This paper investigates signaling by a base station equipped with a dynamic metasurface antenna (DMA) to support the transmission of multi-stream information to multiple near-field users. We consider the joint design of the low-resolution DMA elements’ frequency responses and the baseband precoder to ensure the quality-of-service (QoS) for all users in terms of their rates. First, we develop convex quadratic solver-based iterations of cubic complexity to address the computationally challenging max-min rate optimization problem involving nonsmooth large-scale mixed discrete-continuous optimization. We then opt for the soft max-min rate optimization problem, which involves smooth mixed discrete-continuous optimization, and develop closed-form expression-based iterations of scalable complexity for its computation. The latter approach is not only computationally efficient but also achieves both a high minimum user rate and sum-rate, thereby guaranteeing QoS and high overall network throughput.

This work explores signal transmission for serving multiple communication users (CUs) while simultaneously estimating multiple targets. In this context, the multi-stream signals intended for downlink CUs equipped with multiple antennas are also employed as probing signals for target estimation. We consider precoding design to ensure quality of service, quantified by the CUs’ individual rates and the mean squared error in target estimation. We develop path-following computational procedures that generate a sequence of improved feasible points by iterating closed-form expressions, ensuring convergence. As a byproduct, a computational solution for estimating the targets’ response vectors or their reflection coefficients and angles manifests, addressing long-standing open problems in estimation theory. Computational experiments not only demonstrate their consistency but also reveal that the obtained precoders produce highly directionally selective beampatterns toward the targets, even though this is not a primary objective.

This work investigates the integration of multi-target inference and multiuser communication in an active reconfigurable intelligent surface (aRIS)-assisted network. To infer the targets’ elevation and azimuth pairs and reflection coefficients from signals transmitted by a base station and reflected by the aRIS, which form computationally intractable nonlinear models, we develop a constructive minimum mean square error (MMSE) estimator based on their probability distribution functions. The resulting MSE is expressed analytically as a deterministic function of the probing signal, enabling its optimization. We then formulate the problem of jointly designing a beamformer and the aRISs power-amplified reconfigurable elements to ensure both accurate target inference and fair user rates. A computational program using closed-form updates is developed. Numerical results demonstrate a flexible trade-off between inference accuracy and achieved user rates.

We present the first investigation into the transmission of multi-stream information from a base station equipped with reconfigurable holographic surfaces (RHS) to multiple users with the aid of multi-antenna arrays. Building upon this, we propose the joint design of RHS and baseband beamformers that enables multi-stream delivery at fair rates across all users. Specifically, we first introduce a max-min rate optimization approach, which aims for maximizing the minimum rate for all users through iterative solutions of quadratic problems. To reduce complexity, we then propose a surrogate-based optimization approach that offers a low-complexity design alternative relying on closed-form updates. Our simulations show that the surrogate-based approach achieves nearly the same minimum rate as max-min optimization, while delivering sum-rates comparable to those of sum-rate maximization, overcoming the rate-fairness deficiency typical of the latter.

This paper investigates an integrated sensing and communication (ISAC) network operating over millimeter-wave and sub-Terahertz bands, where a base station serves downlink communication users (CUs), while simultaneously sensing targets. First, we propose a novel hybrid beamforming structure that reduces power consumption in high-frequency bands by using a low number of phase shifters for analog beamforming and enhances spatial diversity in baseband beamforming through improper Gaussian signaling (IGS), addressing the limitations of having only a few radio frequency chains by boosting the number of supported data streams. Together, these techniques establish a new phased-MIMO radar structure and an energy-efficient signaling strategy designed for joint sensing and communication. Second, we formulate a new beampattern-optimization objective that enables computationally efficient algorithms, which iteratively update the hybrid beamformers through closed-form expressions. This design ensures tight mainlobe concentration for sensing while simultaneously serving multiple CUs. A new soft-min function, paired with a closed-form algorithm, secures both strong worst-rate and sum-rate performance. By unifying sensing and communication objectives, the proposed framework offers a well-balanced trade-off between high CU rates and high-quality sensing beampatterns, while maintaining computational complexity scalable. Simulation results validate the practicality of the proposed approach.

This paper investigates the downlink of an internet-of-things (IoT) network with a base station serving multiple IoT devices (IoTDs) with the assistance of two far-apart reconfigurable intelligent surfaces (RISs). We propose joint design of the BS’s beamformer and RISs’ quantized programmable reflecting elements (PREs). Considering the IoTDs’ minimum rate (MR) as the primary optimization objective, we further aim to optimize the multi-objective function of both the MR and sum rate (SR) in the Pareto-optimal sense. We develop convex-solver and closed-form algorithms. Simulations demonstrate that the latter, with scalable complexity, performs as well as the former, which exhibits polynomially increasing complexity. Furthermore, the simulations reveal the advantages of the double-RIS assisted solution over its single-RIS assisted counterpart of the same size.

This is the first investigation focused on delivering multi-stream information to multiple multi-antenna users employing an active reconfigurable intelligent surface (aRIS)-assisted system. We conceive the joint design of the transmit precoders and of the aRIS’s power-amplified reconfigurable elements (APRES) to enhance the log-det rate objective functions for all users, which poses large-scale mixed discrete continuous problems. We develop a max-min log-det solver, which iterates quadratic-solvers of cubic complexity to maximize the nonsmooth function representing the minimum of the users’ log-det rate functions. To mitigate the computational burden associated with cubically escalating complexity in large-scale scenarios, we introduce a pair of alternative problems aimed at maximizing the smooth functions representing the sum of the users’ log-det rate function (sum log-det) and the soft minimum of the users’ log-det rate function (soft min log-det). We develop sum log-det and soft max-min solvers, leveraging closed-form expressions of scalable (linear) complexity for efficient computation. This approach ensures practicality in addressing large-scale scenarios. Furthermore, the soft min log-det enables us to enhance the log-det rates for all users and their sum, ultimately improving the quality of delivering multi-user multi-stream information.

Scalable algorithms are conceived for obtaining the sum-rate capacity of the reconfigurable intelligent surface (RIS)-aided multiuser (MU) multiple-input multiple-output (MIMO) broadcast channel (BC), where a multi-antenna base station (BS) transmits signals to multi-antenna users with the help of an RIS equipped with a massive number of finite-resolution programmable reflecting elements (PREs). As a byproduct, scalable path-following algorithms emerge for determining the sum-rate capacity of the conventional MIMO BCs, closing a long-standing open problem of information theory. The paper also develops scalable algorithms for maximizing the minimum rate (max-min rate optimization) of the users achieved by the joint design of RIS’s PRE and transmit beamforming for such an RIS-aided BC. The simulations provided confirm the high performance achieved by the algorithms developed, despite their low computational complexity.

This is the first treatise on multi-user (MU) beamforming designed for achieving long-term rate-fairness in full-dimensional MU massive multi-input multi-output (m-MIMO) systems. Explicitly, based on the channel covariances, which can be assumed to be known beforehand, we address this problem by optimizing the following objective functions: the users’ signal-to-leakage-noise ratios (SLNRs) using SLNR max-min optimization, geometric mean of SLNRs (GM-SLNR) based optimization, and SLNR soft max-min optimization. We develop a convex-solver based algorithm, which invokes a convex subproblem of cubic time-complexity at each iteration for solving the SLNR max-min problem. We then develop closed-form expression based algorithms of scalable complexity for the solution of the GM-SLNR and of the SLNR soft max-min problem. The simulations provided confirm the users’ improved-fairness ergodic rate distributions.

A wireless network serving multiple users in the millimeter-wave or the sub-terahertz band by a base station is considered. High-throughput multi-user hybrid-transmit beamforming is conceived by maximizing the minimum rate of the users. For the sake of energy-efficient signal transmission, the array-of-subarrays structure is used for analog beamforming relying on low-resolution phase shifters. We develop a convex-solver based algorithm, which iteratively invokes a convex problem of the same beamformer size for its solution. We then introduce the soft max-min rate objective function and develop a scalable algorithm for its optimization. Our simulation results demonstrate the striking fact that soft max-min rate optimization not only approaches the minimum user rate obtained by max-min rate optimization but it also achieves a sum rate similar to that of sum-rate maximization. Thus, the soft max-min rate optimization based beamforming design conceived offers a new technique of simultaneously achieving a high individual quality-of-service for all users and a high total network throughput.

A network relying on a large antenna-array-aided base station is designed for delivering multiple information streams to multi-antenna users over high-frequency bands such as the millimeter-wave and sub-Terahertz bands. The state-of-the-art analog precoder (AP) dissipates excessive circuit power due to its reliance on a large number of phase shifters. To mitigate the power consumption, we propose a novel AP relying on a controlled number of phase shifters. Within this new AP framework, we design a hybrid precoder (HP) for maximizing the users’ minimum throughput, which poses a computationally challenging problem of large-scale, nonsmooth mixed discrete-continuous log-determinant optimization. To tackle this challenge, we develop an algorithm which iterates through solving convex problems to generate a sequence of HPs that converges to the max-min solution. We also introduce a new framework of smooth optimization termed soft max-min throughput optimization. Additionally, we develop another algorithm, which iterates by evaluating closed-form expressions to generate a sequence of HPs that converges to the soft max-min solution. Simulation results reveal that the HP soft max-min solution approaches the Pareto-optimal solution constructed for simultaneously optimizing both the minimum throughput and sum-throughput. Explicitly, it achieves a minimum throughput similar to directly maximizing the users’ minimum throughput and it also attains a sum-throughput similar to directly maximizing the sum-throughput.

The physical-layer security (PLS) of a space–ground communication system is examined. To improve the security performance, a pair of reconfigurable intelligent surfaces (RISs) is integrated into the system and benchmarked against a scheme, where there is only a single RIS close to the ground station. As for the double-RIS scenario, we formulate a secrecy rate maximization problem, and then propose an alternating optimization (AO) algorithm for jointly optimizing three vectors, namely, the beamformer of the ground station and the reflecting vectors of two different RISs. Similarly, as for the single-RIS case, we also propose another AO algorithm for optimizing a pair of vectors, namely, the beamformer of the ground station and the reflecting vector of the single RIS. Both the double-RIS and the single-RIS AO algorithms are developed on the basis of the first-order Taylor expansion and Dinkelbach’s method, which allow us to approximate nonconvex optimization problems by convex ones. Our results demonstrate that the proposed double-RIS scheme outperforms the single-RIS benchmark scheme in terms of its security.

This paper focuses on a network scenario where a multi-antenna access point serves multiple single-antenna users in the presence of multiple eavesdroppers, with the aid of a reconfigurable intelligent surface (RIS). The RIS employs low-resolution programmable reflecting elements (PREs) for cost-effective implementation. In order to establish secure links for all users, we consider the joint design of the transmit beamformers and PREs to maximize either the geometric mean of secrecy rates or the worst user’s secrecy rate. Novel computational algorithms of low computational complexity are developed for the solution of these mixed discrete continuous optimization problems. Simulations show the merit of the proposed designs in in achieving fair secrecy rate distributions and ensuring secure links for all users.

This paper considers hybrid beamforming consisting of analog beamforming (ABF) coupled with digital baseband beamforming (DBF) which is designed for multi-user (MU) multiple input multiple output (MIMO) millimeter-wave (mmWave) communications. ABF uses a limited number of radio frequency (RF) chains and finite-resolution phase-shifters to alleviate the power consumption at the base station (BS), while DBF uses either zero-forcing beamforming (ZFB) or regularized zero forcing beamforming (RZFB) to restrain MU interference. The joint design of ABF and DBF constitutes a computationally challenging mixed discrete continuous optimization problem. The paper develops efficient algorithms for its solution, which iterate scalable-complex expressions. Furthermore, we conceive a new class of MU RZFB for attaining higher rates. Simulations are provided to demonstrate the viability of the proposed algorithms and the advantages of the conceived RZFB.

