Optimal Beamforming Research Articles

Preprint: Global, and local optimization beamforming for broadband sources.

This paper presents an alternative energy function for global optimization (GO) beamforming, tailored to acoustic broadband sources. Given that properties, such as the source location, multipole rotation, or flow conditions, are parameterized over the frequency, a cross spectral matrix-fitting can be performed for all frequencies at once. A numerical analysis shows that the nonlinear energy function for the standard GO problem is equivalent to the source's point spread function (PSF) and contains local minima at the locations of the grating lobes and side lobes. The energy function is improved with the proposed broadband energy, as it averages the PSF. Further, it simplifies the process of identifying sources and reconstructing their spectra from the results. The paper shows that the method is superior on synthetic monopoles compared to standard GO and CLEAN based on source coherence (CLEAN-SC). For real-world data, the results of the proposed method and CLEAN-SC are similar, and outperform standard GO. The main difference is that source assumption violations cause noisy maps for CLEAN-SC and cause wrong spectral estimations of the proposed method. By using reasonable initial values, the GO problem reduces to a local optimization problem with similar results. Further, the proposed method is able to identify synthetic multipoles with different pole amplitudes and unknown pole rotations.

Joint Optimization of IRS Location and Passive Beamforming for Enhanced Received Power

Joint Optimization of IRS Location and Passive Beamforming for Enhanced Received Power

Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming

Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming

Statistically Optimal Beamforming and Ergodic Capacity for RIS-Aided MISO Systems

Statistically Optimal Beamforming and Ergodic Capacity for RIS-Aided MISO Systems

RS-BNN: A Deep Learning Framework for the Optimal Beamforming Design of Rate-Splitting Multiple Access

RS-BNN: A Deep Learning Framework for the Optimal Beamforming Design of Rate-Splitting Multiple Access

Robust Beamforming Optimization Design for RIS-Aided MIMO Systems With Practical Phase Shift Model and Imperfect CSI

The ideal phase shift model and the acquisition of channel state information (CSI) are practically difficult to realize in reconfigurable intelligent surface (RIS) assisted communication systems. Therefore, these have greatly troubled the beamforming design of multiple-input multiple-output (MIMO) systems via RIS. This paper presents a robust beamforming design framework in the RIS-enhanced MIMO communication networks in the presence of considering practical phase shifts for the RIS and imperfect CSI of the cascade channels. More specifically, we propose an alternative optimization framework and adopt the distributed alternating direction method of multipliers (ADMM) method to solve the sum-rate maximization problem by combining the true environment model (TEM) and the mismatch environment model (MEM) with beamforming. In particular, the proposed scheme is also used for the MEM scenario. It shows that the sum rate of the ADMM scheme with MEM under low SNR is about 3 bps/Hz more than the BFP method, approximately 6 bps/Hz more than the APG method. Next, when the SNR is 5dB, the above three schemes are greater than SDR method. But, the above methods are smaller than SDR method in MEM. Then, the sum rate of the SDR method with the TEM is approximately 1.6 times of the MEM, while the CPU time of both is consistent with the computational complexity. Therefore, the differences between the two models affect the performance achieved by different methods. The proposed approach sheds a light to engineers working in wireless communication on enhancing the transmission robustness of existing wireless networks.

Hardware-Impaired Beamforming Optimization for Cooperative Double-IRS Aided Wireless Communications

Hardware-Impaired Beamforming Optimization for Cooperative Double-IRS Aided Wireless Communications

Active-Passive Beamforming Optimization for RIS-Aided Mmwave Joint Localization and Communication System

Active-Passive Beamforming Optimization for RIS-Aided Mmwave Joint Localization and Communication System

Collaborative Beamforming Optimization using Imperialist Competitive Algorithm

Collaborative Beamforming Optimization using Imperialist Competitive Algorithm

Joint active and passive beamforming optimization for IRS-assisted downlink MISO-URLLC in max–min fairness

Joint active and passive beamforming optimization for IRS-assisted downlink MISO-URLLC in max–min fairness

Distance-Angle Beamforming for Covert Communications via Frequency Diverse Array: Toward Two-Dimensional Covertness

In this paper, we study the beamforming schemes via the novel frequency diverse array (FDA) on enhancing covert communications performance. We first consider the ideal scenario where the channel state information (CSI) is perfectly known at the transmitter. Then we characterize the key role of minimizing the correlation of the communication and detection channels in boosting the covertness of the system, which also provides a theoretical design principle for FDA-specific carrier frequency scheduling. By exploiting the optimization framework block successive upper bound minimization (BSUM), we propose a frequency scheduling method which leads to a two-phase beamforming scheme to facilitate the covert transmission. Subsequently, we extend the scenario to the more practical one with partial CSI. By leveraging the convex hull, we manage to transform the formulated semi-infinite programming problem to an equivalent semi-definite one which can be solved optimally. In addition, we present a process to construct the optimal beamforming vector. To mitigate the channel correlations in this scenario, we generalize the steps of BSUM and propose an algorithm to schedule the frequencies efficiently. Afterwards, a three-phase robust beamforming scheme is summarized, which boosts the covert rate significantly. Numerical results are provided to demonstrate the superiority of the proposed schemes.

