Integrated sensing and communication (ISAC) beamforming designs typically rely on the true sensing parameters which can hardly be achieved. In this work, we present a statistically robust ISAC beamforming design to combat the performance degradation caused by the random target and clutter parameter estimation errors. Specifically, our goal is to maximize the radar output signal-to-interference-plus-noise ratio (SINR) averaged over angle and reflection coefficient estimation errors, while fulfilling the communication users’ SINR requirements. To solve this challenging stochastic optimization problem, we develop an efficient majorization-minimization (MM)-based algorithm. In particular, a semi-closed form solution is derived in each iteration for the single-user case. Simulation results show that, under the same communication constraints, our proposed algorithm outperforms the traditional non-robust designs in terms of the detection probability, and even approaches the clairvoyant scheme with known parameters.

We investigate the communication data enhanced direct position determination (DPD) for uplink multi-antenna orthogonal frequency division multiplexing (OFDM) systems. Different from prior related works, both pilot and unknown data signals are employed to estimate the positions of the mobile station (MS) and the virtual points corresponding to non-line-ofsight (NLOS) paths. To solve the novel localization problem, we propose a space alternating generalized expectation maximization (SAGE) based algorithm which regards the unknown data as latent variables. We further analyze the localization CramerRao bound (CRB) that incorporates the impact of communication data. Simulation results and the CRB analysis verify the remarkable localization accuracy improvement achieved by employing data signals. Moreover, the localization performance is also shown to approach the communication data aware CRB under relatively high signal-to-noise ratios (SNRs).

We consider the transmit waveform design for a collocated multiple-input multiple-output (MIMO) radar system, where one-bit analog-to-digital converters (ADCs) are deployed to enable a low-lost and power-efficient hardware implementation. Focusing on improving the target parameter estimation performance, we first derive a novel one-bit Cramér-Rao bound (CRB) metric by exploiting the Bussgang-based linear signal model and the worst-case Gaussian assumption. Then, based on the maximum likelihood principle, we develop a practical one-bit parameter estimation method to approach the derived CRB performance. Next, by minimizing the above one-bit CRB objective subject to a total power constraint, we formulate a transmit waveform optimization problem, which is highly nonconvex due to the one-bit quantization. To solve this problem, a majorization-minimization framework integrated with a projected gradient descent (MMPGD) algorithm is carefully designed whose convergence and complexity analysis are also provided. Finally, numerical results substantiate the tightness of the proposed one-bit CRB and the effectiveness of the MMPGD algorithm, where clear performance improvements can be achieved compared to the existing benchmark schemes.

In this paper, we study the transmit beamformer design to minimize the weighted sum finite blocklength (FBL) decoding error probability for a downlink multiuser multiple-input single-output (MISO) ultra reliable and low-latency communication (URLLC) system. Since the Gaussian Q-function in the design objective does not admit a closed form, the considered problem is challenging to solve. In order to handle the complicated problem, we first obtain its reformulation via a tight Gaussian Q-function approximation, which is then solved via the majorization-minimization (MM) technique tailored for the reformulated problem. Moreover, in order to further reduce the computational complexity, we first obtain the optimal structure of the transmit beamformer for the original problem, based on which we establish a neural network solution which avoids solving convex problems. Furthermore, we also investigate the extension to the statistically robust beamforming design with the imperfect channel state information (CSI). Simulation results verify the clear performance advantages of the proposed solutions over several state-of-the-art schemes as well as widely used beamforming schemes such as regularized zero-forcing (RZF) precoding in terms of the FBL decoding error probability. Moreover, the low-complexity solutions can effectively approach the proposed MM-based algorithms with much reduced computational costs.

