Integrated sensing and communication (ISAC) systems have emerged as a promising solution to improve spectrum efficiency and enable functional convergence. However, ensuring secure information transmission while maintaining high-quality sensing performance remains a significant challenge. In this paper, we investigate an integrated sensing and covert communication (ISCC) system, in which a base station (BS) simultaneously serves multiple downlink users and senses malicious targets that may act as both potential eavesdroppers (Eves) and wardens. We propose a novel symbol-level precoding (SLP)-based waveform design for ISCC that achieves covert communication intrinsically, without requiring additional transmission resources such as artificial noise. The proposed design integrates symbol shaping to enhance reliability for legitimate users and noise shaping to obscure transmission activities from the targets. For imperfect channel state information (CSI), the framework incorporates bounded uncertainty models for user channels and target angles, yielding a more robust design. The resulting ISCC waveform optimization problem is non-convex; to address this, we develop a low-complexity proximal distance algorithm (PDA) with closed-form updates under both PSK and QAM modulations. Simulation results demonstrate that the proposed method achieves superior covertness and sensing-communication performance with negligible degradation compared to traditional beamforming and conventional SLP approaches without noise-shaping mechanisms.

This paper will discuss a series of game-based adversarial jamming beamforming problems in the electronic countermeasures domain, and aims to develop a unified method to solve these problems as well as finding their shared solution. In that scenario, a smart multi-antenna jammer attempts to minimize the maximum signal-to-interference-plus-jamming-plus-noise ratio (SIJNR) of multiple single-antenna users, while a smart multi-antenna base station tries to maximize the minimum SIJNR. According to the transmission order of jammer and communication system, the jamming problem can be modeled by the Nash game, the jammer-leader Stackelberg game, or the jammer-follower Stackelberg game. However, the jammer may find it enormously difficult to detect the transmission order, so it can not decide which game to choose. To address this issue, this paper will show that the three games share an equilibrium by introducing three surrogate problems and exploring their relations, so that the information of transmission order is redundant. Subsequently, to find the shared equilibrium, the surrogate problems are transformed into the problems of the mathematical program with equilibrium constraint, and a convex-concave-procedure-based penalty method is developed to solve them. The numerical results verify the existence of the shared equilibrium and demonstrate the superior performance of the proposed method.

Integrating massive multiple-input multiple-output (mMIMO) systems with intelligent reflecting surfaces (IRS) presents a promising paradigm for enhancing physical-layer security (PLS) in wireless communications. However, deploying high-resolution quantizers in large-scale mMIMO arrays, along with numerous IRS elements, leads to substantial hardware complexity. To address these challenges, this paper proposes a cost-effective PLS design for IRS-assisted mMIMO systems by employing one-bit digital-to-analog converters (DACs). The focus is on jointly optimizing one-bit quantized precoding at the transmitter and constant-modulus phase shifts at the IRS to maximize the secrecy rate. This leads to a highly non-convex fractional secrecy rate maximization (SRM) problem. To efficiently solve this problem, two algorithms are proposed: (1) the WMMSE-PDD algorithm, which reformulates the SRM problem into a sequence of non-fractional programs with auxiliary variables using the weighted minimum mean-square error (WMMSE) method and solves them via the penalty dual decomposition (PDD) approach, achieving superior secrecy performance; and (2) the exact penalty product Riemannian gradient descent (EP-PRGD) algorithm, which transforms the SRM problem into an unconstrained optimization over a product Riemannian manifold, eliminating auxiliary variables and enabling faster convergence with a slight trade-off in secrecy performance. Both algorithms provide analytical solutions at each iteration and are proven to converge to Karush–Kuhn–Tucker (KKT) points. Simulation results confirm the effectiveness of the proposed methods and highlight their respective advantages.