Joint power control and passive beamforming optimization in RIS-assisted anti-jamming communication

Joint power control and passive beamforming optimization in RIS-assisted anti-jamming communication

Joint IRS Selection and Passive Beamforming in Multiple IRS-UAV-Enhanced Anti-Jamming D2D Communication Networks

Intelligent reflective surfaces (IRSs) as low energy consumption and easy to attach devices have been widely applied in the field of anti-jamming recently. In particular, the combination of IRS and unmanned aerial vehicle (UAV), as IRS-UAV, further expands the scope of IRS services. In this paper, the joint IRS selection and beamforming optimization problem has been investigated in multiple IRS-UAV assisted anti-jamming D2D networks. To solve the above optimization problem, a distributed matching-based selection and Q-learning-based beamforming optimization algorithm (DMQ) was proposed. In detail, the optimization problem is decomposed into two subproblems, namely, the IRS selection subproblem is formulated as a non-commutative many-to-many matching game model to describe peer effects and uncertainty selection quotas, and the passive beamforming optimization subproblem is solved by a reinforcement algorithm to satisfy the complex environment. Numerical simulations confirm the convergence and near-optimal performance of the proposed scheme with lower latency and greater robustness.

Joint Beamforming and Backscatter Communication Design for Symbiotic Radio Networks

Symbiotic radio (SR) is a promising energy-, spectrum-and cost-efficient communication technology for Internet of Things. This paper considers an SR network (SRN) in which backscatter devices (BDs) communicate with a primary receiver (PR) by riding on ambient radio frequency carriers from a primary transmitter (PT). First, both the achievable primary rate and backscatter-link sum rate are derived for BDs adopting spatial-division-multiple-access (SDMA) scheme and dynamic time-division-multiple-access (TDMA) scheme, respectively. Then, two optimization problems are formulated to maximize the BDs’ sum rate, by jointly optimizing the PT’s beamforming matrix, and the BDs’ power reflection coefficients as well as the backscatter time allocation. For the single-antenna PR case, the optimal beamforming is obtained in semi-closed forms. For the multi-antenna PR case, to solve the non-convex problems with coupled variables, efficient iterative algorithms are proposed based on block coordinate descent and sequential convex approximation techniques. Finally, numerical results show that the proposed design enhances the SRN’s throughput significantly, and give useful insights on the BDs’ multiple-access schemes.

Computation Offloading and Beamforming Optimization for Energy Minimization in Wireless-Powered IRS-Assisted MEC

Intelligent reflecting surface (IRS) has been recently exploited as a symbiotic radio (SR) technology to improve energy-and spectral-efficiencies in wireless systems. In this paper, we consider a symbiotic IRS-assisted mobile edge computing (MEC) system that allows edge users to first harvest RF power from a hybrid access point (HAP) and then offload its computational workload to the MEC server associated with the HAP. We aim to minimize the HAP’s energy consumption by jointly optimizing the users’ offloading schemes, the HAP’s active beamforming, and the IRS’s passive beamforming strategies. We propose an optimization-driven hierarchical deep deterministic policy gradient (OH-DDPG) framework to decompose the energy minimization problem into the optimization and the learning sub-problems, respectively. The outer-loop DDPG learning method adapts the IRS’s passive beamforming strategy, while the inner-loop optimization deals with the other control variables with reduced dimensionality. Moreover, to improve the learning efficiency, we extend OH-DDPG to the multi-agent scenario. In particular, the HAP first estimates the users’ offloading strategy by the inner-loop optimization and shares it with all user agents. Then, each user agent refines its offloading decision using the DDPG algorithm independently. This can avoid signaling overhead among users and improve the multi-user learning efficiency. Simulation results show that the proposed OH-DDPG and the multi-user extension can achieve significant performance gains compared to the conventional model-free learning algorithms.

Joint Power Allocation and Passive Beamforming for RIS-Assisted Wireless Energy-Constrained Systems With Multi-User Scheduling

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted wireless communications system with multi-user scheduling, where a pilot-embedded data transmission is carried out from a selected user to an access point (AP) with the aid of an RIS. Specifically, by taking into account a transmit energy constraint, we propose a joint passive beamforming and power allocation optimization (JPB-PAO) scheme for the RIS-assisted wireless system, in which a problem of joint optimization on the pilot power, data power, and RIS's phase shifts is formulated for maximizing the achievable rate of the user-AP transmission. To this end, we propose a Dinkelbach and majorization-minimization (DMM) aided JPB-PAO (termed as DMM-JPB-PAO) scheme for the sake of obtaining a semi-closed form expression of the optimal RIS phase shifts, based on which a closed-form solution of power allocation is derived. Moreover, we adopt the widely used semidefinite relaxation (SDR) algorithm as a baseline to solve the JPB-PAO problem, which is referred to as the SDR-JPB-PAO. Numerical results show that our proposed DMM-JPB-PAO achieves almost the same achievable rate as the SDR-JPB-PAO at a much lower computational complexity. Meanwhile, DMM-JPB-PAO scheme yields a higher achievable rate than the passive beamforming optimization with fixed power allocation (PBO-FPA) scheme and the random passive beamforming with fixed power allocation (RPB-FPA) scheme.

