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The design of hybrid beamforming (HBF) is one of the key issues in millimeter wave (mmWave) and terahertz (THz) massive multi-input multi-output (MIMO) communications. In particular, only rank-deficient channel state information (CSI) can be acquired in hybrid MIMO architecture, which enhances design challenges. However, this practical problem of rank-deficient channels has been avoided by most of the existing studies on HBF. In this letter, we propose a new deep learning (DL) network model called Multi-Generator Generative Adversarial Network (MGGAN) to accomplish the design of HBF against rank-deficient channels. Specifically, MGGAN includes three generators for recovering the rank-deficient channels, achieving the analog and the digital beamforming matrices, respectively. In addition, a spectrum efficiency (SE) module is introduced to take system SE as the optimization goal of the network. Simulation results show the superiority of our proposed MGGAN model in SE performance over several DL network architectures and classical HBF algorithms. Moreover, it is more robust against the rank-deficiency of hybrid architecture MIMO channels. [ABSTRACT FROM AUTHOR]

In the face of the unprecedented demand for higher data rates while coping with the constraints from physical size (i.e., windward area), it is profitable to deploy compact planar arrays (CPAs) on the base stations (BSs). But massively densified antenna deployment not only leads to antenna mutual coupling (MC), but also consumes additional energy due to the equipment of more radio frequency (RF) chains. This paper studies energy efficiency (EE) optimization for massive multi-input multi-output (MIMO) wireless systems with a CPA in the presence of MC. In comprehensive consideration of the spectral efficiency (SE) and power consumption, a joint optimization problem of beamforming and antenna subset (AS) is proposed. Our motivation is to relieve the effects of MC and reduce the relevant energy consumption by constructing irregular AS. Particularly, considering the nonconvexity of the objective and the discrete 0-1 constraints, an available iterative algorithm, named the energy-efficient beamforming and AS (EE-BF&AS) algorithm, is developed. Numerical results illustrate that when MC is modeled, the proposed EE-BF&AS algorithm outperforms conventional related approaches in EE. By using the AS switching strategy, the irregular AS can be constructed to make MC less serious and let the relevant energy consumption decrease. [ABSTRACT FROM AUTHOR]

Thanks to the short wavelength at high frequency (HF), it is promising to combine massive multiple-input and multiple-output (MIMO) with HF communications. In particular, HF massive MIMO differs from the conventional MIMO in array structure and radio propagation. In this paper, we investigate the HF massive MIMO system with a compact planar array (CPA) deployed at the base station (BS) in the presence of electromagnetic mutual coupling (MC) for the asymptotic case under unlimited antenna elements assumption. Specifically, the HF sparse channel is analyzed asymptotically to show that the randomness characteristic of channel decreases in the asymptotic regime, which is conducive to the design of communication systems. Moreover, on the basis of the asymptotic analysis results, the optimal beamforming to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) is derived by applying random matrix theory. Numerical results verify the accuracy of the asymptotic analysis, indicate the performance superiority of the proposed scheme to the traditional minimum mean squared error (MMSE) and maximum ratio transmission (MRT) schemes, and reveal the robustness of the proposal to electromagnetic MC. [ABSTRACT FROM AUTHOR]

This paper investigates the application of nonorthogonal multiple access (NOMA) in millimeter-Wave (mmWave) communications to address resource allocation issues for multiuser downlink transmission. Inspired by the K-means clustering algorithm, a NOMA user grouping policy is first employed in accordance with the channel correlation between users. The first-stage decoding order for each NOMA group is then determined by merely utilizing the users’ channel gain to formulate a joint power allocation and hybrid beamforming optimization problem. Specifically, we use signal-to-leakage-plus-noise ratio (SLNR) as the performance index of the optimization problem to reduce the computational complexity, because it can decouple the design of the beamforming and power allocation issues so they can be executed iteratively. Under given beamforming matrix, the power allocation issue is expressed as a quadratic programming (QP) problem, and then transformed into a convex problem by introducing an auxiliary-positive real variable to solve through the Lagrange multiplier method. On the contrary, the beamforming optimization problem is non-convex and very difficult to solve due to the constant modulus constraints in the hybrid architecture. However, the optimum solution of the ideal full-digital beamforming can be acquired primarily by generalized eigenvalue decomposition (GED). In this condition, two hybrid beamforming algorithms are proposed, in which normalized phase matching and equal gain transmission are performed respectively in the analog domain. On this basis, a second-stage decoding order can be employed according to the effective channel gain of users to perform successive interference cancellation (SIC) successfully. Numerical results show that the proposed joint power allocation and hybrid beamforming algorithms are superior to the classical mmWave-NOMA and orthogonal multiple access (OMA) schemes not only in lower implementation complexity, but also in better spectrum efficiency and energy efficiency performance. [ABSTRACT FROM AUTHOR]

In this letter, the use of intelligent reflecting surface (IRS) to enhance the physical layer security of downlink wireless communication is investigated. Assuming a single-antenna legitimate user and a multi-antenna eavesdropper, we propose an effective algorithm to jointly optimize the active and passive beamforming. In the proposed algorithm, the optimal transmit beamforming vector at the BS under fixed IRS phase shifts is derived, and a low-complexity algorithm based on fractional programming (FP) and manifold optimization (MO) is proposed to obtain near optimal IRS phase shifts. Simulation results demonstrate that the proposed algorithm can almost achieve the performance upper bound with a fast convergence rate. [ABSTRACT FROM AUTHOR]