channel direction quantization bits should be (M-1) times the number of channel magnitude quantization bits, where M is the number of base-station antennas. Moreover, users with higher requested quality-of-service (QoS), i.e., lower target outage probabilities, and higher requested downlink rates, i.e., higher target SINR's, should use larger shares of the feedback rate. It is also shown that, for the target QoS parameters to be feasible, the total feedback bandwidth should scale logarithmically with the geometric mean of the target SINR values and the geometric mean of the inverse target outage probabilities. In particular, the minimum required feedback rate is shown to increase if the users' target parameters deviate from the corresponding geometric means. Finally, the paper shows that, as the total number of feedback bits B increases, the performance of the limited-feedback system approaches the perfect-CSI system as 2-B/M2. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the game theoretical approach in designing the multiuser downlink beamformers in multicell systems. Sharing the same physical resource, the base-station of each cell wishes to minimize its transmit power subject to a set of target signal-to-interference-plus-noise ratios (SINRs) at the multiple users in the cell. In this context, at first, the paper considers a strategic noncooperative game (SNG) where each base-station greedily determines its optimal downlink beamformer strategy in a distributed manner, without any coordination between the cells. Via the game theory framework, it is shown that this game belongs to the framework of standard functions. The conditions guaranteeing the existence and uniqueness of a Nash Equilibrium (NE) in this competitive design are subsequently examined. The paper then makes a revisit to the fully coordinated design in multicell downlink beamforming, where the optimal beamformers are jointly designed between the base-stations. A comparison between the competitive and coordinated designs shows the benefits of applying the former over the latter in terms of each design's distributed implementation. Finally, in order to improve the efficiency of the NE in the competitive design, the paper considers a more cooperative game through a pricing mechanism. The pricing consideration enables a base-station to steer its beamformers in a more cooperative manner, which ultimately limits the interference induced to other cells. The study on the existence and uniqueness of the new game's NE is then given. The paper also presents a condition on the pricing factors that allow the new NE point to approach the performance established by the coordinated design, while retaining the distributed nature of the multicell game. [ABSTRACT FROM PUBLISHER]

This paper studies the design and optimization of a limited feedback single-user system with multiple-antenna transmitter and single-antenna receiver. The design problem is cast in form of the minimizing the average transmission power at the base station subject to the user's outage probability constraint. The optimization is over the user's channel quantization codebook and the transmission power control function at the base station. Our approach is based on fixing the outage scenarios in advance and transforming the design problem into a robust system design problem. We start by showing that uniformly quantizing the channel magnitude in dB scale is asymptotically optimal, regardless of the magnitude distribution function. We derive the optimal uniform (in dB) channel magnitude codebook and combine it with a spatially uniform channel direction codebook to arrive at a product channel quantization codebook. We then optimize such a product structure in the asymptotic regime of B \ura \infty, where B is the total number of quantization feedback bits. The paper shows that for channels in the real space, the asymptotically optimal number of direction quantization bits should be (M-1)/2 times the number of magnitude quantization bits, where M is the number of base station antennas. We also show that the performance of the designed system approaches the performance of the perfect channel state information system as 2^- 2B/(M+1). For complex channels, the number of magnitude and direction quantization bits are related by a factor of (M-1) and the system performance scales as 2^- B/M as B \ura \infty. [ABSTRACT FROM AUTHOR]

This paper considers the problem of optimizing the downlink capacity in a heterogeneous home area network (HAN) with a beamforming technique at the smart meter for smart grid applications. We model the communication scenario between the smart meter and in-home appliances as a heterogeneous multiuser network. The optimal power-allocation algorithm is developed under the constraints that 1) each user satisfies individual signal-to-interference-plus-noise ratio (SINR) requirement for successful heterogeneous communication, 2) the sum of transmit power allocated to each user is equal to the permissible total transmit power at the smart meter, and 3) the allocated transmit power to each user is feasible. The optimization problem is mathematically shown to be convex, and the optimal power allocation is thus derived. By employing the theorem that the sum of independent exponentially distributed random variables follows Erlang distribution, the probability distribution function (pdf) of the smallest allocated transmit power is mathematically obtained from the properties of downlink indoor Saleh–Valenzuela (S–V) channels. It is analytically shown that the allocated transmit power has a lower limit, which is determined by the SINR threshold and the total number of active users in the HAN. Furthermore, numerical results verify the capacity performance improvements of the proposed optimal power-allocation scheme. It is also shown that beamforming technique contributes to the optimal power-allocation scheme. [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]