Integrated sensing and communication (ISAC) enabled by millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) technologies is envisioned to be a promising candidate for future wireless systems. In this paper, we study the mmWave massive MIMO aided ISAC transceiver design with one-bit digital-to-analog converters (DACs) and one-bit analog-to-digital converters (ADCs) for reduced hardware complexity and power consumption. First, we develop a novel one-bit target detector based on the Bussgang decomposition, which fully exploits the spatial correlation at the receiver side for performance enhancement. Then, we derive the detection probability and the false alarm probability of the proposed detector in closed forms, from which we establish an interesting relationship between the detection performance and a new signal-to-quantization-plus-interference-plus-noise ratio (SQINR) metric. Furthermore, we formulate a one-bit ISAC transceiver optimization problem by incorporating both the communication mean-squared error (MSE) and proposed sensing SQINR metrics into the objective function. To address the complicated discrete optimization, we propose an efficient alternating optimization framework embedded with a majorization-minimization (AOMM) algorithm with guaranteed convergence. Finally, extensive simulations are conducted which confirm the validity of our one-bit detection performance analysis and show that the proposed detector outperforms a recent solution relying on the low input signal-to-noise/interference-to-noise ratio (LIS) assumption. Moreover, the proposed ISAC transceiver design can achieve excellent communication and sensing performances with acceptable complexity.

In this paper, we focus on the decoding error probability based beamforming design for a downlink multiantenna multiuser ultra reliable and low-latency communication (URLLC) system. We first optimize the transmit beamformer to minimize the maximum finite blocklength (FBL) decoding error probability under block fading channels, which turns out to be a complicated nonconvex problem with a non-closed-form objective function. A successive convex approximation (SCA)-based algorithm is developed to determine a high-quality solution. Moreover, in order to reduce the computational complexity of the proposed algorithm, we perform the downlink beamforming optimization by solving a simpler virtual uplink problem. The complexity of the resultant solution only scales linearly with the number of transmit antennas. Furthermore, a special case with quasi-static channels is investigated, where the proposed SCA and the uplink-downlink duality based solutions for the block fading channel can be simplified in a non-trivial manner. Simulation results verify the performance superiorities of the proposed algorithms in the context of short packet communication. In particular, the proposed designs outperform conventional regularized zero-forcing (RZF) and max-min signal-to-interference-plus-noise ratio (SINR) beamforming by evident gains in terms of the FBL decoding error probability.

Cost-effective phase noise (PN) compensation is essential for orthogonal frequency division multiplexing (OFDM) systems, especially in millimeter-wave (mmWave) communications. In this paper, we propose an intelligent semi-blind receiver design for PN-affected OFDM systems. Specifically, we first formulate a joint channel estimation, PN compensation, and data detection problem in the time domain, accounting for the constant modulus constraint of PN and discrete constraint of the data. To address the constrained non-convex problem, a low-complexity receiver based on the alternating direction method of multipliers (ADMM) and minorization-maximization (MM) is proposed. Subsequently, by adopting the deep unfolding technique, we present an intelligent receiver to avoid cumbersome parameter selection and also accelerate the algorithm convergence. Simulation results validate the clear performance superiority of our proposed design over existing schemes. Moreover, the complexity per iteration/layer is as low as O(N log2N) with N being the number of subcarriers.

This paper investigates the joint design of transmit waveform and receive filter for a collocated multipleinput multiple-output (MIMO) radar, where one-bit digitalto-analog converters (DACs) and one-bit analog-to-digital converters (ADCs) are employed to reduce the hardware cost and power consumption. We first derive a novel signal-toquantization-plus-interference-plus-noise ratio (SQINR) metric via a careful theoretical analysis to accurately characterize the one-bit MIMO radar performance. Then, we formulate a onebit transceiver optimization problem by maximizing the SQINR objective subject to binary DAC constraints. To handle the complicated mixed-integer problem, we propose an efficient penalty-based majorization-minimization (PMM) algorithm with guaranteed convergence. As validated via simulations, the proposed algorithm achieves a noticeable performance gain over the existing benchmark scheme based on a low input signal-to-noise/interference-to-noise ratio (LIS) assumption.