This paper develops a non-coherent index modulation (IM) system in which activation patterns are characterized by multi-level block codes. We analyze performance of such a system under the maximum-likelihood (ML) receiver and when the set of activation patterns follows a multi-level code generated from asymptotically optimal alphabets. An asymptotic analysis of the pair-wise error probability (PEP) shows that the system can exploit a diversity order that is determined by the distance of the worst codeword pair in the$l_{1}$metric, known as the Manhattan norm. We then explore the rate-diversity tradeoff for the developed non-coherent IM system as a function of the code length. Specifically, Gilbert-style bounds on the data rates for systems based on binary and ternary codes are obtained that can ensure a given diversity order. We approach the problem of packing in the$l_{1}$metric by partitioning codes into permutation modulation codes (PMCs) and obtaining Gilbert-style bounds on PMCs. Several achievable rates for non-coherent binary and ternary IM systems, as well as a tradeoff between the information rate and codeword error probability (CEP) are also derived. Finally, simulation results are provided to corroborate the theoretical analysis.

Relay-aided multi-user OFDM is investigated under which multiple sources transmit their signals to a multi-antenna relay during the first relaying stage and then the relay amplifies and forwards the composite signal to all destinations during the second stage. The signal transmission of both stages experience frequency selectivity. The relay is powered both by an energy source through the wireless power transfer as well as by the energy recycled from its own self-interference during the second stage. Accordingly, we jointly design the power allocations both at the multiple source nodes and at a common relay node for maximizing the network’s sum-throughput, which poses a large-scale nonconvex problem, regardless whether proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS) is used for signal transmission to the relay. We develop new alternating descent procedures for solving our joint optimization problems, which are based on closed-forms and thus are of very low computational complexity even for large numbers of subcarriers. The results show the superiority of IGS over PGS in terms of both its sum-rate and individual user-rate. Another benefit of IGS over PGS is that the former promises fairer rate distribution across the subcarriers. Moreover, the recycled self-interference also provides a beneficial complementary energy source.

A multi-antenna aided base station (BS) supporting several multi-antenna downlink users with the aid of a reconfigurable intelligent surface (RIS) of programmable reflecting elements (PREs) is considered. Low-resolution PREs constrained by a set of sparse discrete values are used for reasons of cost-efficiency. Our challenging objective is to jointly design the beamformers at the BS and the RIS’s PREs for improving the throughput of all users by maximizing their geometric-mean, under a variety of different access schemes. This constitutes a computationally challenging problem of mixed continuous-discrete optimization, because each user’s throughput is a complicated function of both the continuous-valued beamformer weights and of the discrete-valued PREs. We develop low-complexity algorithms, which iterate by directly evaluating low-complexity closed-form expressions. Our simulation results show the advantages of non-orthogonal multiple access-aided signaling, which allows the users to decode a part of the multi-user interference for enhancing their throughput.

This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users’ rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs.

This paper considers a reconfigurable intelligent surface (RIS)-aided network, which relies on a multiple antenna array aided base station (BS) and an RIS for serving multiple single antenna downlink users. To provide reliable links to all users over the same bandwidth and same time-slot, the paper proposes the joint design of linear transmit beamformers and the programmable reflecting coefficients of an RIS to maximize the geometric mean (GM) of the users’ rates. A new computationally efficient alternating descent algorithm is developed, which is based on closed-forms only for generating improved feasible points of this nonconvex problem. We also consider the joint design of widely linear transmit beamformers and the programmable reflecting coefficients to further improve the GM of the users’ rates. Hence another alternating descent algorithm is developed for its solution, which is also based on closed forms only for generating improved feasible points. Numerical examples are provided to demonstrate the efficiency of the proposed approach.

This paper considers a network of a multi-antenna array base station (BS) and a reconfigurable intelligent surface (RIS) to deliver both information to information users (IUs) and power to energy users (EUs). The RIS links the connection between the IUs and the BS as there is no direct path between the former and the latter. The EUs are located nearby the BS in order to effectively harvest energy from the high-power signal from the BS, while the much weaker signal reflected from the RIS hardly contributes to the EUs’ harvested energy. To provide reliable links for all users over the same time-slot, we adopt the transmit time-switching (transmit-TS) approach, under which information and energy are delivered over different time-slot fractions. This allows us to rely on conjugate beamforming for energy links and zero-forcing/regularized zero-forcing beamforming (ZFB/RZFB) and on the programmable reflecting coefficients (PRCs) of the RIS for information links. We show that ZFB/RZFB and PRCs can be still separately optimized in their joint design, where PRC optimization is based on iterative closed-form expressions. We then develop a path-following algorithm for solving the max-min IU throughput optimization problem subject to a realistic constraint on the quality-of-energy-service in terms of the EUs’ harvested energy thresholds. We also propose a new RZFB for substantially improving the IUs’ throughput.

The paper is the first work that considers a constrained feedback control strategy to navigate an unmanned aerial vehicle (UAV) from a given starting point to a given terminal point while harvesting solar energy and providing a wireless communication service for ground users. Wireless communication channels are stochastic and cannot be known off-line, making the problem of off-line UAV path planning for wireless communication as considered in most existing works less meaningful. We consider the problem of navigating a solar-powered UAV from a starting point to a terminal point to harvest solar energy while serving the two-way communication between multiple pairs of ground users in a complex terrain. The objective is to jointly optimize the UAV’s flight time and its flight path by trading-off between the harvested energy and power consumption subject to the ground users’ minimum throughput requirement. We develop a new model predictive control (MPC) technique to address this problem. Namely, based on the well-known statistics of the air-to-ground (A2G) and ground-to-air (G2A) wireless channels, a predictive control model is proposed at each time-instant, which leads to an optimization problem over a receding horizon for the control design. This problem is non-convex due to the involvement of various optimization variables, which is then solved via novel convex iterations. Simulation results show the merits of the proposed algorithm. The results obtained by the proposed algorithm match with the benchmark non-MPC and offline-MPC approaches.

The capability of massive multiple-input multiple-output (mMIMO) systems supporting the throughput requirement of as many users as possible is investigated. The bottleneck of serving small numbers of users by a large number of transmit antennas in conventional mMIMO is unblocked by a new time-fraction-wise beamforming technique, which focuses signal transmission in fractions of a time slot. Based on this time-fraction-wise signal transmission, a new user service scheduling scheme for multi-cell mMIMO, whose cell-edge users suffer not only poor channel conditions but also multi-cell interference, is proposed to support a large user-population. We demonstrate that the numbers of users served by our multi-cell mMIMO within a time-slot may be as high as twice the number of its transmit antennas.

Unmanned aerial vehicle (UAV)-aided two-way relaying networks are designed, where a UAV is deployed to assist multiple pairs of users in their information exchange. There are two basic approaches for the user pairs' information exchange within a single time slot via the UAV relay. The first approach is based on full-duplex, where all participants operate in the full-duplex mode to transmit and receive signals simultaneously. However, all transceivers have to operate in the face of severe self-interference, which cannot be completely suppressed. The second approach is based on conventional half-duplex, where the users send their information to the UAV within a certain fraction of the time slot, and the UAV relays them within the remaining fraction to avoid the self-interference. In either approach, the joint bandwidth and power allocation maximizing the sum information exchange throughput under realistic resource and user throughput constraints poses a complex nonconvex problem. New inner approximations are proposed for developing path-following algorithms for their computation. Our numerical results show that the time-fraction-based half-duplex approach clearly outperforms the high-complexity full-duplex approach.

A multi-user fog radio access network (F-RAN) is designed for supporting content-centric services. The requested contents are partitioned into sub-contents, which are then ‘beamformed’ by the remote radio heads (RRHs) for transmission to the users. Since a large number of beamformers must be designed, this poses a computational challenge. We tackle this challenge by proposing a new class of regularized zero forcing beamforming (RZFB) for directly mitigating the inter-content interferences, while the ‘intra-content interference’ is mitigated by successive interference cancellation at the user end. Thus each beamformer is decided by a single real variable (for proper Gaussian signaling) or by a pair of complex variables (for improper Gaussian signaling). Hence the total number of decision variables is substantially reduced to facilitate tractable computation. To address the problem of energy efficiency optimization subject to multiple constraints, such as individual user-rate requirement and the fronthauling constraint of the links between the RRHs and the centralized baseband signal processing unit, as well as the total transmit power budget, we develop low-complexity path-following algorithms. Finally, we confirm their performance by simulations.

Under both long (infinite) and short (finite) blocklength transmissions, this paper considers physical layer security for a wireless interference network of multiple transmitter-user pairs, which is overheard by multiple eavesdroppers (EVs). The EVs are assumed to have better channel conditions than the legitimate users (UEs), making the conventional transmission unsecured. The paper develops a novel time-fraction based transmission, under which the information is transmitted to the UEs within a fraction of the time slot and artificial noise (AN) is transmitted within the remaining fraction to counter the strong EVs' channels. Based on channel distribution information of UEs and EVs, the joint design of transmit beamforming, time fractions and AN power allocation to maximize the worst users' secrecy rate is formulated in terms of nonconvex problems. Path-following algorithms of low complexity and rapid convergence are proposed for their solution. Simulations are provided to demonstrate the viability of the proposed methodology.

Providing ultra reliable and low-latency communication (URLLC) is considered one of the major challenges for wireless communication networks. This article considers a downlink URLLC system in which a base station (BS) serves multiple single-antenna users in the short blocklength regime. With the objective of maximizing the users' minimum rate, three different optimization problems are considered: (i) joint design of bandwidth and power allocation for the case of a single-antenna BS; (ii) beamforming design for the case of a multiple-antenna BS; and (iii) design of power allocation with regularized zero-forcing beamforming for the case of a multiple-antenna BS. In the short blocklength regime, the achievable rate is a complicated function of bandwidth and power allocation coefficients or beamforming vectors, which makes these max-min rate optimization problems challenging to solve. This work develops path-following algorithms, which generate a sequence of improved feasible points and converge at least to a locally optimal solution, to solve these three optimization problems. Performance of the proposed algorithms is analyzed through extensive simulations under various settings of transmit power budget, number of users, total bandwidth, transmission time, and number of transmit antennas at the BS. Simulation results clearly demonstrate the merits of the proposed algorithms.

This paper considers cell-free massive MIMO (cfm-MIMO) for downlink ultra reliable and low-latency communication (URLLC). At the time of writing, cfm-MIMO has only been considered for communication in the long blocklength regime (LBR), whose throughput is determined by the Shannon capacity with the interference treated as Gaussian noise. Conjugate beamforming (CB) is often used as it requires only local channel state information (CSI) for implementation but its design is based on a large-scale nonconvex problem, which is computationally intractable. The rate function in URLLC is much more complex than the Shannon rate function. The paper proposes a special class of CB, which admits a low-scale optimization formulation for computational tractability. Accordingly, a new path-following algorithm, which generates a sequence of better feasible points and converges at least to a locally optimal solution, is developed for optimizing URLLC rates and cfm-MIMO energy efficiency. Furthermore, the paper also develops improper Gaussian signaling to improve both the Shannon rate and URLLC rate.

Improper Gaussian signaling (IGS) has shown its capability of improving the rate of interference-limited networks by exploiting the additional degrees of freedom in signal processing. This article considers a system of a multiple-input single-output (MISO) cellular network coexisting with device-to-device (D2D) communication, where the former employs proper Gaussian signaling (PGS) but the latter employs IGS to improve D2D's rate and also to mitigate interference to the former. Both non-orthogonal and orthogonal bandwidth sharing between cellular users (CUs) and D2D pairs are considered. The problems of joint bandwidth allocation and signal beamforming to maximize the minimum CUs' rate subject to the transmit power budget and D2D's rate threshold are addressed, which pose critical computational challenges. Path-following algorithms of low complexity are developed for computational solutions. Two distinct scenarios, i.e., unmanned aerial vehicle (UAV)-enabled networks, and MISO cellular networks are simulated to give insight into the superiority of using IGS over PGS. Our results reveal that in UAV-enabled networks, orthogonal sharing produces a higher D2D's rate, while non-orthogonal sharing offers better CUs' rate under practical levels of D2D's rate. In MISO systems, IGS is a game-changer, which enables the orthogonal sharing to uniformly outperform the non-orthogonal sharing in terms of CUs' rate.

This paper considers a wireless interference network in which the communication between multiple transmitter-user pairs is overheard by multiple eavesdroppers (EVs). Based on knowledge of the channel distribution, the goal is to maximize the worst users' secrecy rate under both long (infinite) blocklength and short (finite) blocklength transmissions. Under long blocklength transmission, the performance of the existing algorithms is unsatisfactory when the wiretapped channels are sufficiently strong. To address this drawback, we adopt a time-fraction based information and artificial noise (AN) transmission, under which first the information is transmitted within the initial fraction of the time slot and then AN is transmitted within the remaining fraction. Accordingly, the problem of join optimization of the time fractions, transmit power, and AN power to maximize the minimum secrecy rate is proposed and computed by a path-following algorithm, which iterates feasible points and converges at least to a locally optimal solution. A similar problem under short blocklength transmission is also proposed and computed. The provided simulations results clearly show the merits of the proposed approach.