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]

This paper focuses on linear beamforming design and power allocation strategy for ergodic rate optimization in a two-user Multiple-Input Single-Output (MISO) system with statistical and delayed channel state information at the transmitter (CSIT). We propose a transmission strategy, denoted as Statistical Alternative MAT (SAMAT), which exploits both channel statistics and delayed CSIT. Firstly, with statistical CSIT only, we focus on statistical beamforming (SBF) design that maximizes a lower bound on the ergodic sum-rate. Secondly, relying on both statistical and delayed CSIT, an iterative algorithm is proposed to compute the precoding vectors of Alternative MAT (AMAT), originally proposed by Yang et al., which maximizes an approximation of the ergodic sum-rate with equal power allocation. Finally, via proper power allocation, the SAMAT framework is proposed to softly bridge between SBF and AMAT for an arbitrary number of transmit antennas and signal-to-noise ratio (SNR). A necessary condition for the power allocation optimization is identified from the Karush-Kuhn-Tucker (KKT) conditions. The optimum power allocation to maximize an ergodic sum-rate approximation is computed using Sequential Quadratic Programming (SQP). Simulation results show that the proposed SAMAT scheme yields a significant sum-rate enhancement over both SBF and AMAT. [ABSTRACT FROM PUBLISHER]

The newly emerging wireless powered communication networks (WPCNs) have recently drawn significant attention, where radio signals are used to power wireless terminals for information transmission. In this paper, we study a WPCN where one multi-antenna access point (AP) coordinates energy transfer and information transfer to/from a set of single-antenna users. A harvest-then-transmit protocol is assumed where the AP first broadcasts wireless power to all users via energy beamforming in the downlink (DL), and then, the users send their independent information to the AP simultaneously in the uplink (UL) using their harvested energy. To optimize the users' throughput and yet guarantee their rate fairness, we maximize the minimum throughput among all users by a joint design of the DL–UL time allocation, the DL energy beamforming, and the UL transmit power allocation, as well as receive beamforming. We solve this nonconvex problem optimally by two steps. First, we fix the DL–UL time allocation and obtain the optimal DL energy beamforming, UL power allocation, and receive beamforming to maximize the minimum signal-to-interference-plus-noise ratio of all users. This problem is shown to be still nonconvex; however, we convert it equivalently to a spectral radius minimization problem, which can be solved efficiently by applying the alternating optimization based on the nonnegative matrix theory. Then, the optimal time allocation is found by a one-dimensional search to maximize the minimum rate of all users. Furthermore, two suboptimal designs of lower complexity are also proposed, and their throughput performance is compared against that of the optimal solution. [ABSTRACT FROM AUTHOR]