Reconfigurable intelligent surface (RIS) is a promising technique to improve the performance of future wireless communication systems at low energy consumption. To reap the potential benefits of RIS-aided beamforming, it is vital to enhance the accuracy of channel estimation. In this paper, we consider an RIS-aided multiuser system with non-ideal reflecting elements, each of which has a phase-dependent reflecting amplitude, and we aim to minimize the mean-squared error (MSE) of the channel estimation by jointly optimizing the training signals at the user equipments (UEs) and the reflection pattern at the RIS. As examples the least squares (LS) and linear minimum MSE (LMMSE) estimators are considered. The considered problems do not admit simple solution mainly due to the complicated constraints pertaining to the non-ideal RIS reflecting elements. As far as the LS criterion is concerned, we tackle this difficulty by first proving the optimality of orthogonal training symbols and then propose a majorization-minimization (MM)-based iterative method to design the reflection pattern, where a semi-closed form solution is obtained in each iteration. As for the LMMSE criterion, we address the joint training and reflection pattern optimization problem with an MM-based alternating algorithm, where a closed-form solution to the training symbols and a semi-closed form solution to the RIS reflecting coefficients are derived, respectively. Furthermore, an acceleration scheme is proposed to improve the convergence rate of the proposed MM algorithms. Finally, simulation results demonstrate the performance advantages of our proposed joint training and reflection pattern designs.

The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximuma posteriori(MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities.

A novel joint phase noise compensation and signal detection algorithm based on deep learning (DL) is proposed for orthogonal frequency division multiplexing (OFDM) systems under rapidly time-varying channels. Specifically, we first develop an alternating direction method of multipliers (ADMM) based solution to the difficult mixed-integer maximum likelihood (ML) estimation, along with a low-complexity ADMM solution that exploits the inherent feature of the inter-carrier interference (ICI). Furthermore, by unfolding the ADMM iterations, we present a model-driven network with trainable variables, which avoids cumbersome parameter search and also results in faster convergence. Simulation results demonstrate that our proposed DL-aided ADMM algorithms significantly outperform existing baselines in terms of BER performance with acceptable complexity.

We study the resource allocation for an uplink ultra reliable and low-latency communication (URLLC) system. The receive beamformer at the base station (BS) and the transmit power of users are jointly optimized to minimize the finite block-length (FBL) decoding error probability. To solve the difficult nonconvex problem, we first acquire a tractable reformulation by approximating the Gaussian Q-function. Although the resultant problem is still nonconvex, we propose an efficient algorithm where the optimal receive beamformer is obtained in closed form and the transmit powers of users are optimized by employing the majorization-minimization (MM) technique. Simulation results and complexity analysis verify the superiority of the proposed algorithm over existing benchmark schemes.

Interferences between uplink communication and downlink radar sensing can severely impair the performance of the time-division duplex (TDD) multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) system. In this paper, we propose an efficient joint downlink and uplink design to address this issue. Specifically, we jointly optimize the downlink radar sensing waveform and the uplink power allocation to minimize the weighted sum of sensing and communication mean squared errors (MSEs). Despite the non-convexity of the joint downlink and uplink design problem, we develop a majorization-minimization (MM) based algorithm to determine an efficient solution. The proposed algorithm is proved to be convergent. Moreover, as validated via simulation results, the proposed joint design can effectively mitigate the interference between uplink communication and radar sensing and provide remarkable performance gains over conventional separate design.

In this paper, we investigate the beamforming design for an integrated sensing and communication (ISAC) system involved full-duplex (FD) communications. Specifically, an FD ISAC base station (BS) performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problem is formulated to minimize the total transmit power of the system while ensuring the communication and sensing requirements. The downlink and uplink transmissions are tightly coupled, making the joint optimization challenging. To solve this intractable problem, we first determine the receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power and then suggest an iterative solution to the remaining problem. We demonstrate via numerical results that the optimized FD communication-based ISAC leads to power efficiency improvement compared to conventional ISAC with HD communication.