This paper proposes a novel successive interference cancellation method to enhance the ergodic spectral efficiency of a cell-free massive multiple-input multiple-output (MIMO) system with non-orthogonal multiple-access (NOMA). Unlike the majority of existing research works on performance evaluation of NOMA, which assume perfect channel state information and perfect data detection for successive interference cancellation, we take into account the effect of practical (hence imperfect) successive interference cancellation (SIC). We show that the received signal at the backhaul network of a cell-free massive MIMO-NOMA system can be effectively treated as a signal received over an AWGN channel. As a result, a discrete joint distribution between the interfering signal and its detected version can be analytically found, from which an adaptive SIC scheme is proposed to improve performance of interference cancellation.

This paper investigates phasor measurement unit (PMU) placement for informative state estimation in smart grid by incorporating various constraints for observability. Observability constitutes an important property for PMU placement to characterize the depth of the buses' reachability by the placed PMUs, but addressing it solely by binary linear programming as in many works still does not guarantee a good estimate for the grid state. Some existing works have considered optimization of some estimation indices by ignoring the observability requirements for computational ease and thus potentially lead to trivial results such as acceptance of the estimate for an unobserved state component as its unconditional mean. In this work, the PMU placement optimization problem is considered by minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state subject to observability constraints, which incorporate operating conditions such as presence of zero injection buses, contingency of measurement loss, and limitation of communication channels per PMU. The proposed design is thus free from the fundamental shortcomings in the existing PMU placement designs. The problems are posed as large scale binary nonlinear optimization problems involving thousands of binary variables, for which this paper develops efficient algorithms for computational solutions. Their performance is analyzed in detail through numerical examples on large scale IEEE power networks. The solution method is also shown to be extendable to AC power flow models, which are formulated by nonlinear equations.

This paper considers a network consisting of a multiple antenna array access point serving multiple single antenna downlink users with the assistance of a reconfigurable intelligent surface (RIS). The reflecting coefficients of the RIS can be programmed to ensure that the signals reflected from the RIS elements add coherently at the users. The joint design of these programmable reflecting coefficients and transmit beamforming to maximize the users' worst rate is addressed. Under either proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS), the design poses a very computationally challenging nonconvex problem. Based on their exactly penalized optimization reformulation, which incorporates the computationally intractable unit-modulus constraints on the reflecting coefficients into the optimization objectives, new iterative algorithms of low computational complexity, which converge at least to a locally optimal solution, are developed. The provided simulations show not only the benefit of using the RIS, but also the advantage of IGS over PGS in delivering higher rates to users.

We consider a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) network in which a source node, Alice, communicates with an energy-harvesting destination node, Bob, in the presence of a passive eavesdropper. To secure the wireless transmission, Alice generates a hybrid artificial noise (AN) in both frequency and time domains. Moreover, in order to collect more energy, Bob splits the received signal power of the cyclic prefix of each OFDM block. We then propose two non-convex optimization problems to balance both the need for security and the need for harvesting energy at Bob. While one considers maximizing the secrecy rate, the other approach aims at maximizing the harvested energy. Path-following algorithms of low computational complexity are developed and evaluated. Our numerical results show the gain of our proposed scheme and the effectiveness of our proposed algorithms.

This paper considers wireless-powered relaying multiple-input-multiple-output (MIMO) communication, where all four nodes (information source, energy source, relay, and destination) are equipped with multiple antennas. Orthogonal frequency division multiplexing (OFDM) is applied for information processing to compensate the frequency selectivity of communication channels between the information source and the relay and between the relay and the destination as these nodes are assumed to be located far apart from each. The relay is equipped with a full-duplexing interface for harvesting energy not only from the wireless transmission of the dedicated energy source but also from its own transmission while relaying the source information to the destination. The problem of designing the optimal power allocation over OFDM subcarriers and transmit antennas to maximize the overall spectral efficiency is addressed. Due to a very large number of subcarriers, this design problem poses a large-scale nonconvex optimization problem involving a few thousand variables of power allocation, which is very computationally challenging. A novel path-following algorithm is proposed for computation. Based on the developed closed-form calculation of linear computational complexity at each iteration, the proposed algorithm rapidly converges to an optimal solution. Compared to the best existing solvers, the computational complexity of the proposed algorithm is reduced at least 105times, making it very efficient and practical for online computation while existing solvers are ineffective. Numerical results for a practical simulation setting show promising results by achieving high spectral efficiency.

Recent studies of single-cell two-user networks have shown that a higher network throughput is achieved by using a common message to be decoded by both users and conveying partial information for both users, rather than using the common message to convey the entire information for one of the two users. The latter is essentially the conventional non-orthogonal multiple access (NOMA), which performs better than orthogonal multiple access (OMA) only under users' dissimilar channel conditions. Unlike NOMA, the former performs consistently better than OMA. This paper generalizes such a signaling strategy to a general multi-cell multiuser network, which leads to a new NOMA approach (called n-NOMA) in which each pair of users decodes a message that conveys partial information for one of them only. Unlike the conventional NOMA, whose performance is dependent on the users' pairing strategy, the proposed n-NOMA consistently outperforms both NOMA and OMA schemes. Both proper and improper Gaussian signaling is considered for all the concerned schemes and it is shown that the latter is clearly more advantageous than the former.

The paper considers the problem of beamforming design for a multi-cell network of downlink users, who either harvest energy or decode information or do both by receiving signals from the multi-antenna base station (BS) within a time slot and over the same frequency band. Our previous contributions have showed that the time-fraction based energy and information transmission, under which first the energy is transferred within the initial fraction of time and then the information is transferred within the remaining fraction, is the most efficient design alternative both in terms of its practical implementation and network performance. However, at the time of writing, both energy and information beamforming has only been implemented for proper Gaussian signaling (PGS), which has limited the network's throughput. Although the network throughput could be improved in some specific scenarios by using non-orthogonal multi-access (NOMA), this may compromise the user secrecy. In order to circumvent the above implementations, we conceive improper Gaussian signaling (IGS) for information beamforming, which enables the network to substantially improve its throughput in any scenario without jeopardizing the user secrecy despite its low-complexity signal processing at the user end. A simpler subclass of IGS is also considered, which also outperforms NOMA PGS and works under any arbitrary scenario.

Internet-of-things (IoT) applications continue to drive advancements in serving as many heterogeneous low-latency downlinks and uplinks as possible within a constrained communication bandwidth. Full-duplexing (FD) transceivers have been introduced to implement simultaneous signal transmission and reception (STR) over the entire available frequency band. However, both inter-link interference and FD loop-interference are hardly suppressed to a necessary level for the effectiveness of FD-based STR even for microcells. This paper proposes an alternative STR technique per one time-slot for macrocells, where a fraction of a time-slot is used for downlinks and the remaining complementary fraction of the time-slot is used for uplinks. Thus, STR over the entire available bandwidth can be implemented in a way with no loop interference. Furthermore, another approach of using a fraction of the available bandwidth for downlinks and the remaining complementary fraction of the bandwidth for uplinks over the whole time-slot is also proposed. The problem of both downlink and uplink beamforming to maximize the energy efficiency of such heterogeneous networks subject to the quality-of-service in terms of downlink and uplink throughput is examined for all three possible STRs. Numerical results demonstrate the advantages of the time-fraction-wise STR and bandwidth-fraction-wise STR over the FD-based STR, where the time-fraction-wise STR is not only the best in serving the same numbers of downlinks and uplinks but also is capable of serving many more downlinks and uplinks with a higher energy efficiency.

This paper proposes Han-Kobayashi signaling (HKS), under which each pair of users decodes a common message to improve their throughput, for UAV-enabled multi-user communication. Given that only a single transmit antenna is used and thus there is no null space of users' channels for inserting an artificial noise that would effectively help to jam an eavesdropper without interfering the users' desired signals, a new information and artificial noise transfer scheme to address physical layer security (PLS) for the considered networks is investigated. Under this scheme, the UAV sends the confidential information to its users within a fraction of the time slot and sends the artificial noise within the remaining fraction. Accordingly, the problem of jointly optimizing the time-fraction, bandwidth and power allocation to maximize the users' worst secrecy throughput is formulated. New inner approximations are proposed for developing path-following algorithms for its computation. Simulation shows that the proposed information and artificial noise transfer enables not only HKS but also orthogonal multi-access and nonorthogonal multi-access to provide PLS for UAV-enabled communication even when the eavesdropper is in the best channel condition. HKS outperforms the other two schemes in terms of users' worst secrecy throughput.

In this work, we propose efficient optimisation methods for relay-assisted unmanned aerial vehicles (UAVs) in cognitive radio networks (CRNs) to cope with the network destruction in the event of a natural disaster. Our model considers real- time optimisation in embedded UAV-CRN communication involved in recovering wireless communication services. Particularly, by conceiving advanced optimisation techniques and training deep neural networks, our solutions become capable of supporting real-time applications in disaster recovery scenarios. Our algorithms impose low computational complexity, hence, have a low execution time in solving real- time optimisation problems. Numerical results demonstrate the benefits of our approaches proposed for UAV-CRN.

This paper proposes and investigates an overlay spectrum sharing system in conjunction with the simultaneous wireless information and power transfer to enable communications for the Internet of Things (IoT) applications. Considered is a cooperative cognitive radio network, where two IoT devices (IoDs) exchange their information and also provide relay assistance to a pair of primary users (PUs). Different from most existing works, in this paper, both IoDs can harvest energy from the radio-frequency signals received from the PUs. By utilizing the harvested energy, they provide relay cooperation to PUs and realize their own communications. For harvesting energy, a time-switching-based approach is adopted at both IoDs. With the proposed scheme, one round of bidirectional information exchange for both primary and IoT systems is performed in four phases, i.e., one energy harvesting phase and three information processing phases. Both IoDs rely on the decode-and-forward operation to facilitate relaying, whereas the PUs employ selection combining technique. For investigating the performance of the considered network, this paper first provides exact expressions of user outage probability (OP) for the primary and IoT systems under Nakagami-m fading. Then, by utilizing the expressions of user OP, the system throughput and energy efficiency are quantified together with the average end-to-end transmission time. Numerical and simulation results are provided to give useful insights into the system behavior and to highlight the impact of various system/channel parameters.

For a multi-user multi-cell network, which suffers both intra-cell and inter-cell interference, this letter considers improper Gaussian signaling (IGS) as a means to improve the achievable rate. The problem of interest is designing of improper Gaussian signals' augmented covariance matrices to maximize the users' minimum rate subject to transmit power constraints. This problem is seen as a nonconvex matrix optimization problem, which cannot be solved by conventional techniques, such as weighted minimum mean square error minimization or alternating optimization. A path-following algorithm, which iterates a sequence of improved feasible points, is proposed for its computation. The provided simulation results for three cells serving 18 users show that IGS offers a much better max-min rate compared with that achieved by conventional proper Gaussian signaling. Another problem of maximizing the energy efficiency in IGS is also considered.

An energy harvesting (EH) enabled network is capable of delivering energy to users, who are located sufficiently close to the base stations. However, wireless energy delivery requires much more transmit power than what the normal information delivery does. It is very challenging to provide the quality of wireless information and power delivery simultaneously. It is of practical interest to employ non-orthogonal multiple access (NOMA) to improve the network throughput, while fulfilling the EH requirements. To realize both the EH and information decoding, this paper considers a transmit time-switching (transmit-TS) protocol. Two important problems of users' max-min throughput optimization and energy efficiency maximization under power constraint and EH thresholds, which are non-convex in beamforming vectors, are addressed by efficient path-following algorithms. In addition, the conventional power splitting (PS)-based EH receiver is also considered. The provided numerical results confirm that the proposed transmit-TS-based algorithms clearly outperform the PS-based algorithms in terms of throughput and energy efficiency.