Movable antennas (MAs) represent a novel approach that enables flexible adjustments to antenna positions, effectively altering the channel environment and thereby enhancing the performance of wireless communication systems. However, conventional MA implementations often adopt fully digital beamforming (FDB), which requires a dedicated RF chain for each antenna. This requirement significantly increase hardware costs, making such systems impractical for multi-antenna deployments. To address this, hardware-efficient analog beamforming (AB) offers a cost-effective alternative. This paper investigates the physical layer security (PLS) in an MA-enabled multiple-input single-output (MISO) communication system with an emphasis on AB. In this scenario, an MA-enabled transmitter with AB broadcasts common confidential information to a group of legitimate receivers, while a number of eavesdroppers overhear the transmission and attempt to intercept the information. Our objective is to maximize the multicast secrecy rate (MSR) by jointly optimizing the phase shifts of the AB and the positions of the MAs, subject to constraints on the movement area of the MAs and the constant modulus (CM) property of the analog phase shifters. This MSR maximization problem is highly challenging, as we have formally proven it to be NP-hard. To solve it efficiently, we propose a penalty constrained product manifold (PCPM) framework. Specifically, we first reformulate the position constraints as a penalty function, enabling unconstrained optimization on a product manifold space (PMS), and then propose a parallel conjugate gradient descent algorithm to efficiently update the variables. Simulation results demonstrate that MA-enabled systems with AB can achieve a well-balanced performance in terms of MSR and hardware costs.

Movable antennas (MA) allow flexible positioning within a specified region, enhancing wireless communication performance. This paper explores leveraging MA to improve physical layer security in analog beamforming (AB) systems. Specifically, we aim to maximize the secrecy rate by jointly optimizing the AB and MA positions under constant modulus (CM) and position constraints. To solve the resulting non-convex problem, we propose a penalty product manifold (PPM) method, which converts MA position constraints into a penalty function, reformulating the problem as unconstrained optimization on the product manifold space (PMS). We then derive a parallel conjugate gradient descent (PCGD) algorithm to efficiently update both AB and MA positions, providing analytical solutions at each step and ensuring convergence to a KKT point. Simulation results confirm that the MA system achieves a higher secrecy rate than systems with fixed antenna positions.

Designing dual-functional waveforms for integrated sensing and communication (ISAC) under low-resolution hardware constraints remains a significant challenge. In this paper, we propose a novel few-bit multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) signal design for uplink-downlink ISAC systems, where the transmitter is equipped with low-resolution digital-to-analog converters (DACs). To mitigate the impact of coarse quantization, we adopt a spatial Sigma-Delta (Σ∆) modulation scheme and formulate an optimization problem to maximize the worst-case signal-to-quantization-plus-noise ratio (SQNR) for target sensing, while satisfying symbol error probability (SEP) constraints for all communication users (CUs). A two-stage solution is proposed: a Σ∆ filter is first optimized, followed by a dual accelerated projected gradient (APG) algorithm for symbol-level precoding to generate the DFRC signal. Simulation results demonstrate the effectiveness of the proposed Σ∆ scheme for suppressing the quantization noise, providing promising performance for both sensing and communication tasks.

In this paper, we focus on the secure beamforming design in a beyond-diagonal reconfigurable intelligent surface (BD-RIS) assisted multiple-input multiple-output (MIMO) downlink network. We consider a scenario where a transmitter sends an information signal to a multi-antenna legitimate user while a multi-antenna eavesdropper attempts to intercept it. In this setting, a BD-RIS reconfigures the wireless environment to enhance secrecy. Aiming at the secrecy rate (SR) maximization, the joint optimization of transmit beamforming and BD-RIS reflection coefficients is formulated as a non-convex problem with power constraints on beamforming and symmetric and unitary constraints on BD-RIS reflection coefficients. To efficiently solve this challenging problem, a low-complexity framework that combines the augmented Lagrangian (AL) method and product manifold gradient descent (PMGD) algorithm is proposed to obtain a high-quality suboptimal solution. Numerical results show that BD-RIS-assisted systems achieve higher secrecy rates compared to conventional RIS-assisted systems.

Reconfigurable intelligent surfaces (RISs) hold significant promise for enhancing physical layer security (PLS). However, conventional RISs are typically modeled using diagonal scattering matrices, capturing only independent reflections from each reflecting element, which limits their flexibility in channel manipulation. In contrast, beyond-diagonal RISs (BD-RISs) employ non-diagonal scattering matrices enabled by active and tunable inter-element connections through a shared impedance network. This architecture significantly enhances channel shaping capabilities, creating new opportunities for advanced PLS techniques. This paper investigates PLS in a multiple-input multiple-output (MIMO) system assisted by BD-RISs, where a multi-antenna transmitter sends confidential information to a multi-antenna legitimate user while a multi-antenna eavesdropper attempts interception. To maximize the secrecy rate (SR), we formulate it as a non-convex optimization problem by jointly optimizing the transmit beamforming and BD-RIS REs under power and structural constraints. To solve this problem, we first introduce an auxiliary variable to decouple BD-RIS constraints. We then propose a low-complexity penalty product Riemannian conjugate gradient descent (P-PRCGD) method, which combines the augmented Lagrangian (AL) approach with the product manifold gradient descent (PMGD) method to obtain a Karush-Kuhn-Tucker (KKT) solution. Simulation results confirm that BD-RIS-assisted systems significantly outperform conventional RIS-assisted systems in PLS performance.