In this article, we consider an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and relay communication network consisting of a base station (BS), a UAV, and multiple ground users. The UAV acts as both a wireless power transmission source and an uplink communication relay. Specifically, an entire transmission period of the considered system is divided into two stages. In the first stage, the UAV transfers the power to the ground users along a well-optimized flight trajectory and meanwhile, the users transmit data to the UAV using the harvested energy. Subsequently, in the second stage, the UAV flies to the vicinity of the BS and forwards the data to the BS. For the purpose of minimizing the energy consumed by the UAV, we jointly optimize the time durations of the two stages, the UAV’s transmit powers for WPT and data forwarding, as well as its flight trajectory, subject to the constraints of the Quality of Service (QoS), the information forwarding, the energy causality, and the mobility of the UAV. The involved optimization problem is nonconvex and highly intractable. To this end, we propose an efficient alternating algorithm to iteratively solve the two subproblems with respect to the time durations of the two stages and the UAV’s transmit powers and trajectory, respectively. The first subproblem has a closed-form optimal solution and the second subproblem is handled by addressing a surrogate convex problem based on the technique of successive convex approximation. Finally, the simulation results confirm the superiority of our proposed algorithm.

Beamforming design has been widely investigated for integrated sensing and communication (ISAC) systems with full-duplex (FD) sensing and half-duplex (HD) communication, where the base station (BS) transmits and receives radar sensing signals simultaneously while the integrated communication operates in either downlink or uplink. To achieve higher spectral efficiency, in this paper, we extend existing ISAC beamforming design to a general case by considering the FD capability for both radar and communication. Specifically, we consider an FD ISAC system, where the BS performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problems are formulated under two criteria: power consumption minimization and sum rate maximization. The downlink and uplink transmissions are tightly coupled due to both the desired target echo and the undesired interference received at the BS, making the problems challenging. To handle these issues in both cases, we first determine the optimal receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power. Subsequently, we invoke these results to obtain equivalent optimization problems and propose iterative algorithms to solve them. In addition, we consider a special case under the power minimization criterion and propose an alternative low complexity design. Numerical results demonstrate that the optimized FD communication-based ISAC brings tremendous improvements in terms of both power efficiency and spectral efficiency compared to the conventional ISAC with HD communication.

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods.

Reconfigurable intelligent surfaces (RISs) can establish favorable wireless environments to combat the severe attenuation and blockages in millimeter-wave (mmWave) bands. However, to achieve the optimal enhancement of performance, the instantaneous channel state information (CSI) needs to be estimated at the cost of a large overhead that scales with the number of RIS elements and the number of users. In this paper, we design a quasi-static broad coverage at the RIS with the reduced overhead based on the statistical CSI. We propose a design framework to synthesize the power pattern reflected by the RIS that meets the customized requirements of broad coverage. For the communication of broadcast channels, we generalize the broad coverage of the single transmit stream to the scenario of multiple streams. Moreover, we employ the quasi-static broad coverage for a multiuser orthogonal frequency division multiplexing access (OFDMA) system, and derive the analytical expression of the downlink rate, which is proved to increase logarithmically with the power gain reflected by the RIS. By taking into account the overhead of channel estimation, the proposed quasi-static broad coverage even outperforms the design method that optimizes the RIS phases using the instantaneous CSI. Numerical simulations are conducted to verify these observations.

The trend of developing distributed multiple-input multiple-output (MIMO) cooperation has been growing for future wireless networks due to its potential of capacity improvement through network-level precoding. In massive MIMO applications, the overhead of channel state information (CSI) exchange among distributed transmitters is too large to make it possible in practical implementations. In this paper, we consider a cooperative multicell massive MIMO network with distributed regularized zero-forcing (RZF) precoding at each base station (BS), where a novel CSI exchange scheme is devised to reduce the interactive overhead. As a key finding of this work, we theoretically prove that it suffices to share the Gram matrix of local CSI among the cooperative BSs in order to achieve the same performance as a centralized cooperative MIMO network using the RZF precoding with global CSI sharing. The CSI exchange from each BS is thus reduced to a symmetric matrix that has a much smaller size than the full CSI and the amount of CSI exchange does NOT grow with the large number of antennas in massive MIMO. Specifically, based on the exchanged Gram matrices, we derive a decentralized RZF precoding design at each BS and develop both the optimal and suboptimal cooperative power allocation strategies, which achieve different performance and complexity tradeoffs. A virtual centralized power allocation is accomplished at each BS and the performance achieved by the proposed decentralized precoding is the same as the centralized benchmark scheme with full CSI exchange. These superiorities of the proposed schemes are verified through simulation results.