Unmanned aerial vehicles (UAVs) can be deployed as flying base stations (BSs) to leverage the strength of line-of-sight connections and effectively support the coverage and throughput of wireless communication. This paper considers a multiuser communication system, in which a single-antenna UAV-BS serves a large number of ground users by employing non-orthogonal multiple access (NOMA). The max-min rate optimization problem is formulated under total power, total bandwidth, UAV altitude, and antenna beamwidth constraints. The objective of max-min rate optimization is non-convex in all optimization variables, i.e., UAV altitude, transmit antenna beamwidth, power allocation, and bandwidth allocation for multiple users. A path-following algorithm is proposed to solve the formulated problem. Next, orthogonal multiple access (OMA) and dirty paper coding (DPC)-based max-min rate optimization problems are formulated and respective path-following algorithms are developed to solve them. The numerical results show that NOMA outperforms OMA and achieves rates similar to those attained by DPC. In addition, a clear rate gain is observed by jointly optimizing all the parameters rather than optimizing a subset of parameters, which confirms the desirability of their joint optimization.

Caching and multicast have surged as effective tools to alleviate the heavy load from the backhaul links while enabling content-centric delivery in communication networks. The main focus of work in this area has been on the cache placements to manage the network delay and backhaul transmission cost. An important issue of optimizing the cost efficiency in content delivery has not been addressed. This paper tackles this issue by proposing collaborative multicast beamforming in cache-enabled ultra-dense networks. The objective is to maximize the cost efficiency, which is defined as the ratio of the content throughput to the sum of power consumption and backhaul cost, in providing quality-of-service for content delivery. Zero-forcing beamforming and generalized zero-forcing beamforming are employed to force the multi-content interference to zero or mitigate it while amplifying the desired signals for users. These problems of collaborative multicast beamforming design are computationally difficult. Path-following algorithms, which invoke a simple convex quadratic program at each iteration, are developed for their solution. Numerical results are provided to demonstrate the computational efficiency of the proposed algorithms and also give insights into the impact of caching on the cost efficiency.

This paper investigates the optimal signaling schemes and capacities of non-coherent correlated multiple-input single-output (MISO) channels in fast Rayleigh fading. We consider both channels under per-antenna power constraints as well as channels under joint per-antenna and sum power constraints. For per-antenna power constraint channels, we first establish the convex and compact properties of the feasible sets, and demonstrate the existence of optimal input distribution and the uniqueness of optimal effective magnitude input distribution. By exploiting the solutions of a quadratic optimization problem, we show that the Kuhn-Tucker condition on the optimal inputs can be simplified to a single dimension. As a result, we can apply the Identity Theorem to show the discrete and finite nature of the optimal effective magnitude distribution, with a mass point located at the origin. By using this distribution, we then construct a finite and discrete optimal input vector distribution. The use of this input allows us to determine the capacity gain of MISO over SISO via the phase solutions of a constrained quadratic optimization problem on a sphere, which can be obtained using a proposed penalized optimization algorithm. We also extend the results to MISO channels subject to the joint per-antenna and sum power constraints. Under this consideration, it is shown that not all per-antenna constraints are active. While the finiteness and discreteness of the optimal effective magnitude and the optimal input vector distributions still hold, the optimal phases and the optimal power allocation among the transmit antennas need to be determined simultaneously via a quadratic optimization problem under inequality constraints. These solutions can finally be used to obtain the MISO capacity gain.

A multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) network in the presence of a passive eavesdropper is considered. The deployment of radio frequency power transfer at the receiver and the use of hybrid artificial noise at the transmitter are simultaneously taken into account. At the legal receiver, the cyclic prefix of each OFDM block is used for the purpose of harvesting energy. In parallel, the power-splitting SWIPT technique is additionally used. We then propose a trade-off problem to maximize the secrecy rate of the network while keeping the harvested energy above a given threshold. Throughout the numerical results, the performance of our proposed secure scheme is evaluated.

This paper considers a massive MIMO network that includes one multiple-antenna base station, one multiple-antenna eavesdropper, and K single-antenna users. The eavesdropper operates in passive mode and tries to overhear the confidential information from one of the users in the down-link transmission. In order to secure the confidential information, two artificial noise (AN)-aiding schemes are proposed. In the first scheme, AN is injected into the downlink training signals to prevent the eavesdropper from obtaining the correct channel state information of the eavesdropping link. In the second scheme, AN is deployed in both downlink training phase and payload data transmission phase to further degrade the eavesdropping channel. Analytical expressions and tight approximations of the achievable secrecy rate of the considered systems are derived with taking imperfect channel estimation and two types of precoding, i.e., maximum-ratio-transmission and zero-forcing, into consideration. Optimization algorithms for power allocation are proposed to enhance the secrecy performance of the proposed AN-aiding schemes. The results reveal that deploying AN in the downlink training phase of massive MIMO networks does not affect the downlink channel estimation process at users while enabling the system to suppress the downlink channel estimation process at eavesdropper. As a consequence, the proposed AN-aided schemes improve the system performance significantly. Furthermore, implementing AN in both phases allows the considered system having a flexible solution to maximize its secrecy performance at the price of higher complexity.

Based on the knowledge of the channel distributions of a multi-input single-output wireless network of multiple transmitter-user pairs overheard by an eavesdropper, this letter develops an outage-aware beamforming design to optimize the users' quality-of-service (QoS) in terms of their secrecy rates. This is a very computationally difficult problem with a nonconcave objective function and nonlinear equality constraints in beamforming vectors. A path-following algorithm of low-complexity and rapid convergence is proposed for computation, which is also extended to solving the problem of maximizing the network's secure energy efficiency under users' QoS constraints. Numerical examples are provided to verify the efficiency of the proposed algorithms.

This paper studies the security aspect of a recently introduced “cell-free massive MIMO” network under a pilot spoofing attack. First, a simple method to recognize the presence of this type of an active eavesdropping attack to a particular user is shown. In order to deal with this attack, we consider the problem of maximizing the achievable data rate of the attacked user or its achievable secrecy rate. The corresponding problems of minimizing the power consumption subject to security constraints are also considered in parallel. Path-following algorithms are developed to solve the posed optimization problems under different power allocation to access points (APs). Under equip-power allocation to APs, these optimization problems admit closed-form solutions. Numerical results show their efficiency.

A heterogeneous network (HetNet) of a macrocell base station equipped with a large-scale massive multi-in multi-out (MIMO) antenna array overlaying a number of small cell base stations (small cells) can provide high quality of service (QoS) to multiple users under low transmit power budget. However, the circuit power for operating such a network, which is proportional to the number of transmit antennas, poses a problem in terms of its energy efficiency (EE). This paper addresses the beamforming design at the base stations to optimize the network EE under the QoS constraints and a transmit power budget. Beamforming tailored for weak, strong, and medium cross-tier interference HetNets is proposed. In contrast to the conventional transmit strategy for power efficiency in meeting the users' QoS requirements, which suggest the use of a few hundred antennas, it is found out that the overall network EE quickly drops if this number exceeds 50. It is found that, for a given number of antennas, HetNet is more energy efficient than massive MIMO when considering the overall energy consumption.

Considering a multi-user interference network with an eavesdropper, this paper aims at the power allocation to optimize the worst secrecy throughput among the network links or the secure energy efficiency in terms of achieved secrecy throughput per Joule under link security requirements. Three scenarios for the access of channel state information are considered: the perfect channel state information; partial channel state information with channels from the transmitters to the eavesdropper exponentially distributed; and not perfectly known channels between the transmitters and the users with exponentially distributed errors. The paper develops various path-following procedures of low complexity and rapid convergence for the optimal power allocation. Their effectiveness and viability are illustrated through numerical examples. The power allocation schemes are shown to achieve both high secrecy throughput and energy efficiency.

This paper aims to design linear precoders for signal superposition at the base stations of non- orthogonal multiple access multiple-input multiple-output multi-cellular systems to maximize the overall sum throughput subject to the users' quality-of-service requirements, which are imposed independently on the users' channel conditions. This design problem is formulated as the maximization of a highly nonlinear and nonsmooth function subject to nonconvex constraints, which is very computationally challenging. A path- following algorithm for its solution, which invokes only a simple convex problem of moderate dimension at each iteration, is developed. Generating a sequence of improved points, this algorithm converges at least to a local optimum. Numerical results are then provided to demonstrate its merit.

We consider a cooperative relay network in the presence of many eavesdroppers whose locations are distributed according to a homogeneous Poisson point process. The relay, which operates in amplify-and-forward protocol, has very large transmit and receive antenna arrays, while other nodes are equipped with a single antenna. We assume that the relay exploits maximum ratio combing (MRC) in the uplink and maximum ration transmission in the downlink, while all eavesdroppers are able to exploit MRC to maximize the received signals. In addition, there is no perfect channel state information of any eavesdroppers since all eavesdroppers in practice tend to hide from the legitimate users. Furthermore, we suppose that there are direct links between source and eavesdroppers, while a direct link between source and destination does not exist. Under such assumptions, which are totally biased towards eavesdroppers, we examine the security performance of the proposed system throughout secrecy outage probability and connection outage probability.

We consider a diffusion-based nano-network with N spatially distributed transmitters and one receiver. While the transmitters are linked in a unified entity to perform one transmission, the receiver is large enough to be viewed as a plane. Messages are encoded into the number of nano-scale molecules. Based on these assumptions, we analyze the signal-to-interference ratio, which is based on the average numbers of absorbed molecules. Moreover, we present an approach to interference alignment for molecular communications.

This paper considers constellation design for quadrature spatial modulation (QSM) to minimize the average probability of error. Different than the constellation design previously obtained for spatial modulation (SM), it is shown that, the error performance of QSM not only depends on the Euclidean distances between the amplitude-phase modulation (APM) symbols and the energies of APM symbols, but also on the in-phase and quadrature components of the QSM symbols. The analysis of the union bound of the average error probability reveals that at a very large number of transmit antennas, the optimal constellations for QSM converge to a quadrature phase-shift keying (QPSK) constellation. Simulation results demonstrate the performance superiority of the obtained constellations over the standard PSK and QAM constellations, as well as the constellations specifically designed for SM.

The throughput of users with poor channel conditions, such as those at a cell edge, is a bottleneck in wireless systems. A major part of the power budget must be allocated to serve these users in guaranteeing their quality-of-service (QoS) requirements, hampering QoS for other users, and thus compromising the system reliability. In non-orthogonal multiple access (NOMA), the message intended for a user with a poor channel condition is decoded by itself and by another user with a better channel condition. The message intended for the latter is then successively decoded by itself after canceling the interference of the former. The overall information throughput is thus improved by this particular successive decoding and interference cancellation. This paper aims to design linear precoders/beamformers for signal superposition at the base stations of NOMA multiple-input multiple-output multi-cellular systems to maximize the overall sum throughput subject to the users' QoS requirements, which are imposed independently on the users' channel conditions. This design problem is formulated as the maximization of a highly nonlinear and nonsmooth function subject to nonconvex constraints, which is very computationally challenging. Path-following algorithms for its solution, which invoke only a simple convex problem of moderate dimension at each iteration, are developed. Generating a sequence of improved points, these algorithms converge at least to a local optimum. Extensive numerical simulations are then provided to demonstrate their merit.

Considering a multiple-user multiple-input multiple-output channel with an eavesdropper, this letter develops a beamformer design to optimize the energy efficiency in terms of secrecy bits per Joule under secrecy quality-of-service constraints. This is a very difficult design problem with no available exact solution techniques. A path-following procedure, which iteratively improves its feasible points by using a simple quadratic program of moderate dimension, is proposed. Under any fixed computational tolerance, the procedure terminates after finitely many iterations, yielding at least a locally optimal solution. Simulation results show the superior performance of the obtained algorithm over other existing methods.

A cooperative relay network operating in the presence of eavesdroppers, whose locations are distributed according to a homogeneous Poisson point process, is considered. The relay is equipped with a very large antenna array and can exploit maximal ratio combing in the uplink and maximal ratio transmission in the downlink. A realistic model in which the channel state information of every eavesdropper is not known is considered, as eavesdroppers tend to hide themselves in practice. The destination is thus in a much weaker position than all the eavesdroppers because it only receives the retransmitted signal from the relay. Under this setting, the security performance is investigated for two relaying protocols: amplify-and-forward and decode-and-forward. The secrecy outage probability, the connection outage probability, and the tradeoff between them, which is controlled by the source power allocation, are examined. Finally, suitable solutions for the source power (such that once the transmission occurs with high reliability, the secure risk is below a given threshold) are proposed for a tradeoff between security and reliability.