This paper focuses on multicell coordinated beamforming in the presence of channel state information (CSI) errors, where base stations (BSs) collaboratively mitigate their intercell interference (ICI). Assuming that the CSI errors are hyper-spherically bounded, we consider an optimization problem that minimizes the overall transmission power of BSs subject to signal-to-interference-plus-noise-ratio (SINR) constraints at each mobile station (MS). We solve this problem in a distributed manner with a limited information exchange among BSs. Using semidefinite relaxation (SDR) and the S-Lemma, we first reformulate our optimization problem into a numerically tractable one. Since the SINR constraints are coupled, we introduce an algorithm by which each BS can obtain a local version of its coupling variables via a small data exchange with other BSs. Then, we propose an iterative algorithm that employs the projected gradient method to coordinate ICI across multiple BSs. Finally, we extend the application of the proposed algorithm to solve the problem of robust and distributed per-user SINR maximization under per-BS power constraints. Simulation results confirm the effectiveness of the proposed algorithm in terms of power efficiency and convergence in the presence of CSI uncertainties. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the performance of an analog network coding (ANC)-based two-way relay system that employs beamforming at the multi-antenna sources. Specifically, we analyze the overall system performance by deriving an exact expression for the overall symbol error rate (OSER) and an upper bound expression for the ergodic sum-rate (ESR) over independent and non-identically distributed Nakagami- $m$ fading channels. We also derive closed-form representations of these expressions in the asymptotic high signal-to-noise ratio (SNR) regime to provide useful insights into the system behavior, and the optimal power allocation and relay location. Moreover, we address the joint optimization problem of power allocation and relay location to minimize the OSER and to maximize the ESR. Our results highlight the influence of key system/channel parameters on the overall system performance. [ABSTRACT FROM PUBLISHER]

In this paper, system design for a multiple-input–multiple-output (MIMO) relay broadcast channel with individual signal-to-interference-plus-noise ratio (SINR) constraints at mobile stations (MSs) is considered. By exploring the structure of downlink–uplink duality at either the base station (BS) or the relay station (RS), we propose two schemes of joint power allocation and beamforming design at the BS and the RS. The problem of the existence of feasible solutions under practical power constraints at the BS and the RS with given SINR targets is considered first. Then, the problem of sum power minimization is considered. Each design problem can be efficiently solved using optimal joint power allocation and beamforming under the framework of convex optimization. We also show that with subchannel pairing at the RS, the transmission power can be further reduced. Finally, a generalization to the multihop scenario is provided to further improve power efficiency. [ABSTRACT FROM PUBLISHER]

This paper considers a conditional decode-and-forward (DF) based cooperative system where a source (S) with multiple (M) antennas transmits information to a single-antenna destination (D) with the help of multiple (L ≤ M-1) single-antenna relays (R_i). The optimal transmit weighting vector at the source is not available in the literature due to the non-linear conditional DF operation, which renders the problem non-convex. To solve this problem, we first show that the optimal transmit vector for the single-relay system can be determined by comparing the S-D beamformer (maximum-ratio-transmit beamforming vector for the S-D link) with the one that utilizes "just sufficient" energy to activate the relay-link. However, it is difficult to directly apply the above idea to the multi-relay system, due to the fact that the S-R and S-D links are normally not orthogonal. To tackle this issue, we propose to utilize basis functions that are orthogonal to the S-D and S-R links, respectively, which enables the activating of one S-R link without considering the S-D link and the other S-R links. We then apply the new basis functions to the multi-relay system and propose a selective relay-activation algorithm, where the optimal solution is obtained by comparing the S-D beamformer with schemes that selectively activate different combinations of the relay-links. The selective relay-activation algorithm is different from the conventional water-filling in the sense that the energy is filled to discrete levels to activate the S-R links, a unique feature arising from the conditional DF operation. [ABSTRACT FROM PUBLISHER]

This paper considers a downlink beamforming problem in a cognitive radio network where multiple primary and secondary cells coexist. Each multiantenna primary and secondary transmitter serves its own set of single antenna users. The optimization objective is to minimize the sum transmission power over secondary transmitters while guaranteeing the minimum SINR for each secondary user and satisfying the maximum aggregate interference power constraint for each primary user. We propose a decentralized beamforming algorithm where the original centralized problem is decomposed via primal decomposition method into two levels, i.e., transmitter-level subproblems managed by a network-level master problem. The master problem is solved independently at each secondary transmitter using a projected subgradient method requiring limited backhaul signaling among secondary transmitters. To solve the independent transmitter-level subproblems, we propose three alternative approaches which are based on second order cone programming, semidefinite programming and uplink–downlink duality. Special emphasis is put on the last approach, which is also considered in a multi-cell MISO cellular network. Numerical results show that the proposed algorithm achieves close to optimal solution even after a few iterations in quasi-static channel conditions. Moreover, near centralized performance is demonstrated in time-correlated channels. [ABSTRACT FROM AUTHOR]