The vehicle-to-everything (V2X) technology has recently drawn attention from both academic and industrial areas. However, the openness of the wireless communication system makes it more vulnerable to identity impersonation and information tampering. How to employ the powerful radio frequency fingerprint (RFF) identification technology in V2X systems turns out to be a vital and challenging task. In this paper, we propose a novel RFF extraction method for Long Term Evolution-V2X (LTE-V2X) systems. In order to conquer the difficulty of extracting transmitter RFF in the presence of wireless channel and receiver noise, we first estimate the wireless channel which excludes the RFF. Then, we remove the impact of the wireless channel based on the channel estimate and obtain initial RFF features. Finally, we conduct RFF denoising to enhance the quality of the initial RFF. Simulation and experiment results both demonstrate that our proposed RFF extraction scheme achieves a high identification accuracy. Furthermore, the performance is also robust to the vehicle speed.

Coarse quantization by using low-resolution analog-to-digital converters (ADCs) is an attractive approach to relieve the burden of power consumption and hardware cost of implementing massive multiple-input multiple-output (MIMO) systems. In this article, we analyze the uplink spectral efficiency of a multiuser massive MIMO system with low-resolution ADCs in the context of orthogonal frequency division multiplexing (OFDM) under multipath channels. Firstly, we develop an efficient pilot scheme which results in a constant average power of the quantization noise for different channel delay power spectrums and also minimizes the mean squared error of channel estimation. Then a tight approximation of the uplink achievable rate is derived in a closed form considering both perfect channel state information (CSI) and estimated CSI. Then we analyze the impact of multipath channels on the system performance. Under perfect CSI, we discover that an increment of multipath taps has a positive impact on compensating the performance degradation due to the quantization noise. Under imperfect CSI, the most beneficial channel is uniformly distributed over a specific number of taps. Simulations are conducted to verify our analytical results.

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

Unmanned aerial vehicle (UAV)-assisted communication is a promising technology in future wireless communication networks. UAVs can not only help offload data traffic from ground base stations (GBSs) but also improve the Quality of Service (QoS) of cell-edge users (CEUs). In this article, we consider the enhancement of cell-edge communications through a mobile relay, i.e., UAV, in multicell networks. During each transmission period, GBSs first send data to the UAV, and then the UAV forwards its received data to CEUs according to a certain association strategy. In order to maximize the sum rate of all CEUs, we jointly optimize the UAV mobility management, including trajectory, velocity, and acceleration, and association strategy of CEUs to the UAV, subject to minimum rate requirements of CEUs, mobility constraints of the UAV, and causal buffer constraints in practice. To address the mixed-integer nonconvex problem, we transform it into two convex subproblems by applying tight bounds and relaxations. An iterative algorithm is proposed to solve the two subproblems in an alternating manner. Numerical results show that the proposed algorithm achieves higher rates of CEUs as compared with the existing benchmark schemes.