Physical layer security (PLS) is an important technology in wireless communication systems to safeguard communication privacy and security between transmitters and legitimate users. The integration of large-scale antenna arrays (LSAA) and intelligent reflecting surfaces (IRS) has emerged as a promising approach to enhance PLS. However, LSAA requires a dedicated radio frequency (RF) chain for each antenna element, and IRS comprises hundreds of reflecting micro-antennas, leading to increased hardware costs and power consumption. To address this, cost-effective solutions like constant modulus analog beamforming (CMAB) have gained attention. This paper investigates PLS in IRS-assisted communication systems with a focus on jointly designing the CMAB at the transmitter and phase shifts at the IRS to maximize the secrecy rate. The resulting secrecy rate maximization (SRM) problem is non-convex. To solve the problem efficiently, we propose two algorithms: 1) the time-efficient Dinkelbach-BSUM algorithm, which reformulates the fractional problem into a series of quadratic programs using the Dinkelbach method and solves them via block successive upper-bound minimization (BSUM), and 2) the product manifold conjugate gradient descent (PMCGD) algorithm, which provides a better solution at the cost of slightly higher computational time by transforming the problem into an unconstrained optimization on a Riemannian product manifold and solving it using the conjugate gradient descent (CGD) algorithm. Simulation results validate the effectiveness of the proposed algorithms and highlight their distinct advantages.

In this paper, we study the joint admission control and beamforming problem within a network where one multi-antenna base station tries to serve multiple single-antenna users. Unlike most existing studies which merely identify the users that should be denied, our work further suggest better positions in the neighbouring area for them where the previously-rejected users are allowed to access the network. To address this, we assume the knowledge of channel vectors within the network coverage area, and then jointly optimize the transmit beamformer and the channel vector associated with each user to minimize the network power cost, with a penalty measuring the mismatch between the optimized channel and the reference channel with current user position. Specifically, a non-zero mismatch means that the corresponding user is inadmissible at present, but may access network if moving to the position with the channel closest to the optimized result. Basically, this is a challenging non-convex problem, and we design a penalty dual decomposition (PDD)-based algorithm to iteratively achieve a stationary solution. The algorithm is highly efficient since a simple analytical solution is derived in each step.

While the Internet of Things (IoT) technology is booming and offers huge opportunities for information exchange, it also faces unprecedented security challenges. As an important complement to the physical-layer security technologies for IoT, radio frequency fingerprinting (RFF) is of great interest due to its difficulty in counterfeiting. Recently, many machine learning (ML)-based RFF algorithms have emerged. In particular, deep learning (DL) has shown great benefits in automatically extracting complex and subtle features from raw data with high-classification accuracy. However, DL algorithms face the computational cost problem as the difficulty of the RFF task and the size of the deep neural network have increased dramatically. To address the above challenge, this article proposes a novel cost-effective early-exit neural network consisting of a complex-valued neural network (CVNN) backbone with multiple random forest branches, called hybrid CVNN-RF. Unlike conventional studies that use a single fixed DL model to process all radio frequency (RF) samples, our hybrid CVNN-RF considers differences in the recognition difficulty of RF samples and introduces an early-exit mechanism to dynamically process the samples. When processing “easy” samples that can be well classified with high confidence, the hybrid CVNN-RF can end early at the random forest branch to reduce computational cost. Conversely, subsequent network layers will be activated to ensure accuracy. To further improve the early-exit rate, an automated multidimensional early-exit strategy is proposed to achieve scheduling control from multiple dimensions within the network depth and classification category. Finally, our experiments on the public ADS-B data set show that the proposed algorithm can reduce the computational cost by 83% while improving the accuracy by 1.6% under a classification task with 100 categories.