Beamforming design for multi-user wireless relay networks under the criterion of maximin information throughput is an important but also very hard optimization problem due to its nonconvex nature. The existing approach to reformulate the design as a matrix rank-one constrained optimization problem is highly inefficient. This paper exploits the d.c. difference of two convex functions) structure of the objective function and the convex structure of the constraints in such a global optimization problem to develop efficient iterative algorithms of very low complexity to find the solutions. Both cases of concurrent and orthogonal transmissions from sources to relays are considered. Numerical results indicate that the proposed algorithms provide solutions that are very close to the upper bound on the solution of the non-orthogonal source transmissions case and are almost equal to the optimal solution of the orthogonal source transmissions case. This demonstrates the ability of the developed algorithms to locate approximations close to the global optimal solutions in a few iterations. Moreover, the proposed methods are superior to other methods in both performance and computation complexity. [ABSTRACT FROM AUTHOR]

This paper studies the structure of downlink sum-rate maximizing selective decentralized feedback policies for opportunistic beamforming under finite feedback constraints on the average number of mobile users feeding back. First, it is shown that any sum-rate maximizing selective decentralized feedback policy must be a threshold feedback policy. This result holds for all fading channel models with continuous distribution functions. Second, the resulting optimum threshold selection problem is analyzed in detail. This is a nonconvex optimization problem over finite-dimensional Euclidean spaces. By utilizing the theory of majorization, an underlying Schur-concave structure in the sum-rate function is identified, and the sufficient conditions for the optimality of homogenous threshold feedback policies are obtained. Applications of these results are illustrated for well-known fading channel models such as Rayleigh, Nakagami, and Rician fading channels. Rather surprisingly, it is shown that using the same threshold value at all mobile users is not always a rate-wise optimal feedback strategy, even for a network in which mobile users experience statistically the same channel conditions. For the Rayleigh fading channel model, on the other hand, homogenous threshold feedback policies are proven to be rate-wise optimal if multiple orthonormal data carrying beams are used to communicate with multiple mobile users simultaneously. [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]

In this paper, we consider transmit beamforming (BF) design under per-antenna power constraint (PPC) for multiple-input–single-output (MISO) frequency-selective channels. Both cyclic-prefixed (CP) single carriers and orthogonal frequency-division-multiplexing (OFDM) systems are investigated. Since there is no closed-form expression for the optimal PPC BF coefficients, and the optimization tools employed to find the PPC solution are usually complicated, here, we propose three computationally more effective suboptimal solutions to minimize the arithmetic mean of the effective error probabilities. Moreover, we address the issue of limited-rate feedback, and a simple codebook design method for frequency-selective channels is proposed. Unlike conventional methods where the codebook is directly designed for BF vectors, here, we construct codebooks for the amplitude and phase of the time-domain channel state information (CSI) to reduce the rate of feedback. Further, the nonuniform feedback strategy is investigated. Simulation results show that the proposed suboptimal solutions have much lower complexities than the convex optimization-based PPC solutions. The effectiveness of the proposed uniform and nonuniform limited-rate feedback methods is also demonstrated via simulations. [ABSTRACT FROM PUBLISHER]

This paper studies the max-min weighted signal-to-interference-plus-noise ratio (SINR) problem in the multiple- input-multiple-output (MIMO) downlink, where multiple users are weighted according to priority and are subject to a weighted-sum-power constraint. First, we study the multiple-input-single-output (MISO) and single-input-multiple- output (SIMO) problems using nonlinear Perron–Frobenius theory. As a by-product, we solve the open problem of convergence for a previously proposed MISO algorithm by Wiesel, Eldar, and Shamai in 2006. Furthermore, we unify our analysis with respect to the previous alternate optimization algorithm proposed by Tan, Chiang, and Srikant in 2009, by showing that our MISO result can, in fact, be derived from their algorithm. Next, we combine our MISO and SIMO results into an algorithm for the MIMO problem. We show that our proposed algorithm is optimal when the channels are rank-one, or when the network is operating in the low signal-to-noise ratio (SNR) region. Finally, we prove the parametric continuity of the MIMO problem in the power constraint, and we use this insight to propose a heuristic initialization strategy for improving the performance of our (generally) suboptimal MIMO algorithm. The proposed initialization strategy exhibits improved performance over random initialization. [ABSTRACT FROM PUBLISHER]