In this paper, we study the hybrid precoding structures over limited feedback channels for massive multiuser multiple-input multiple-output (MIMO) systems. We focus on the system performance of hybrid precoding under a more realistic hardware network model, particularly, with inevitable dissipation. The effect of quantized analog and digital precoding is characterized. We investigate the spectral efficiencies of two typical hybrid precoding structures, i.e., the sub-connected structure and the fully connected structure. It is revealed that increasing signal power can compensate for the performance loss incurred by quantized analog precoding. In addition, by capturing the nature of the effective channels for hybrid processing, we employ a channel correlation-based codebook and demonstrate that the codebook shows a great advantage over the conventional random vector quantization codebook. It is also discovered that, if the channel correlation-based codebook is utilized, the sub-connected structure always outperforms the fully connected structure in either massive MIMO or low signal-to-noise ratio scenarios; otherwise, the fully-connected structrue achieves better performance. Simulation results under both Rayleigh fading channels and millimeter wave (mm-wave) channels verify the conclusions above.

In this paper, we study the hybrid precoding structures for massive multiuser multiple-input multiple-output (MIMO) systems. Particularly, the practical hardware network models with insertion loss are developed. The achievable rates of two typical hybrid precoding structures, the fully-connected structure and the sub- connected structure, are investigated, from which we discover that the sub-connected structure always outperforms the fully-connected structure in terms of the achievable rate in massive MIMO. We further characterize the effect of quantized analog precoding which indicates that the subconnected structure is able to achieve better performance with fewer feedback bits than the fully-connected structure. We also propose a channel statistics-based codebook used for the digital precoding stage which is more suitable for hybrid precoding systems than the conventional random vector quantization (RVQ) codebook.

Indoor positioning for visible light communication (VLC) has gained significant attentions recently with the popularity of light-emitting diodes (LEDs). In this paper, we consider a typical application of VLC by proposing a three-dimensional positioning scheme for a target terminal equipped with multiple photodiodes (PDs). Given the relative coordinates between the target terminal and receiving PDs along with positions of fixed transmitting LEDs, precise location estimation of the terminal device can be achieved via measuring received signal strength (RSS) through line-of-sight (LoS) channels. Moreover, multipath reflections from interior walls are considered as a major interference in non-LoS environment. It is discovered that the positioning error increases linearly with respect to the reflection coefficient of walls, which also verified by simulation results. The positioning error is achieved in millimeter scale under an ideal condition and in decimeter scale with multipath reflections.

This paper considers the optimal energy efficient association for HetNets by applying almost blank subframes (ABSs) techniques. Aiming at maximizing energy efficiency (EE) without the quality of service (QoS) constraints, we obtain a closed-form optimal solution, which indicates that the optimal choice of blank resource block (RB) fraction for EE maximization is binary without individual user QoS requirements. We further incorporate QoS constraints and equivalently transform the corresponding complicated fractional EE optimization problem to a single linear program (LP) by introducing some new auxiliary variables. Finally, we confirm the validity of the assumption that a user is served by at most one BS in a given RB both in theory and by numerical results.

We study transceiver optimization in the context of amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying with source-destination link. In particular, by applying the known results of the optimal destination receiver and relay precoder, we mainly focus on the source beamformer optimization which manifests itself as a difficult non-convex problem. To find a globally optimal solution to this problem, we first transform it into an equivalent solvable convex problem via semidefinite relaxation (SDR). Inspired by the SDR based approach, we conduct a further analysis and successfully derive a novel semi-closed form solution to the optimal source beamformer. It is interesting to observe that the solution possesses a generalized eigenmode transmission structure depending on a weighted sum of the Gram matrices of both source-relay and source-destination channels.

In this letter, we study physical-layer secure transmission for a multi-antenna relay system in the presence of multiple eavesdroppers, with emphasis on quality-of-service (QoS) based optimization. In particular, our ultimate goal is to minimize relay transmit power and meanwhile guarantee the fulfillment of QoS constraints with regard to both legitimate receiver and eavesdroppers. Instead of directly dealing with this intricate problem, we first prove that the optimal relay precoder possesses a generalized channel matching structure, which allows us to transform the original problem into a tractable semi-definite programming (SDP). We then rigorously verify the optimality of this approach, and further discuss its extension to the scenario with only channel statistics. Simulations are finally performed to demonstrate the superiority of the proposed optimal design.