Information and energy can be transferred over the same radio-frequency channel. In the power-splitting (PS) mode, they are simultaneously transmitted using the same signal by the base station (BS) and later separated at the user (UE)'s receiver by a power splitter. In the time-switching (TS) mode, they are either transmitted separately in time by the BS or received separately in time by the UE. In this paper, the BS transmit beamformers are jointly designed with either the receive PS ratios or the transmit TS ratios in a multicell network that implements wireless information and power transfer (WIPT). Imposing UE-harvested energy constraints, the design objectives include: 1) maximizing the minimum UE rate under the BS transmit power constraint, and 2) minimizing the maximum BS transmit power under the UE data rate constraint. New iterative algorithms of low computational complexity are proposed to efficiently solve the formulated difficult nonconvex optimization problems, where each iteration either solves one simple convex quadratic program or one simple second-order-cone-program. Simulation results show that these algorithms converge quickly after only a few iterations. Notably, the transmit TS-based WIPT system is not only more easily implemented but outperforms the receive PS-based WIPT system as it better exploits the beamforming design at the transmitter side.

A communication system is considered consisting of a full-duplex multiple-antenna base station (BS) and multiple single-antenna downlink users (DLUs) and single-antenna uplink users (ULUs), where the latter need to harvest energy for transmitting information to the BS. The communication is thus divided into two phases. In the first phase, the BS uses all available antennas for conveying information to DLUs and wireless energy to ULUs via information and energy beamforming, respectively. In the second phase, ULUs send their independent information to the BS using their harvested energy while the BS transmits the information to the DLUs. In both the phases, the communication is operated at the same time and over the same frequency band. The aim is to maximize the sum rate and energy efficiency under ULU achievable information throughput constraints by jointly optimizing beamforming and time allocation. The utility functions of interest are nonconcave and the involved constraints are nonconvex, so these problems are computationally troublesome. To address them, path-following algorithms are proposed to arrive at least at local optima. The proposed algorithms iteratively improve the objectives with convergence guaranteed. Simulation results demonstrate that they achieve rapid convergence and outperform conventional solutions.

In order to enhance secrecy performance of protecting scenarios, understanding the illegitimate side is crucial. In this paper, from the perspective of the illegitimate side, the security attack from a full-duplex cyber-weapon equipped with massive antenna arrays is considered. To evaluate the behavior of the proposed cyber-weapon, we develop a closed-form, a tight approximation, and asymptotic expressions of the achievable ergodic secrecy rate with taking into consideration imperfect channel estimation at the cyber-weapon. The results show that even under some disadvantage conditions, i.e., imperfect channel estimation and self-interference, the full-duplex massive array cyber-weapon can disable traditional physical layer protecting schemes, i.e., increasing the transmit power and the number of antennas at the legitimate transmitter. In addition, when a transmit power optimization scheme for maximizing the difference between the eavesdropping rate and the legitimate rate is applied at the full-duplex cyber-weapon, the malicious attack is even more dangerous. The results also reveal that when the legitimate side faces an advance adversary, it is essential to prevent important information in the training phases exposing to the illegitimate side.

It is known that superposition signaling in Gaussian interference networks is capable of improving the achievable rate region. However, the problem of maximizing the rate gain offered by superposition signaling is computationally prohibitive, even in the simplest case of two-user single-input single-output interference networks. This paper examines superposition signaling for the general multiple-input multiple-output broadcast Gaussian interference networks. The problem of maximizing either the sum rate or the minimal user's rate under superposition signaling and dirty paper coding is solved by a computationally efficient path-following procedure, which requires only a convex quadratic program for each iteration but ensures convergence at least to a locally optimal solution. Numerical results demonstrate the substantial performance advantage of the proposed approach.

This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme. Accordingly, new optimization formulations for tensor completion are proposed as well as two new algorithms for their solution. The first one called simple low-rank tensor completion via TT (SiLRTC-TT) is intimately related to minimizing a nuclear norm based on TT rank. The second one is from a multilinear matrix factorization model to approximate the TT rank of a tensor, and is called tensor completion by parallel matrix factorization via TT (TMac-TT). A tensor augmentation scheme of transforming a low-order tensor to higher orders is also proposed to enhance the effectiveness of SiLRTC-TT and TMac-TT. Simulation results for color image and video recovery show the clear advantage of our method over all other methods.

Some next-generation wireless networks will likely involve the energy-efficient transfer of information and energy over the same wireless channel. Moreover, densification of such networks will make the physical layer more vulnerable to cyber attacks by potential multi-antenna eavesdroppers. To address these issues, this paper considers transmit time-switching (TS) mode, in which energy and information signals are transmitted separately in time by the base station (BS). This protocol is not only easy to implement but also delivers the opportunity for multi-purpose beamforming, in which energy beamformers can be used to jam eavesdroppers during wireless power transfer. In the presence of imperfect channel estimation and multiantenna eavesdroppers, the energy and information beamformers and the transmit TS ratio are jointly optimized to maximize the worst-case user secrecy rate subject to energy constrained users' harvested energy thresholds and a BS transmit power budget. New robust path-following algorithms, which involve one simple convex quadratic program at each iteration are proposed for computational solutions of this difficult optimization problem and also the problem of secure energy efficiency maximization. The latter adds further complexity due to additional optimization variables appearing in the denominator of the secrecy rate function. Numerical results confirm that the performance of the proposed computational solutions is robust against channel uncertainties.

This paper considers the joint design of user power allocation and relay beamforming in relaying communications, in which multiple pairs of single-antenna users exchange information with each other via multiple-antenna relays in two time slots. All users transmit their signals to the relays in the first time slot while the relays broadcast the beamformed signals to all users in the second time slot. The aim is to maximize the system's energy efficiency (EE) subject to quality-of-service (QoS) constraints in terms of exchange throughput requirements. The QoS constraints are nonconvex with many nonlinear cross-terms, so finding a feasible point is already computationally challenging. The sum throughput appears in the numerator while the total consumption power appears in the denominator of the EE objective function. The former is a nonconcave function and the latter is a nonconvex function, making fractional programming useless for EE optimization. Nevertheless, efficient iterations of low complexity to obtain its optimized solutions are developed. The performance of the multiple-user and multiple-relay networks under various scenarios is evaluated to show the merit of the proposed method.

In this paper, new strategies are devised for joint load balancing and interference management in the downlink of a heterogeneous network, where small cells are densely deployed within the coverage area of a traditional macrocell. Unlike existing work, the limited backhaul capacity at each base station (BS) is taken into account. Here, users (UEs) cannot be offloaded to any arbitrary BS, but only to ones with sufficient backhaul capacity remaining. Jointly designed with traffic offload, transmit power allocation mitigates the intercell interference to further support the quality of service of each UE. The objective here is either: 1) to maximize the network sum rate subject to minimum throughput requirements at individual UEs, or 2) to maximize the minimum UE throughput. Both formulated problems belong to the difficult class of mixed-integer nonconvex optimization problems. The inherently binary BS-UE association variables are strongly coupled with the transmit power variables, making the problems even more challenging to solve. New iterative algorithms are developed based on an exact penalty method combined with successive convex programming, where the binary BS-UE association problem and the nonconvex power allocation problem are dealt with one at a time. At each iteration of the proposed algorithms, only two simple convex problems need to be solved at the same time scale. It is proven that the algorithms improve the objective functions at each iteration and converge eventually. Numerical results demonstrate the efficiency of the proposed algorithms in both traffic offloading and interference mitigation.

This paper considers the efficient design of precoding matrices for sum throughput maximization under throughput quality of service (QoS) constraints and energy harvesting (EH) constraints for energy-constrained devices in a full-duplex (FD) multicell multi-user multiple-input-multiple-output network. Both time splitting (TS) and power splitting are considered to ensure practical EH and information decoding. These problems are quite complex due to non-concave objectives and nonconvex constraints. Especially, with TS, which is implementation-wise quite simple, the problem is even more challenging because the time splitting variable is not only coupled with the downlink throughput function but also coupled with the self-interference in the uplink throughput function. New path-following algorithms are developed for their solutions, which require only a single convex quadratic program for each iteration and ensure rapid convergence. Moreover, the FD EH maximization problem under throughput QoS constraints with TS is also considered. The performance of the proposed algorithms is compared with that of the modified problems assuming half-duplex systems. Finally, the merit of the proposed algorithms is demonstrated through extensive simulations.

We consider the downlink of a dense multicell network where each cell region is divided into two zones. The users nearby their serving base station (BS) in the inner zone implement simultaneous wireless information and power transfer (SWIPT), thus harvest energy and decode information using the power splitting approach. Further, they try to eavesdrop the information intended for other users within the same cell. The users in the outer zone of each cell only implement information decoding. Our objective is to maximize the minimum user equipment (UE) signal-to-interference-and-noise ratio (SINR) under constraints on the BS transmit power, minimum energy harvesting levels of near-by users, and maximum SINR of eavesdroppers in the presence of multi-cell interference. For such a highly non-convex problem, semidefinite relaxation (SDR) may even fail to locate a feasible solution. We propose two methods to address such a difficult problem. In the spectral optimization, we express the rank-one constraints as a single reverse convex nonsmooth constraint and incorporate it into the optimization objective. In the difference-of-convex-functions iteration method, we directly solve for the beamforming vectors via quadratic programming (QP), avoiding the matrix rank constraints. In each iteration of the proposed algorithms, we only solve one simple convex semidefinite program (SDP) or QP. Our simulation results confirm that the proposed algorithms converge quickly after a few iterations. More importantly, our algorithms yield the performance that is very close to the theoretical bound given by SDP relaxation with comparable computational complexity.

This paper designs jointly optimal linear precoders for both base stations (BSs) and users in a multiuser multi-input multi-output (MU-MIMO) multicell network. The BSs are full-duplexing transceivers while uplink users and downlink users (DLUs) are equipped with multiple antennas. Here, the network quality-of-service (QoS) requirement is expressed in terms of the minimum throughput at the BSs and DLUs. We consider the problems of either QoS-constrained sum throughput maximization or minimum cell throughput maximization. Due to the nonconcavity of the throughput functions, the optimal solutions of these two problems remain unknown in both half-duplexing and full-duplexing networks. The first problem has a nonconcave objective and a nonconvex feasible set, whereas the second problem has a nonconcave and nonsmooth objective. To solve such challenging optimization problems, we develop iterative low-complexity algorithms that only invoke one simple convex quadratic program at each iteration. Since the objective value is proved to iteratively increase, our path-following algorithms converge at least to the local optimum of the original nonconvex problems. Due to their guaranteed convergence, simple implementation, and low complexity, the devised algorithms lend themselves to practical precoder designs for large-scale full-duplex MU-MIMO multicell networks. Numerical results demonstrate the advantages of our successive convex quadratic programming framework over existing solutions.

This paper considers the Han-Kobayashi transmission strategy that mitigates the downlink intercell interference in a multiuser multi-input multi-output (MU-MIMO) multicell network. The base station (BS) splits the transmitted data of a user (UE) into a common message and a private message, the former of which is then decoded by a paired UE in another cell so as to reduce the respective cross-cell interference. Our aim here is to develop (i) A pairing rule that determines pairs of UEs to share common messages, and (ii) Optimal precoders at the BSs that maximize either the minimum UE throughput or the network sum-rate. To solve the combinatoric UE pairing problem, we propose a heuristic that pairs UEs with the largest corresponding cross-cell channel gains. This approach ensures that the most significant source of intercell interference is eliminated through common message decoding. We then apply the Frank-and-Wolfe procedure of concave programming to solve the highly nonconvex precoder design problems. We show that this procedure generates a sequence of improved solutions and eventually converges to at least a local optimum. Numerical results confirm the advantages of our proposed solution over the conventional strategy where intercell interference is treated as noise.

This paper considers power allocation between data and pilot symbols for an orthogonal frequency division multiplexing (OFDM) system in a high-speed train (HST) environment. The channel gains are very quickly time-varying within an OFDM symbol so both their estimation and symbol detection must be simultaneously implemented with unavoidable inter-carrier interferences. The average channel complex gains are estimated and are used to calculate the basis expansion model (BEM) coefficients of the HST channel in data detection. We choose the effective signal-to-interference and noise ratio (SINR) in symbol detection as the cost function and propose the effective algorithm to maximize it. The simulation results confirm the viability of our proposed algorithm.