Based on worst-case performance optimization, the recently developed adaptive beamformers utilize the uncertainty set of the desired array steering vector to achieve robustness against steering vector mismatches. In the presence of large steering vector mismatches, the uncertainty set has to expand to accommodate the increased error. This degrades the output signal-to-interference-plus-noise ratios (SINRs) of these beamformers since their interference-plus-noise suppression abilities are weakened. In this paper, an iterative robust minimum variance beamformer (IRMVB) is proposed which uses a small uncertainty sphere (and a small flat ellipsoid) to search for the desired array steering vector iteratively. This preserves the interference-plus-noise suppression ability of the proposed beamformer and results in a higher output SINR. Theoretical analysis and simulation results are presented to show the effectiveness of the proposed beamformer. [ABSTRACT FROM PUBLISHER]

Secure transmission techniques have been receiving growing attention in recent years, as a viable, powerful alternative to blocking eavesdropping attempts in an open wireless medium. This paper proposes a secret transmit beamforming approach using a quality-of-service (QoS)-based perspective. Specifically, we establish design formulations that: i) constrain the maximum allowable signal-to-interference-and-noise ratios (SINRs) of the eavesdroppers, and that ii) provide the intended receiver with a satisfactory SINR through either a guaranteed SINR constraint or SINR maximization. The proposed designs incorporate a relatively new idea called artificial noise (AN), where a suitable amount of AN is added in the transmitted signal to confuse the eavesdroppers. Our designs advocate joint optimization of the transmit weights and AN spatial distribution in accordance with the channel state information (CSI) of the intended receiver and eavesdroppers. Our formulated design problems are shown to be NP-hard in general. We deal with this difficulty by using semidefinite relaxation (SDR), an approximation technique based on convex optimization. Interestingly, we prove that SDR can exactly solve the design problems for a practically representative class of problem instances; e.g., when the intended receiver's instantaneous CSI is known. Extensions to the colluding-eavesdropper scenario and the multi-intended-receiver scenario are also examined. Extensive simulation results illustrate that the proposed AN-aided designs can yield significant power savings or SINR enhancement compared to some other methods. [ABSTRACT FROM AUTHOR]

This paper studies distributed optimization schemes for multicell joint beamforming and power allocation in time-division-duplex (TDD) multicell downlink systems where only limited-capacity intercell information exchange is permitted. With an aim to maximize the worst-user signal-to-interference-and-noise ratio (SINR), we devise a hierarchical iterative algorithm to optimize downlink beamforming and intercell power allocation jointly in a distributed manner. The proposed scheme is proved to converge to the global optimum. For fast convergence and to reduce the burden of intercell parameter exchange, we further propose to exploit previous iterations adaptively. Results illustrate that the proposed scheme can achieve near-optimal performance even with a few iterations, hence providing a good tradeoff between performance and backhaul consumption. The performance under quantized parameter exchange is also examined. [ABSTRACT FROM AUTHOR]