This paper studies adaptive coordinated control of device-to-device (D2D) communications underlaying a cellular network with a multi-antenna base station (BS). We analyze the performance of cellular and D2D users under the channel imperfections including both quantization error and feedback delay. More specifically, we derive the received signal-to-interference-and-noise ratio (SINR) distributions of both D2D and cellular users. Based upon this, we further obtain a closed-form approximation of the achievable rate, which serves as a theoretical support for choosing transmit beamforming strategies adaptively.

We study the robust transceiver design for amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay systems with a direct link and imperfect channel state information (CSI). Under this circumstance, we aim at obtaining the globally optimal transceiver that minimizes the mean-squared error (MSE) of symbol detection. Specifically, given a source beamformer, we first derive closed-form expressions for the optimal relay precoder and destination receiver. Then, to tackle the intricate non-convex problem with respect to source beamformer, we develop an efficient approach involving one-dimensional search and semidefinite programming (SDP). We prove rigourously that the proposed method yields a globally optimal solution. Simulation results verify the pronounced performance provided by the proposed design.

Transceiver designs have been a key issue in guaranteeing the performance of multiple-input multiple-output (MIMO) relay systems, which are, however, often subject to imperfect channel state information (CSI). In this paper, we aim to design a robust MIMO transceiver for nonregenerative MIMO relay systems against imperfect CSI from a worst-case robust perspective. Specifically, we formulate the robust transceiver design, under the minimum mean-squared error (MMSE) criterion, as a minimax problem. Then, by decomposing the minimax problem into two subproblems with respect to the relay precoder and destination equalizer, respectively, we show that the optimal solution to each subproblem has a favorable channel-diagonalizing structure under some mild conditions. Based on this finding, we transform the two complex-matrix subproblems into their equivalent scalar forms, both of which are proven to be convex and can be efficiently solved by our proposed methods. We further propose an alternating algorithm to jointly optimize the precoder and equalizer that only requires scalar operations. Finally, the effectiveness of the proposed robust design is verified by simulation results.

In this paper, the multi-mode resource-constrained project scheduling problem (MRCPSP) is considered for makespan minimization, which leads to even utilization of machine capacity. According to the characteristics of the considered problem, a discrete particle swarm optimization (DPSO) is adapted, based on which a cooperative optimization method with multiple discrete particle swarms (CPSO) is proposed for MRCPSP. Two swarms are distinctively adopted to the mode assignment and activity sequencing sub-problem. Once a mode list and an activity list are obtained, the two swarms cooperatively search for better solutions. In other words, one swarm updates the mode list in terms of the given activity list and the activity list is improved by the updated mode list by the other swarm. As the same time, a local search procedure is investigated to balance the exploration and exploitation. Computation results of Project Scheduling Problem Library (PSPLIB) sets show CPSO is efficient to solve complicated combination optimization problems.

In this paper, we develop a novel joint transmit and receive design for multiuser MIMO (MU-MIMO) systems. The proposed scheme exploits the channel temporal correlation and incorporates the matched filter (MF) combining vector into the beamforming design. Considering the beamforming is optimized with the outdated channel state information (CSI) feedback, we design the beamformer by maximizing the conditional expectation of signal-to-leakage-and-noise ratio (SLNR) given that a delayed version of CSI feedback is available. In this way, the optimized beamforming is able to alleviate the performance degradation due to the CSI delay. Numerical results show that our proposed scheme achieves almost the same performance as conventional iterative algorithms but with much lower complexity.

In Spiking neural networks, information is encoded in separate spike times. The traditional gradient descent based learning algorithm (SpikeProp) trends to be trapped in local optima and cannot converge if the negative synaptic weights are allowed. In this paper, a cooperative PSO (Particle Swarm Optimization) method is proposed for its supervised learning. A simplified neural network structure is suggested. The CPSO-based learning method can improve both the weights of the spike neurons and the delays between the neurons. Both the positive and negative weights can be preserved by the biological neurons. Experiments on benchmark problems show the proposal is reliable and efficient for learning spike patterns.