Motivated by the work of Dahrouj and Yu in applying the Han-Kobayashi transmission strategy for mitigating the intercell interference in a multi-cell multi-user multiple-input single-output interference network (MISO IN), this paper considers splitting messages into private and common parts in a multi-cell multi-user MIMO IN. Specifically, the covariances of the private messages and common messages are designed to optimize either the sum rate or the minimal rate. The common messages and private messages are decoded in sequence using successive decoding. This paper shows how these difficult optimization problems can be adequately solved by means of d.c. (difference ofconcave functions) optimization over a simple convex set. Numerical and simulation results also reveal the great advantage of our proposed solutions for various types of INs. In particular, the proposed solutions are shown to outperform the algorithm developed by Dahrouj and Yu for the simpler case of the MISO IN.

This paper considers joint linear processing at multi-antenna sources and one multiple-input multiple-output (MIMO) relay station for both one-way and two-way relay-assisted wireless communications. The one-way relaying is applicable in the scenario of downlink transmission by a multi-antenna base station to multiple single-antenna users with the help of one MIMO relay. In such a scenario, the objective of join linear processing is to maximize the information throughput to users. The design problem is equivalently formulated as the maximization of the worst signal-to-interference-plus-noise ratio (SINR) among all users subject to various transmission power constraints. Such a program of nonconvex objective minimization under nonconvex constraints is transformed to a canonical d.c. (difference of convex functions/sets) program of d.c. function optimization under convex constraints through nonconvex duality with zero duality gap. An efficient iterative algorithm is then applied to solve this canonical d.c program. For the scenario of using one MIMO relay to assist two sources exchanging their information in two-way relying manner, the joint linear processing aims at either minimizing the maximum mean square error (MSE) or maximizing the total information throughput of the two sources. By applying tractable optimization for the linear minimum MSE estimator and d.c. programming, an iterative algorithm is developed to solve these two optimization problems. Extensive simulation results demonstrate that the proposed methods substantially outperform previously-known joint optimization methods.

This paper considers joint optimization of cooperative beamforming and relay assignment for multi-user multi-relay wireless networks to maximize the minimum of the received signal-to-interference-plus-noise ratios (SINR). Separated continuous optimization of beamforming and binary optimization of relay assignment already pose very challenging programs. Certainly, their joint optimization, which involves nonconvex objectives and coupled constraints in continuous and binary variables, is among the most challenging optimization problems. Even the conventional relaxation of binary constraints by continuous box constraints is still computationally intractable because the relaxed program is still highly nonconvex. However, it is shown in this paper that the joint programs fit well in the d.c. (difference of two convex functions/sets) optimization framework. Efficient optimization algorithms are then developed for both cases of orthogonal and nonorthogonal transmission by multiple users. Simulation results show that the jointly optimized beamforming and relay assignment not only save transmission bandwidth but can also maintain well the network SINRs.

This paper presents power allocation in nonlinear sensor networks for Gaussian Mixture (GM) information source. The observations of sensors are transmitted through independent Rayleigh flat fading channels to a fusion centre (FC). Transmit Power is optimally allocated to sensor nodes so as to minimize the mean square error (MSE) of estimate at FC. Bayesian linear and optimal nonlinear estimators are deployed at FC to compare the proposed optimal and uniform power allocation among sensors. Extensive simulations validate that the proposed Bayesian linear estimator with optimized power gains effectively works for GM prior distribution.

This paper is concerned with design problems of joint source power allocation and relay beamforming in multi-user multi-relay networks that use single-carrier frequency division multiple access (SC-FDMA) and amplify-and-forward relaying. Examined are the joint programs of (i) maximizing the minimum signal-to-interference-plus-noise ratio (SINR) under various transmitted power constraints, and (ii) minimizing the total transmitted power subject to prescribed SINR thresholds of users. Although these optimization problems are highly nonconvex and have large dimensions, by exploiting their partial convexities and making elegant nonlinear variable changes, they are recast as d.c. (difference of two convex) programs. Efficient d.c. iterative procedures are then developed to find the solutions. Simplified joint programs under the two cases of equal source power and equal relay beamforming weights, respectively, are also considered. Branch-and-bound algorithms of deterministic global optimization are then proposed for solving the simplified joint programs. Simulation results confirm the excellent performance and computational efficiency of all the proposed solutions.

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.

Optimizations of precoding matrices in precode-and-forward (PF) MIMO relaying are nonconvex programs in precoding matrix variables. The semidefinite relaxation (SDR) technique, which relaxes the concerned nonconvex quadratic constraints by (convex) semi-definite ones, can locate the optimal solutions, provided that the numbers of relaying antennas and users are very small. The computational complexity of the SDR grows explosively even with a very moderate increase in the numbers of relaying antennas and/or users, making the existing semidefinite programming (SDP) solvers incapable. In this paper, much more efficient problem formulations of precoding matrix design that exploit the spectral matrix optimization are developed. Such formulations have a low dimensionality and are computationally-tractable nonconvex matrix programs. Furthermore, by exploiting their partial convex structures in the d.c. (difference of two convex functions) framework, new effective iterative solutions are obtained. Extensive simulation results are presented to support the computational advantage of the proposed approach and show that the proposed approach can effectively handle all three considered optimization problems of precoding matrices in MIMO PF relaying, while the SDR approach either is computationally impractical or fails.

We are concerned with a cooperative beamforming design for multi-user multi-relay wireless networks in which the single-carrier frequency division multiple access (SC-FDMA) technique is employed at the terminals. The problem of interest is to find the beamforming weights across relays to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among users subject to individual power constraints at each relay. Such a beamforming design is shown to be a hard nonconvex program and therefore it is mathematically challenging to find the optimal solution. By exploring its partial convex structures, we recast the design problem as minimization of a d.c. (difference of two convex) objective function subject to convex constraints and develop an effective iterative algorithm of low complexity to solve it. Simulation results show that our optimal cooperative beamforming scheme realizes the inherent diversity order of the relay network and it performs significantly better than the equal-power beamforming weights.

For multi-user (MU) amplify-and-forward (AF) cooperative networks, their spectral efficiency can be upgraded within the orthogonal transmission of each source node to an assigned subset of all available relays while their information throughput can be improved through optimized power allocation. We consider the joint optimization in both relay assignment for each source-destination pair and power allocation, which is in fact among the hardest problems in optimization. This is the minimization of a nonconvex objective function subject to mixed integer constraints. The existing numerical algorithms could rarely address to its solutions through computationally affordable procedures. Even the conventional relaxation of the integer constraints by linear constraints does not lead to convex optimization, so the standard convexification does not work either. Nevertheless, we show that it can be effectively solved in the d.c. (difference of two convex) programming context. Numerical simulation confirms the effectiveness of our setting.

Sensor selection is to pick out an appropriate subset of active sensors for reliable collaborative sensing. Naturally, the selected sensors should be as uncorrelated as possible to have more independent sensing outputs for information fusion. In this paper, various uncorrelation metrics are unified by the concept of Bregman divergence. The sensor selections are then systematically formulated as NP-hard integer programs. Unlike commonly used exhaustive enumeration, heuristic searches or simple relaxation of discrete constraints with inherent drawbacks, this paper recasts them into a continuous d.c. (difference of two convex functions) program under convex constraints. Accordingly, an efficient iterative optimization procedure is tailored for locating the optimal solution. Simulation results show its superior performances in comparison with other existing sensor selections.

Total relay transmit power minimization in Amplify-forward (AF) MIMO relay beamforming is naturally formulated as an indefinite quadratic (nonconvex) program and then semidefinite relaxation (SDR) technique can be used to locate their optimal solutions. Indeed, SDR often gives rank-one optimal solution within a low number of relaying antennas and communicating users. However, as the antennas number increases, the computational complexity of SDR grows up explosively that requires a huge amount of additional variables. In this paper, a more efficient problem formulation is introduced, which needs a much reduced number of auxiliary variables but the optimal solutions are still achieved iteratively. Furthermore, it works as well as for max-min relay power optimization, for which SDR seems to be powerless for solution. Simulation shows the viability of the proposed approach.

This paper considers beamforming problem in relay-assisted multi-user communication and presents an efficient algorithm to solve Min-max optimization of information throughput under limited power resources. We use DC programming and penalty function method to handle the inherent non-convexity of the associated objective function and follow an iterative procedure to solve for the global optimal beamforming vector. Extensive simulations are performed to establish the effectiveness and superiority of the proposed algorithm over conventional techniques based on randomization.

Optimal training design and channel estimation for spatially correlated multi-user multi-input multi-output with orthogonal frequency-division multiplexing (MIMO-OFDM) systems is still an open research topic of great interest. This study first applies tractable semi-definite programming (SDP) to obtain the optimal training signal for the general case of spatial channel correlations for multi-user MIMO-OFDM. In order to reduce the computational complexity of the SDP-based solution, an approximate solution in closed-form is then presented. For a special case of transmit correlations, an optimal solution in closed-form expression is also derived. Analytical and simulation results demonstrate the excellent performance of the proposed designs and their performance advantage over the existing equi-powered training designs.

Power allocations in an interference-limited wireless network for global maximization of the weighted sum throughput or global optimization of the minimum weighted rate among network links are not only important but also very hard optimization problems due to their nonconvexity nature. Recently developed methods are either unable to locate the global optimal solutions or prohibitively complex for practical applications. This paper exploits the d.c. (difference of two convex functions/sets) structure of either the objective function or constraints of these global optimization problems to develop efficient iterative algorithms with very low complexity. Numerical results demonstrate that the developed algorithms are able to locate the global optimal solutions by only a few iterations and they are superior to the previously-proposed methods in both performance and computation complexity.

Optimization problems of beamforming in multi-user amplify-and-forward (AF) wireless relay networks are indefinite (nonconvex) quadratic programs, which require effective computational solutions. Solutions to these problems have often been obtained by relaxing the original problems to semi-definite programs (SDPs) of convex optimization. Most existing works have claimed that these relaxed SDPs actually provide the optimal beamforming solutions. This paper, however, shows that this is not the case in many practical scenarios where SDPs fail to provide even a feasible beamforming solution. To fill this gap, we develop in this paper a nonsmooth optimization algorithm, which provides the optimal solution at low computational complexity.

Power allocations in an interference-limited wireless network for global maximization of the weighted sum throughput or global maximization of the minimum rate among network links are not only important but also very hard optimization problems due to their nonconvexity nature. Recently developed methods are either unable to locate the global optimal solutions or prohibitively complex for practical applications. This paper exploits the d.c. (difference of two convex functions/sets) structure of either the objective function or constraint of the these global optimization problems to develop efficient iterative algorithms with very low complexity. Numerical results demonstrate that the developed algorithms are able to locate the global optimal solutions by only a few iterations and they are superior to the previously-proposed methods in both performance and computation complexity.

Beamforming problems in amplify-forward (AF) wireless relay network can be formulated as nonconvex quadratically constrained quadratic programming (QCQP) problems which is very difficult to solve directly. Generally, by transforming a QCQP problem into a semi-definite program (SDP) and by relaxing rank-one constraints, the problem can be tackled essentially. If resulting matrices found after solving SDP problems are of rank-one then the task can be terminated. However, in some scenarios such as minimizing individual power constraints on relays, most of the rank-one dropped SDP solutions have rank higher than one. In this case, a nonsmooth reverse convex optimization technique is employed to solve the problem iteratively then rank-one solutions can be optimized numerically.

The paper is concerned with a multiuser communication network, which is assisted by multiple relays. It has been observed through our previous related works that the conventional simultaneous beamforming at parallel amply-and-forward (AF) relays is not quite effective and often infeasible to target practically desirable signal-to-interference-and-noise ratio (SINR) at the destinations. To overcome this shortage, we propose the time-division for multiple-user transmission to the relays so the later can perform beamforming on signals received from the individuals and then parallelly forward its combinations at once to the destinations. The optimal beamforming problem is a nonconvex quadratically constrained optimization, which is globally solved by our tailored algorithm of nonsmooth optimization. Its found global optimal solutions are shown very effective and over-perform other possible multi-user relay beamformings.