In this paper, we examine single user and multiuser multiple-input multiple-output (MIMO) beamforming networks with channel distribution information (CDI). Since CDI changes infrequently compared to channel state information (CSI), algorithms based on CDI can achieve significant savings in feedback compared to algorithms based on CSI. With CDI, we can only guarantee quality of service for a specified outage probability in the network. Assuming correlated Rayleigh fading on all the links, we derive a closed-form expression for the outage probability. Then, using this expression, we derive algorithms for joint transmit/receive beamforming and power control to minimize the weighted sum power in the network while guaranteeing these outage probabilities. For both single-user and multiuser MIMO scenarios, we present optimal algorithms under the Kronecker model assumption, and we present near-optimal algorithms assuming general correlation structures on the links. We then show that using these algorithms based on CDI, if we are willing to accept given outages on the links, we can achieve comparable power usage in the network relative to algorithms based on CSI. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multisite radar system (MSRS) with multiple-input and multiple-output (MIMO) radars, i.e., MIMO-MSRS system, to improve the detection performance of fluctuating targets. The proposed MIMO-MSRS system increases the local signal-to-noise ratio (SNR) by using digital beamforming (DBF) among all transmitting and receiving channels in a single site. Then it smoothes the target's fluctuation via spatial diversity among the DBF outputs of different sites. For the MIMO-MSRS system, we derive the likelihood ratio test (LRT) detector at first based on the proposed signal model and spatial diversity conditions. Furthermore, with the derived statistics of the LRT detector in the fixed noise background, three optimization problems are discussed on the MIMO-MSRS system configurations, i.e., the numbers of sites and collocated channels in different sites. The first problem is to detect the lowest SNR target with a given probability of false alarm (P_F), probability of detection (P_D) and total system degrees of freedom (DOF). The second is to detect a target with the highest P_D for a given P_F, target SNR, and system DOF. The third is on the minimal system DOF to detect a target with a given P_F, P_D, and target SNR. For the uniform MIMO-MSRS system, both the standard optimal site number, i.e., the diversity DOF, and its closed-form approximation of the above three problems are obtained. Finally, some numerical results are also provided to demonstrate the effectiveness of the proposed MIMO-MSRS systems. [ABSTRACT FROM AUTHOR]

In this paper, we consider beamforming (BF) for cooperative networks with one multi—antenna source, multiple multi—antenna amplify—and—forward (AF) relays, and one single—antenna destination. The source BF vector and the AF—BF matrices at the relays are optimized for maximization of the signal—to—noise ratio (SNR) at the destination under three different power constraints. In particular, we consider individual relay power constraints, a joint relay power constraint, and a joint power constraint for the source and the relays. We solve the associated optimization problems in two stages. In the first stage, we find the optimal AF—BF matrices for a given BF vector at the source. For the cases of individual and joint relay power constraints, closed—form solutions for the AF—BF matrices are provided, respectively. Furthermore, for the case of a joint source—relay power constraint, the AF—BF matrices are obtained in closed form up to a scalar power allocation factor and an efficient numerical algorithm for the power allocation between the source and the relays is given. In the second stage, the optimal source BF vectors are computed. Thereby, we show that for the joint relay and the joint source—relay power constraints, the resulting problem can be transformed into a non—convex polynomial programming problem which allows for an exact solution for small—scale networks. For large—scale networks and networks with individual relay power constraints, we propose efficient suboptimal optimization methods for the source BF vector. Simulation results show the benefits of having multiple antennas at the source and/or multiple multi—antenna relays and illustrate the performance differences introduced by the three different power constraints. [ABSTRACT FROM AUTHOR]

Cooperative communications have received considerable attention for wireless multimedia applications as a technique to improve reliability and service coverage. To apply such cooperation over slow fading channels, we consider exploiting the spatial diversity in multiple relay networks. In this paper, we focus on amplify-and-forward relaying schemes, namely, orthogonal amplify-and-forward, selective relaying, and distributed beamforming, for the cooperative wireless multimedia transmission. Furthermore, the successive refinement source coding is exploited such that the multimedia signal is encoded into multiple layers and the quality of its reconstruction at the receiver is improved when more layers are received correctly. We study two strategies for the layered source transmission, namely, progressive transmission and superposition coding. With our developed framework, we propose suboptimal resource allocation algorithms to efficiently assign rate, power, and channel uses to different layers so as to maximize the quality of the multimedia signal reconstructed at the receiver. The proposed optimization methodology is simple and only requires the knowledge of the channel statistics compared to the existing algorithms. As a result, the receiver only requires to feedback the determined rate, power, and channel uses to the transmitter whenever the channel statistics or the layered source transmission strategy changes. [ABSTRACT FROM PUBLISHER]