Secure Transmission for STAR-RIS Aided NOMA Against Internal Eavesdropping

Simultaneous transmitting and reflecting reconfig- urable intelligent surface (STAR-RIS) has attracted extensive attentions due to its prominent advantages to future wireless networks. This correspondence considers a downlink multi-input single-output non-orthogonal multiple access system, in which the STAR-RIS is utilized to improve the secrecy performance. To restrain the internal eavesdropping, we formulate a secrecy rate maximization problem and propose a secure transmission design via the joint active and passive beamforming optimization with optimum power allocation. To decouple optimization variables, the original non-convex problem is divided into two subproblems, which are transformed into convex forms via successive convex approximation and solved by alternating optimization. Simula- tion results demonstrate the superiority of the proposed scheme in the security enhancement.

UAV Trajectory and Multi-User Beamforming Optimization for Clustered Users Against Passive Eavesdropping Attacks With Unknown CSI

This paper tackles the fundamental passive eavesdropping problem in modern wireless communications in which the location and the channel state information (CSI) of the attackers are unknown. In this regard, we propose deploying an unmanned aerial vehicle (UAV) that serves as a mobile aerial relay (AR) to help ground base station (GBS) support a subset of vulnerable users. More precisely, our solution (1) clusters the single-antenna users in two groups to be either served by the GBS directly or via the AR, (2) employs optimal multi-user beamforming to the directly served users, and (3) optimizes the AR's 3D position, its multi-user beamforming matrix and transmit powers by combining closed-form solutions with machine learning techniques. Specifically, we design a plain beamforming and power optimization combined with a deep reinforcement learning (DRL) algorithm for an AR to optimize its trajectory for the security maximization of the served users. Numerical results show that the multi-user multiple input, single output (MU-MISO) system split between a GBS and an AR with optimized transmission parameters without knowledge of the eavesdropping channels achieves high secrecy capacities that scale well with increasing the number of users.

Location Information Assisted Beamforming Design for Reconfigurable Intelligent Surface Aided Communication Systems

The large overhead arising from conventional channel estimations in reconfigurable intelligent surface (RIS) aided millimeter-wave communication systems, may offset the performance gain brought by the RIS. To tackle this issue, we propose a location information assisted beamforming design without the requirement of the channel training process. First, we establish the geometrical relationship between the channel model and the user location, and mathematically derive an approximate channel state information (CSI) error bound based on the user location error region. Then, for combating the negative impact of the location error on the communication performance, we formulate a worst-case robust beamforming optimization problem to optimize the beamformer at the base station (BS) and the phase-shift matrix at the RIS. To solve this non-convex problem, we develop a novel relaxed alternating optimization process (RAOP) by utilizing various optimization tools, such as the Lagrange multiplier, the matrix inversion lemma, the semidefinite relaxation (SDR), as well as the branch-and-bound (BnB). Additionally, we prove sufficient conditions for the SDR to yield rank-one solutions, and modify the BnB to acquire the phase-shift solution under an arbitrary constraint of possible phase-shift values. Finally, we analyse the convergence and complexity of the proposed RAOP, and carry out simulations for performance evaluations. Compared to the conventional non-robust beamforming, our method performs better and shows strong robustness against the location-error-related CSI uncertainty. Compared to the robust beamforming based on the S-procedure and penalty convex-concave procedure (CCP), our method with BnB shows the advantages of being able to converge faster and handle arbitrary phase-shift argument sets.

Beamforming and data detection in intelligent reflecting surfaces‐assisted communication using deep learning

SummaryIn this article, we propose two deep neural network (DNN) architectures for an intelligent reflecting surface (IRS)‐assisted network with a single transmitter and multiple receivers. The first DNN is designed to obtain an optimal beamforming weight vector that maximizes the achievable sum rate over the IRS‐aided network. The beamforming vector is designed based on a pilot‐based channel estimation procedure, which requires only a small fraction of the available number of reflecting surfaces on the IRS to be active. The obtained vector is fed to the second DNN, which is designed for detecting information symbols from the transmitter. Our simulation study shows that the achievable rate obtained using the designed beamforming vector approaches the maximum achievable rate quickly, as the number of active elements increases. The impact of the number of active elements and the beamforming vector on the performance of the detector is also studied, in terms of the bit error rate (BER). We show that the BER performance of the second DNN is close to that of the optimal maximum likelihood detector. Further, we study the effect of the channel estimation errors on the performance of the DNNs. Moreover, we study the effect of the hyperparameters of the architectures on the training time and performance, in terms of root mean squared error and accuracy.