This paper discusses dynamic state estimation for nonlinear measurement model through distributed multisensor network under power constraints. For this scenario, we propose an optimized power allocation strategy based on semidefinite programming, that achieves minimum mean-squared error for the estimate subject to constraints on total transmit power. System nonlinearity is handled effectively with the help of distributed unscented Kalman filtering and linear fractional transformation. Furthermore, advantage of using multiple sensors over a single independent sensor is established through simulation results for tracking a maneuvering target.

This paper deals with optimized training sequences to estimate multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) channel states in the presence of spatial fading correlations. The optimization criterion is the entropy minimization of the error between the high multi-dimensional and correlated channel state and its estimator. The globally optimized training sequences are exactly solved by a semi-definite programming (SDP) of tractable computational complexity O((Mt(Mt+ 1)/2)2.5), where Mtis the transmit antenna number. With new tight two-sided bounds for the objective function, the optimal value of the generic SDP can be approximately solved by the standard water-filling algorithm. Intensive simulation results are provided to illustrate the performance of our methods.

The optimum multiuser detection (OMD) is a discrete (binary) optimization. The previously developed approaches often relax it by a semi-definite program (SDP) and then employ randomization for searching the optimal solution around the solution of this relaxed SDP. In this paper, we show the limited capacity of this SDP program, which at the end cannot give a better solution than the simple linear minimum mean square error detector (LMMSE). Our departure point is to express the problem as quadratic minimization over quadratic equality constraint (QMQE) or concave quadratic minimization over a box of continuous optimization (CQOB). The QMQE allows us to develop a nonsmooth optimization algorithm to locate the global optimal solution of OMD, while CQOB facilities effective confirmation of the solutions found by QMQE. Our intensive simulation clearly shows that the algorithm outperforms all previously developed algorithms while the computational burden is essentially reduced.

Optimal training design and channel estimation for spatially correlated multi-user multi-input multi-output with orthogonal frequency-division multiplexing (MIMO-OFDM) systems is still an open research topic of great interest. This paper applies tractable semi-definite programming to obtain the optimal training signal for the general case of spatial channel correlations for multi-user MIMO-OFDM. The optimal solution in closed-form expression is also derived for a special case of transmit correlations. Analytical and simulation results demonstrate the excellent performance of the proposed designs and their performance advantage over the equi-powered training designs.

The paper proposes a new approach for the design of linear phase finite impulse response (FIR) filters with discrete co-efficient values. This problem is a very hard combinatoric discrete optimization, which results in the prohibitive computational complexity for solution. In this paper, we first explicitly express the discrete coefficients of filters as indefinite quadratic but continuous constraints. We then develop an efficient iterative algorithm to tackle the nonconvex optimization problem to locate optimal discrete filter coefficients. By numerical simulation results, we show that our proposed method significantly outperform the methods using quantized coefficients of filters. We also provide an image sampling application to illustrate the performance of our designed filters.

This correspondence proposes an efficient semidefinite programming (SDP) method for the design of a class of linear phase finite impulse response triplet halfband filter banks whose filters have optimal frequency selectivity for a prescribed regularity order. The design problem is formulated as the minimization of the least square error subject to peak error constraints and regularity constraints. By using the linear matrix inequality characterization of the trigonometric semi-infinite constraints, it can then be exactly cast as a SDP problem with a small number of variables and, hence, can be solved efficiently. Several design examples of the triplet halfband filter bank are provided for illustration and comparison with previous works. Finally, the image coding performance of the filter bank is presented.

It is well-known that the optimal beamforming problems for cognitive multicast transmission are indefinite quadratic (nonconvex) optimization programs. The conventional approach is to reformulate them as convex semi-definite programs (SDPs) with additional rank-one (nonconvex and discontinuous) constraints. The rank-one constraints are then dropped for relaxed solutions, and randomization techniques are employed for solution search. In many practical cases, this approach fails to deliver satisfactory solutions, i.e., its found solutions are very far from the optimal ones. In contrast, in this paper we cast the optimal beamforming problems as SDPs with the additional reverse convex (but continuous) constraints. An efficient algorithm of nonsmooth optimization is then proposed for seeking the optimal solution. Our simulation results show that the proposed approach yields almost global optimal solutions with much less computational load than the mentioned conventional one.

We revisit robust complex- and mixed-μ synthesis problems based on upper bounds and show that they can be recast as specially structured controller design programs. The proposed reformulations suggest a streamlined handling of μ synthesis problems using recently developed (local) nonsmooth optimization methods where both scalings or multipliers and a controller of given structure are obtained simultaneously. A first cut of the nonsmooth code for structured H∞synthesis is made available through the MATLAB R2010b Prerelease, Robust Control Toolbox Version 3.5 developed by The MathWorks, Inc.

A design method of a linear-phased, two-dimensional (2-D), two-fold symmetric circular shaped filter is presented in this paper. Although the proposed method designs a non-separable filter, its implementation has linear complexity. The shape of the passband and the stopband is expressed in terms of level sets of second order trigonometric polynomials. This enables the transformation of the filter specifications to a Semi-Definite Program (SDP) of moderate dimension. The proposed filter outperforms currently available filter design methods. We present a performance comparison, as well as a homomorphic processing image enhancement example to illustrate the effectiveness of this method.

The optimal beamforming for cognitive multicast transmission is nonconvex rank-one constrained optimization problem. For a solution, a popular method is the combination of relaxed convex semi-definite programming, where the rank-one constraint is dropped, and randomization. We show that in many cases, this method cannot give satisfactory solutions. As an initial step, we develop a simple alternative method, which gives much better solutions. Our simulation confirms this fact.

This paper presents an efficient technique for power allocation to the sensor nodes in a nonlinear sensor network (NSN). We minimize mean square error of the estimation of a random scalar parameter subject to a constraint on total amount of power consumed by the sensor nodes. This estimation is carried out at fusion center (FC) which receives the local observations from the sensors located at different positions. We convert the optimization problem into a convex one, and then use semidefinite programming to find the global optimal solution. The simulation results show that our approach outperforms the previous work both for the channel with white noise and the one with colored noise. The proposed strategy also gives better results in case of nonlinear model when compared to the strategy of assigning equal power to sensor nodes.

Wireless amplify-and-forward relay networks in which the source communicates with the relays and destination in the first phase and the relays simultaneously forward signals to the destination in the second phase over uncorrelated Rayleigh fading channels are considered. We examine the scenario in which each relay only knows the perfect information of its source-relay channel while the destination knows the exact information of the relay-destination channels and the statistics of the source-relay channels. Based on a combiner developed at the destination, we propose an efficient beamforming scheme at the relays and develop its quantized version using Lloyd's algorithm to work with a limited-rate feedback channel. Simulation results show that the non-orthogonal relaying with the proposed beamforming scheme outperforms the orthogonal relaying with power allocation in terms of the ergodic capacity. In terms of the signal-to-noise ratio, the non-orthogonal scheme also becomes superior to the orthogonal scheme when the number of quantization regions increases.

This paper develops an amplify-and-forward relaying scheme for multiuser wireless cooperative networks under frequency-selective block-fading. Single-carrier frequency division multiple-access with frequency-domain equalization technique is employed at both the relay and destination to combat the inter-block and inter-symbol interference caused by multipath propagation. With the assumption that the full channel state information (CSI) is available at the destination, the relay only knows the uplink channels while no CSI is available at the sources, two power allocation schemes are developed for the relay: (i) to minimize the total transmit power at the relay while maintaining the signal-to-interference-plus-noise ratio (SINR) for each user at the destination above a certain level, and (ii) to maximize the worst SINR among all the users subject to a constraint on total relay power. In the first problem, it is shown that SINR adaptation is needed not only to guarantee a feasible solution but also to significantly reduce the transmit power at the relay for certain channel conditions. In the second problem, a flexible multi-level water-filling scheme is developed, which can be easily modified to adapt to the different channel conditions as well as different quality-of-service provision strategies.

Only one asymptotic training design for a special case of channel correlation was proposed in the literature for spatially correlated multiple-input multiple-output with orthogonal frequency-division multiplexing (MIMO-OFDM) systems. To fill this gap, this letter applies tractable semi-definite programming (SDP) to obtain the optimal superimposed training signals for the general case of channel correlation. For a more efficient computation, two approximate designs are also proposed. Simulation results demonstrate the efficiency of our approach and its advantage over the asymptotic design.

In this paper, the training sequence design for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems under the minimum mean square error (MMSE) criterion is addressed. The optimal training sequence for channel estimation in spatially correlated MIMO-OFDM systems was not known for an arbitrary signal-to-noise ratio (SNR). Only one class of training sequences was proposed in the literature in which the power allocation is given only for the extreme conditions of low and high SNRs. The current paper presents a necessary and sufficient condition for the optimal training sequence, and reformulates the training design problem as a convex optimization problem whose optimal solution is efficiently solved. In addition, tight upper bounds for MMSE and resulting low complexity iterative algorithms with the closed-form expression in iterations to find the optimum training sequence are derived. Simulation results confirm the superiority of the proposed design over the existing one in terms of both MSE estimation and BER performance. The proposed methods are also shown to be robust with respect to the spatial correlation mismatch at the transmitter.

This paper develops an amplify-and-forward relaying technique for multiuser wireless cooperative networks under frequency-selective block-fading. Single-carrier frequency division multiple-access with frequency-domain equalization technique is employed at both the relay and destination to combat the inter-block and inter-symbol interference caused by multipath propagation. With the assumption that the full channel state information (CSI) is available at the destination, the relay only knows the uplink channels while no CSI is available at the sources, two power allocation schemes are obtained: (i) to minimize the total transmit power at the relay while maintaining the signal-to-interference-plus-noise ratio (SINR) for each user at the destination above a certain level, and (ii) to maximize the worst SINR among all the users subject to a constraint on total relay transmit power. Analysis and simulation results are provided to illustrate the effectiveness of the proposed schemes.

This paper proposes a systematic approach for the design of a general class of analog infinite-impulse-response (IIR) filters, which includes all well-known classical analog filters as a special case. All specifications including the conventional ones and also filter flatness degrees are explicitly incorporated into design process. Several numerical examples are presented to demonstrate the efficiency and flexibility of the proposed method.

In this paper, we propose Bayesian filtering technique for continuous-time dynamical models with sampled-data measurements using the linear fractional transformation (LFT) model which transforms the nonlinear state space model into an exact equivalent linear model with a simple nonlinear feedback loop. The linear model is amenable to Euler discretization. Simulation results demonstrate that the proposed filtering technique gives better approximation and tracking performance than the unscented Kalman filter (UKF) which diverges for highly nonlinear problems.

The optimal training sequence for channel estimation in spatially correlated multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems has not been found for an arbitrary signal-to-noise ratio (SNR). Only one class of training sequences was proposed in the literature in which the power allocation is given only for the extreme conditions of low and high SNR. Provided in this paper are (i) a necessary and sufficient condition for the optimal training sequence together with a convex programming to find the solution, and (ii) efficient procedures to find the optimal training sequence. Simulation results confirms the superiority of the proposed design over the existing one.

This paper considers a communication network with multiple pairs of source and destination, assisted by multiple relays. It is assumed that perfect channel state information (CSI) is available at the relays. In a two-stage AF protocol, all the sources broadcast their signals to all the relays in the first stage. The received signal at each relay is processed by a beamforming weight and then re-broadcasted to all the destinations at the same time with other relays in the second stage. The focus is to find the optimal beamforming weights to meet a given set of target signal-to-interference-and-noise ratio (SINR) at the destinations, while minimizing the total transmitted power at the relays. We show that this problem can be formulated as a nonconvex quadratically constrained quadratic program (QCQP). Through relaxations, the problem can be solved efficiently by convex programming.

This paper derives an optimal power allocation (PA) to maximize the effective average signal-to-noise ratio (SNR) of distributed space-time coding (DSTC) in wireless relay networks, where the locations of the relays can be anywhere between the source and destination. It is first shown that in maximizing the average SNR, not all the relays might be active, and hence the code performance might be compromised. The amount of fading is then introduced for the relay networks and used as a constraint to derive a novel PA scheme. This new PA is shown to obtain the maximum diversity order of both noncoherent and coherent DSTC systems at high SNR.