Base station (BS) activation is a widely-used approach to alleviate the system power cost for device maintenance. However, frequently switching on/off BSs may also introduce extra power and signaling costs, which motivates us to limit the BS switching frequency when performing BS activation. To address this, we study a problem of joint long-term BS activation and short-term beamforming (J-LTBA-STBF) in a network where multiple multi-antenna BSs cooperatively serve multiple single-antenna users. LTBA means that each BS’s active/inactive switching frequency is properly controlled to yield relatively stable BS-user association and limited switching power, while STBF lies in that due to the low cost of refreshing beamformers, the transmit beamformers are allowed to be updated frequently to maximally lower the transmit power. Following the idea, two efficient algorithms are designed, according to the data amount they request to start running, to optimize the active BSs and the BS transmit beamformers in multiple adjacent time slices. Consequently, the transmit, maintenance and switching powers can be well balanced to facilitate power-efficient communications. Numerical results validate the efficacies of the algorithms.

Abstract Robust beamforming is a commonly‐used solution to various array processing problems. Recently, with the inevitable and rapid increasing of array size, the complexity issue arising from high‐dimension matrix computations has become an urgent concern in algorithm design. In this paper, an efficient distributed robust beamforming algorithm is developed for large‐scale arrays. Specifically, the problem of robust minimum variance distortionless response (MVDR) beamforming with sidelobe level control is considered. By fitting the worst‐case reformulation of robust MVDR beamforming into the framework of alternating direction method of multipliers, the problem can be iteratively solved in a distributed manner, with each iteration being computed in closed form. Numerical results demonstrate that this design is more efficient than many related approaches.

Recently, various wireless Internet-of-Things devices and unmanned aerial vehicles have been frequently used to facilitate daily life. On the other hand, they can also pose serious threats to public security if maliciously used. A countermeasure against these threats is to transmit jamming signals to break the communication links between attacker and wireless devices. However, this is not easy since modern multi-device (multi-user) wireless communication systems are smart in avoiding jamming. Moreover, a relay is widely used to improve the jamming resistance. To successfully jam those malicious devices, we consider a jamming and anti-jamming zero-sum game, in which the attacker tries to maximize the sum-rate of a relay-assisted wireless system and the jammer tries to minimize it. Finding the equilibrium is challenging due to its non-convexity and complex structure. We address this problem in two cases. Specifically, in the single-device (single-user) case, we show that putting the whole jamming power to either relay or device achieves the Nash equilibrium. In the multi-device case, an efficient method is developed, which transforms this game into a min-max optimization problem and decouples it into two sub-problems by the hybrid block successive approximation method. The resultant sub-problems are solved by the multi-block alternating direction method of multipliers and the block successive upper-bound minimization method of multipliers, respectively. Then, a stationary point of the original problem can be iteratively achieved. Numerical results demonstrate that the two proposed jamming methods outperform many traditional methods.

Massive MIMO attains high spectral and power efficiency transmission by leveraging a large number of transmit antennas. However, to capture the benefits of massive MIMO, each antenna should be accompanied with a dedicated RF chain, and consequently, the hardware costs would scale up tremendously with the increase of the antennas. Cheap implementations of massive MIMO have recently gained considerable attention, and constant modulus (CM) signaling is seen as a promising solution, owing to its low peak-to-average power ratio (PAPR). This paper investigates the physical-layer (PHY) security in massive MIMO with an emphasis on the CM signaling. In particular, we consider a transmitter with massive antennas broadcast common confidential information to a group of legitimate receivers, and a number of eavesdroppers overhear the transmission and attempt to intercept the information. Our goal is to design the CM beamforming at the transmitter so that the multicast secrecy rate is maximized. This secrecy rate maximization (SRM) problem is generally NP-hard. To tackle it, two tractable approaches are developed. The first one employs the semidefinite relaxation (SDR) technique and the Charnes–Copper transformation to obtain a convex relaxation of the SRM problem. However, due to the dimension lifting of SDR, this approach is feasible only for small to medium antenna sizes. The second approach leverages the Dinkelbach method to work directly over the beamformer domain; a custom-build nonconvex alternating direction method of multipliers (ADMM) algorithm is proposed to efficiently perform each Dinkelbach update. Simulation results demonstrate that the second approach is computationally more efficient and can achieve nearly optimal performance when the number of antennas is large.

The max–min fairness (MMF) strategy has been widely employed to manage wireless networks since it guarantees fairness among users. However, with a large number of users awaiting service, the network tends to be congested and the quality‐of‐service (QoS) will degrade substantially. This motivates us to study the MMF problem jointly with the consideration of admission control. Specifically, the authors consider a downlink network consisting of a multi‐antenna base station (BS) and multiple single‐antenna users. By jointly optimising the admissible users and the BS transmit beamformers, they aim to maximise the minimum signal‐to‐interference‐plus‐noise‐ratio of the admissible users, such that high QoS and fairness can be guaranteed simultaneously for them. This problem is essentially NP‐hard, and hence they pursue an efficient approximate solution to it. To this end, they first reformulate this problem from the perspective of sparse optimisation, and then develop a low‐complexity algorithm to iteratively solve the approximate problem. Moreover, to facilitate the algorithm's implementation, they further recast the subproblem in each iteration, such that it fits into the framework of the alternating direction methods of multipliers. Finally, an efficient distributed algorithm is designed, with each step being simply computed in a closed form.

In most existing robust array beam pattern synthesis studies, the bounded-sphere model is used to describe the steering vector (SV) uncertainties. In this letter, instead of bounding the norm of SV perturbations as a whole, we explore the amplitude and phase perturbations of each SV element separately, thereby obtaining a tighter SV uncertainty model. On the basis of this model, we formulate the robust pattern synthesis problem from the perspective of the min-max optimization, which aims to minimize the maximum side lobe response, while preserving the main lobe response. However, this problem is difficult due to the infinitely many nonconvex constraints. As a compromise, we employ the worst-case criterion and recast the problem as a convex second-order cone program (SOCP). To solve the SOCP, we further design an alternating direction method of multipliers based algorithm, which is computationally efficient by coming up with closed-form solutions in each step.

Consider a network consisting of one multi-antenna base station (BS) and multiple pairs of multi-antenna user equipment's (UEs). For each UE pair, the communication between transmitter and receiver is established either through BS or via device-to-device (D2D) link. We assume that the D2D transmission and cellular transmission are equally prioritized and share the same resources. To improve the network throughput, we maximize the sum rate by jointly optimizing the transmission mode of each UE pair and the associated transceivers. Due to the NP-hardness of this problem, we first perform some efficient approximation to it and then design an iterative algorithm, which is guaranteed to converge to a stationary solution by solving a series of weighted minimum mean square error (WMMSE) problems. The proposed algorithm has two distinguishing features. First, it only solves the WMMSE problem inexactly in each iteration, which thereby has a simplified algorithm structure and accelerated convergence behavior than the classical one. Second, we further fit the WMMSE problem into the alternating direction method of multipliers (ADMM) framework, making it amenable to parallel and distributed computation. Finally, the approximated problem is solved efficiently and distributively, with simple closed-form solutions in each step.

This paper considers mobile computation offloading in fog-radio access networks (F-RAN), where multiple mobile users offload their computation tasks to the F-RAN through a number of fog nodes [a.k.a. enhanced remote radio heads (RRHs)]. In addition to communication capability, the fog nodes are also equipped with computational resources to provide computing services for users. Each user chooses one fog node to offload its task, while each fog node may simultaneously serve multiple users. Depending on computational burden at the fog nodes, the tasks may be completed at the fog nodes or further offloaded to the cloud via fronthaul links with limited capacities. To complete all the tasks as fast as possible, a joint optimization of radio and computational resources of F-RAN is proposed to minimize the maximum latency of all users. This problem is formulated as a mixed integer nonlinear program (MINP). We first show that the MINP can be reformulated as a continuous optimization problem with a difference-of-convex (DC) objective. Then, an inexact DC algorithm is proposed to handle the min-max problem with stationary convergence guarantee. Simulation results show that the proposed algorithm outperforms the minimum distance-based and the random-based offloading strategies.

In this paper, we consider how to maintain the communication quality for high-speed users in array transmission. Due to high user speed, the array transmission angle changes quickly. As a consequence, the phase shifters (beamformers) of traditional phase arrays need to be updated frequently to aim at the user, thus yielding high implementation cost. To alleviate this, we propose a novel frequency diverse array (FDA) approach, which intentionally introduces some frequency offsets across the array antennas to activate an angle-range-time dependent beampattern; i.e., the FDA beampattern peak automatically moves in space. This motivates us to carefully design FDA parameters such that the beampattern peak accompanies the quickly-moving users. To this end, we maximize the average beampattern gain along some given user trace by optimizing the frequency offsets. The block successive upper-bound minimization (BSUM) method is applied to obtain a stationary solution to this non-convex problem. Compared with phase array beamforming, the FDA approach maintains service quality for high-speed users by updating frequency offsets less frequently, thus reducing the implementation cost remarkably.