Amplify-and-forward (AF) wireless relay networks in which the source communicates with the relays and destination in the first phase and the relays forward signals to the destination in the second phase over orthogonal and uncorrelated Rayleigh fading channels are considered. Convex programming is used to obtain optimal and approximately optimal power allocation (OPA) schemes to maximize the average signal-to-noise ratios(SNRs) at the output of the receiver filters under two different assumptions of partial channel state information (CSI). Analysis and simulation results demonstrate the superiority of the proposed power allocation schemes over the equal-power allocation scheme. Performance comparison to the extreme cases of (i) direct transmission between the source and destination and (ii) having full CSI is made to illustrate the gain and loss, respectively, of the proposed schemes. The impact of power allocation between the source and the relays is also investigated by computer simulation.

By using Semi-Definite Programming (SDP) as a tool, a new deign for Two-Dimensional (2-D) Diamond-Shaped (DS) filters is developed. Surprisingly, the diamond shape of the filter is exactly expressed by using simple 2-D trigonometric polynomial curves of second order. In contrast to the high-order polynomial transformation based methods, the order of the designed filters is kept moderate with no performance sacrifices. Unlike conventional non-separable 2-D filters, the designed filters allow fast digital implementation despite being nonseparable and hence they are of low complexity. Numerical Simulations with application to quincunx image sampling are also performed to illustrate the viability of our method.

Optimal training design and channel estimation for spatially correlated multiple-input multiple-output systems with orthogonal frequency-division multiplexing (MIMO-OFDM) is still an open research topic of great interest. Only one asymptotic design for a special case of channel correlations was proposed in the literature. To fill this gap, this paper applies tractable semi- definite programming (SDP) to obtain the optimal superimposed training signals for the general case of channel correlations. To improve computational efficiency, an approximate design in closed-form is also proposed. This approximate design is formed by minimizing an upper bound of the channel estimation mean-square error. Since the superimposed training approach is taken, the derivation of an optimal non-redundancy precoder for data detection enhancement is also given. Analytical and simulation results demonstrate the excellent performance of the proposed designs and their superior performance compared to the previously proposed design.

The problems of designing signature sequences and power allocation policy for code-division multiple access (CDMA) are important and have been the subject of intensive research in recent years. Taking the maximization of the system information theoretic sum capacity as the design criterion, most of the previous works only consider the optimizations of signature sequences and power allocation separately. In contrast, this letter presents a jointly optimal design of signature sequences and power allocation for a CDMA system with fixed, but unequal channel gains and under the sum power constraint. The proposed design is of closed-form, and applicable for the general case of correlated signals and colored noise. Numerical results verify the superiority of the proposed design over the existing ones.

The problem of channel estimation for spatially correlated fading multiple-input multiple-output (MIMO) systems is considered. Based on the channel's second order statistic, the minimum mean-square error (MMSE) channel estimator that works with the superimposed training signal is first developed. The problem of designing the optimal superimposed signal is then addressed and solved with an iterative optimization algorithm. Results show that under the constraint of equal training power and bandwidth efficiency, our optimal design of the superimposed training signal leads to a significant reduction in channel estimation error when compared to the conventional design of time-multiplexing training, especially for slowly time-varying channels with a large coherence time. The issue of power allocation between the information-bearing and training signals for detection enhancement is also investigated. Simulation results demonstrate excellent bit-error-rate performance of orthogonal space-time block codes with our proposed channel estimation.

For a transfer function/filter F(ejω) of order n, Kalman-Yakubovich-Popov (KYP) lemma characterizes the intractable semi-infinite programming (SIP) condition F(e-jω)1 Θ [F(ejω)1]T≥ 0 ∀ ω in frequency domain by a tractable semi-definite programming (SDP) in state-space domain. Some recent results generalize this lemma to SDP for SIP of frequency selectivity (FS-SIP). All these SDP characterizations are given at the expense of the introduced Lyapunov matrix variable of dimension n × n, making them impractical for high order problem. Moreover, the existing SDP characterizations for FS-SIP do not allow to formulate synthesis/design problems as SDPs. In this paper, we propose a completely new SDP characterization of general FS-SIP, which is of moderate size and is free from Lyapunov variables. Extensive examples are provided to validate the effectiveness of our result.

A new design method for linear phase half-band diamond (DS) and fan-shaped (FS) 2-D filters is proposed. A general formulation for frequency mask constraints in different shapes using 2-D trigonometric curves is developed. This facilitates semi-definite programming (SDP) of moderate dimension for the design problem. Several examples are included to illustrate advantages of our method.

The paper presents an efficient semidefinite programming (SDP) based design for prototype filters of cosine-modulated filter banks (CMFBs). We consider a class of near-perfect reconstruction CMFBs with the linear phase prototype filter, which structurally eliminates the amplitude overall distortion. The prototype filter design problem is then formulated into a convex semi-infinite programming problem. Furthermore, to handle the semi-infinite constraints, we use the linear matrix inequality (LMI) characterization of positive trigonometric polynomials to cast the semi-infinite programming problem into SDP one. Finally, convex duality is applied to transform the SDP into another SDP with the minimal number of additional variables, which is efficiently solved. An additional advantage of the proposed method is that we can precisely control the filter specifications.

The problems of designing signature sequences and power allocation policy for code-division multiple access (CDMA) are important and have been the subject of intensive research in recent years. Two different criteria adopted in such design problems are the user capacity and the information-theoretic capacity. Regarding the maximization of the information-theoretic capacity, most of the previous works only consider the optimizations of signature sequences and power allocation separately. In contrast, this paper presents a jointly optimal design of signature sequences and power allocation under the sum power constraint. The proposed design is of closed-form and applicable for the general case of correlated signals and colored noise. Numerical results verify the superiority of the proposed design over the existing ones.

Exploiting channel state information at the transmitter and receiver to design an optimal linear precoder and decoder for a multiple-input multiple-output (MIMO) communication system is an active research area. The design is often based on the information rate criterion, that is to design the precoder and decoder such that the system capacity is maximized, subject to the average transmit power constraint. Although such an optimization problem has been considered intensively and there have been numerous proposals so far, they are not rigorously correct. We propose a mathematically rigorous framework for solving this optimization problem. Our proposed solution is applicable to both MIMO flat fading and frequency selective fading channels. Simulations verify the theoretical analysis.

A new design for linear phase diamond-shaped (DS) and circular-shaped (CS) 2D filters is developed. First, the frequency masks are efficiently constrained by 2D second-order trigonometric polynomials. Then semi-definite programming (SDP) of reasonably low dimension is employed to effectively express the filter specifications. Several numerical examples are provided to demonstrate the superior performance of our design in comparison with all other existing designs.

The paper proposes a new approach for the design of M-channel pseudo-quadrature mirror filter (QMF) banks. First, the convex hull of 2Mth band linear phase filters admitting linear phase spectral factors is analytically described by semidefinite programming (SDP). Then, the prototype filter design is cast into an SDP problem, which is efficiently solved. Design examples are presented to illustrate the effectiveness of the proposed method and to evaluate the design performance in comparison with the existing designs.

Based on convex programming, this paper presents the optimal training signal design for orthogonal frequency-division multiplexing (OFDM) under colored noise. An effective method for OFDM symbol detection with the use of preceding is also described. Both analytical and simulation results show that the proposed design and method can effectively estimate the channel and reliably detect the OFDM symbols. By working in the frequency domain rather than the time domain, the computation complexity of the proposed estimation algorithm is significantly reduced.

In recent years there has been much interest in the probability hypothesis density (PHD) filtering approach, an attractive alternative to tracking unknown numbers of targets and their states in the presence of data association uncertainty, clutter, noise, and miss-detection. In particular, it has been discovered that the PHD filter has a closed form solution under linear Gaussian assumptions on the target dynamics and birth. This finding opens up a new direction where the PHD filter can be practically implemented in an effective and reliable fashion. However, the previous work is not general enough to handle jump Markov systems (JMS), a popular approach to modeling maneuvering targets. In this paper, a closed form solution for the PHD filter with linear JMS is derived. Our simulations demonstrate that the proposed PHD filtering algorithm provides promising performance. In particular, the algorithm is capable of tracking multiple maneuvering targets that cross each other

In this paper, a new technique for designing linear phase 2-D filter based on semi-definite programming (SDP) is proposed. This approach allows the design of 2-D filters with accurate cut-off frequency, subject to hard bounds on the frequency response to be achieved on a standard computer. Using the notion of 2-D trigonometric curves, we generalize the 2-D trigonometric Markov-Lukacs theorem to identify the pass-band and the stop-band in the region of support. The 2-D filter specifications are expressed as linear matrix inequalities. We also exploit convex duality to derive SDP formulations of reduced dimensions. Numerical examples illustrating the advantages of our method are also presented

A new design method for complex-valued two-channel FIR filter banks with both orthogonality and symmetry properties is developed. Based on a novel linear matrix inequality (LMI) characterization of trigonometric curves, the optimal design of the perfect reconstruction filter bank is reformulated as a semi-definite programme. The dimension of the resulting semi-definite programme is further reduced by exploiting the strong convex duality. Consequently, the globally optimal solution can be effectively found for any practical filter length and desired regularity order

A new efficient method is developed for optimal maximum likelihood (ML) decoding of an arbitrary binary linear code based on data received from a Gaussian channel. The decoding algorithm is based on minimization of a difference of two monotonic objective functions subject to the 0-1 constraint of bit variables. The iterative process converges to the global optimal ML solution after a finite number of steps. The proposed algorithm's computational complexity depends on the input sequence length k which is much less than the codeword length n, especially for codes with small code rates. The viability of the developed method is verified through simulations on different coding schemes

The problem of designing a jointly optimal linear precoder and decoder for a multiple-input multiple-output (MIMO) channel has received much interest recently. However, most existing works only deal with white input signal. When the input signal is colored, pre-whitening and its inverse operation are often applied prior to precoding and after decoding, respectively. Consequently, the precoder and decoder are no longer optimal with respect to the original colored signal. In this paper, we propose a closed-form solution for optimal linear precoder and decoder for colored input signal. Our approach is based on minimizing the symbol mean squared error under an average output power constraint, and is applicable to both MIMO flat fading and frequency selective fading channels. Simulations show the advantage of our solution over prewhitening-based method.

In this paper, we develop a new linear matrix inequality (LMI) technique, which is practical for solutions of the general trigonometric semi-infinite linear constraint (TSIC) of competitive orders. Based on the new full LMI characterization for the convex hull of a trigonometric curve, it is shown that the semi-infinite optimization problem involving TSIC can be solved by an LMI optimization problem with additional variables of dimension just n, the order of the trigonometric curve. Our solution method is very robust which allows us to address almost all practical filter design problems. Unlike most previous works involving several complex mathematical tools, our derivation arguments are based on simple results of the convex analysis and some formal elementary transforms. Furthermore, many filter/filterbank design problems can be reformulated as the optimization of linear/convex quadratic objectives over the TSIC. Based on this reformulation, these problems can be equivalently reduced to LMI optimization problems with the minimal size. Our examples of designing up to 1200-tap filters verifies the viability of our formulation.

A new fuzzy modeling based on fuzzy linear fractional transformations model is introduced. This new representation is shown to be a flexible tool for handling complicated nonlinear models. Particularly, the new fuzzy model provides an efficient and tractable way to handle the output feedback parallel distributed compensation problem. We demonstrate that this problem can be given a linear matrix inequality characterization and hence is immediately solvable through available semidefinite programming codes. The capabilities of the new fuzzy modeling is illustrated through numerical examples.

The advantage of IIR filters over FIR ones is that the former require a much lower order to obtain the desired response specifications. However, the existing deterministic techniques for IIR filter bank design based on heuristic usually lead to too high order IIR filters and thus cannot be practically used. In this paper, we propose new method to solve the low-order IIR filter bank design, which is based on linear matrix inequalities (LMI) optimization. Our focus is the QMF bank design, although other IIR filter related problems can be treated and solved in similar way.

This paper proposes different parameterized linear matrix inequality (PLMI) characterizations for fuzzy control systems. These PLMI characterizations are, in turn, relaxed into pure LMI programs, which provides tractable and effective techniques for the design of suboptimal fuzzy control systems. The advantages of the proposed methods over earlier ones are then discussed and illustrated through numerical examples and simulations.