Transmit beamforming and artificial noise-based methods have been widely employed to achieve physical-layer (PHY) security. However, these approaches may fail to provide satisfactory secure performance if the channels of legitimate user (LU) and eavesdropper (Eve) are highly correlated, which usually occurs in the case of close-located LU and Eve. The goal of this paper is to address the PHY security problem for proximal LU and Eve in millimeter-wave transmissions. To this end, we propose a novel frequency diverse array (FDA) beamforming approach, which intentionally introduces some frequency offsets across array antennas to decouple the highly correlated channels of LU and Eve. By exploiting this decoupling capability, the FDA beamforming can degrade Eve's reception and thus enhance PHY security. Leveraging FDA beamforming, we aim to maximize the secrecy rate by jointly optimizing the frequency offsets and the transmit beamformer. This secrecy rate maximization problem is difficult due to the tightly coupled variables. However, we show that it can be reformulated into a form only depending on the frequency offsets. Building upon this reformulation, we further employ the block successive upper-bound minimization method to iteratively obtain a solution with stationary convergence guarantee. Numerical results demonstrate that FDA beamforming can provide higher secrecy rate than conventional beamforming, especially for proximal LU and Eve.

Consider a network consisting of one multi-antenna base station (BS) and multiple single-antenna users. With a lot of users awaiting service, the network tends to be congested and the quality of service (QoS) degrades remarkably. This promotes the research on user admission control; i.e., to guarantee QoS, the network serves only a subset of users and rejects the rest. In this paper, we consider a max-min fairness (MMF) problem based on joint admission control and beamforming. In particular, by jointly optimizing the admissible users and the transmit beamformers, we maximize the minimum signal-to-interference-plus-noise-ratio (SINR) of admissible users, such that high QoS and fairness can be guaranteed for them. This problem is essentially NP-hard, and hence we develop a low-complexity iterative deflation algorithm to obtain some efficient approximate solution. In each iteration, we improve the users' SINRs and drop the user with the lowest SINR-to-power ratio, until the given user number is achieved. To facilitate the algorithm implementation, especially in large-scale networks, we further employ the alternating direction method of multipliers (ADMM) to perform per-user optimization. Finally, an efficient distributed algorithm is developed, with each step being solved in closed form.

This paper considers the physical-layer secrecy design for full-duplex (FD) bidirectional communications in the presence of an eavesdropper (Eve). The goal of this work is to maximize the sum secrecy rate (SSR) of the bidirectional transmissions via appropriately designing the transmit covariance matrices at the legitimate nodes. To this end, we propose an alternating difference-of-concave (ADC) approach to iteratively optimizing the transmit covariance matrices. We show that each ADC iteration can be carried out efficiently with a semi-closed- form solution, and that every limit point of ADC iterations is a stationary solution of the SSR maximization problem. Besides the SSR maximization, this paper also deals with a robust SSR maximization problem to account for imperfect CSI of Eve. Assuming a moment-based random CSI error model (i.e., only mean and covariance of the error are known, but the exact distribution is not known), robust transmit designs based on Markov's inequality and the robust conic reformulation are developed. The efficacy of the proposed designs is demonstrated through simulations.

Consider a network that consists of one multi-antenna base station (BS) and multiple pairs of multi-antenna user equipments (UEs). In each UE pair, the communication between transmitter and receiver is established either through BS or via device-to-device (D2D) link. All the D2D transmission and the uplink transmission of BS relaying share the same resources, while causing interference to each other. To improve the network throughput, we consider a sum-rate maximization problem by jointly optimizing the transmission mode of each UE pair and the corresponding transceivers. Due to the NP-hardness of the problem, we seek for some efficient approximate solutions to it. To this end, we first reformulate the problem by the weighted MMSE (WMMSE) approach, and then fit it into the alternating direction method of multipliers (ADMM) framework. Finally, an efficient distributed algorithm, which converges to a stationary solution, is developed. In particular, each step of the algorithm can be computed in closed form, thus giving it very low complexity.

Consider the max-min problem for an uplink SIMO heterogeneous network, where the base stations (BS) are coordinated dynamically for joint reception under some backhaul overhead constraints. We formulate this problem in the perspective of joint power allocation, BS assignment and beamformer design, and develop an efficient algorithm based on alternating optimization. In particular, we transfer the joint BS assignment and beamformer design subproblem into a group LASSO problem by applying the alternating direction method of multipliers (ADMM). Consequently, the problem is solved in a partially distributed manner and in each iteration a simple closed-form solution is derived. Numerical simulations demonstrate the effectiveness and efficiency of the proposed algorithm.