mmWave Multiuser Multiple-input Multiple-output Research Articles

Hybrid Analog and Digital Beamforming for RIS-Assisted mmWave Communications

Reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) communications has been envisioned as a prominent technology for future wireless networks, since it is capable of simultaneously providing abundant spectrum resources and favorable propagation environments. The small wavelength at mmWave bands also enables the widespread use of large antenna arrays, of which the hybrid beamforming structure has emerged as a cost-effective solution. In this paper, we aim to minimize the sum-mean-square-error (sum-MSE) in the RIS-assisted mmWave multiuser multiple input multiple output (MU-MIMO) system by jointly optimizing the hybrid analog-digital precoders and the RIS reflection matrix. We demonstrate that the role of RIS in assisting mmWave communications can be completely replaced by a large-scale Kronecker-structured hybrid array. Moreover, an accelerated Riemannian gradient algorithm using majorization minimization technique is proposed to tackle the unit-modulus constrained analog precoder/RIS design. Under the assumption of perfect channel state information (CSI), we firstly consider the single-user MIMO (SU-MIMO) setup and propose an effective alternating minimization (AM) procedure to characterize the system performance limit. Moreover, a two-stage scheme is developed for low-complexity implementation. This AM procedure is then extended to the general MU-MIMO scenario. In addition, we develop a novel enhanced regularized zero-forcing (ERZF) scheme for simultaneously combating strong noise in the low-SNR regime and mitigating multi-user interference (MUI) in the high-SNR regime. The optimality of our proposed algorithms is validated for some simplified practical scenarios. Numerical results illustrate that the proposed algorithms outperform existing benchmark schemes in terms of the actual complexity and performance.

Switch-and-inverter based adaptive subarrays for hybrid mmWave multi-user MIMO systems

ABSTRACT The hybrid precoding design is considered for a millimeter-wave (mmWave) multi-user (MU) multiple-input multiple-output (MIMO) downlink communication system. A small quantity of more energy-efficient switches and inverters (SIs) are being substituted for a large amount of high-resolution phase shifters (PSs) in the analog precoder of the mmWave MIMO system. This SI-based hybrid precoding architecture reduces significantly both the hardware complexity and energy consumption and thus enhances substantially the transmission energy efficiency (EE). On the basis of the achievable sum-rate (ASR) maximization, the SI-based overlapped subarray (OSA) hybrid precoding is exploited. Compared with the traditional SI-based sub-connected (SC) array configurations, the greater the overlapping antenna elements, the better the transmission ASR performance, and the nearer the fully PS-based precoding configuration. Finally, the SI-based hybrid precoding is equipped with the Gram–Schmidt orthogonalization (GSO) based dynamic subarray mapping and antenna selection (AS) mechanism to improve further the EE measure while maintaining excellent ASR performance.

Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO With Lens Arrays

The millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays (DLA) have received great attention due to their simple hardware implementation and excellent performance. In this work, we investigate the joint design of beam selection and digital precoding matrices for mmWave MU-MIMO systems with DLA to maximize the sum-rate subject to the transmit power constraint and the constraints of the selection matrix structure. The investigated non-convex problem with discrete variables and coupled constraints is challenging to solve and an efficient framework of joint neural network (NN) design is proposed to tackle it. Specifically, the proposed framework consists of a deep reinforcement learning (DRL)-based NN and a deep-unfolding NN, which are employed to optimize the beam selection and digital precoding matrices, respectively. As for the DRL-based NN, we formulate the beam selection problem as a Markov decision process and a double deep Q-network algorithm is developed to solve it. The base station is considered to be an agent, where the state, action, and reward function are carefully designed. Regarding the design of the digital precoding matrix, we develop an iterative weighted minimum mean-square error algorithm induced deep-unfolding NN, which unfolds this algorithm into a layer-wise structure with introduced trainable parameters. Simulation results verify that this jointly trained NN remarkably outperforms the existing iterative algorithms with reduced complexity and stronger robustness.

Multi-User Angle-Domain MIMO-NOMA System for mmWave Communications

Thanks to the high directionality of millimeter-wave (mmWave) channels, angle-domain beamforming is an appealing technique for multi-user multiple-input multiple-output (MU-MIMO) in terms of sum-throughput performance and limited feedback. By utilizing only the angular information of users at the transmitter, we propose an angle-domain non-orthogonal multiple access (NOMA) scheme to enhance the sum-throughput of the mmWave MU-MIMO system, especially in congested cells. We first derive a set of angular-based performance metrics, such as the inter-user spatial interference, the user channel quality, and the sum-throughput, by exploiting the specific features of the mmWave propagation. Then, a multi-user clustering algorithm is developed based on the spatial interference metric, and a new user ordering strategy is proposed using the angular-based channel quality metric. Additionally, we design a power allocation method that maximizes the angular-based sum-throughput. Extensive numerical results show that the proposed scheme significantly improves the performance of the mmWave MU-MIMO system by achieving up to 39% increase in the spectral efficiency when the number of users is closed to the number of antennas. Moreover, we find that the proposed user ordering strategy outperforms other limited feedback strategies, and the angular-based power allocation allows for efficient successive interference cancellation.

Hybrid Combining Based on Constant Phase Shifters and Active/Inactive Switches

In this paper, we propose a new hybrid analog and digital combining architecture for millimeter wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO) systems. The proposed structure employs antenna subset selection per radio frequency (RF) chain based on active / inactive switches and uses constant phase shifters (CPS) to control the phases of signals in the RF circuit. In this scheme, for each RF chain, a subset of receive antennas that contribute more to the desired signal power than the interference power is chosen for signal combining in the analog domain, whereas other receive antennas are excluded from signal combining, thereby enhancing sum-rates. Simultaneously, the proposed structure reduces power consumption in the RF circuit by exclusively activating switches that correspond to the antennas selected for each RF chain. We also develop three low-complexity algorithms for per-RF chain antenna subset selection. Finally, through numerical simulation, we show that the proposed structure provides higher spectral efficiency and higher energy efficiency than conventional hybrid analog and digital combining schemes for mmWave MU-MIMO systems.

Fully-/Partially-Connected Hybrid Beamforming Architectures for mmWave MU-MIMO

Hybrid digital analog (HDA) beamforming has attracted considerable attention in practical implementation of millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems due to the low power consumption with respect to its fully digital baseband counterpart. The implementation cost, performance, and power efficiency of HDA beamforming depends on the level of connectivity and reconfigurability of the analog beamforming network. In this paper, we investigate the performance of two typical architectures that can be regarded as extreme cases, namely, the fully-connected (FC) and the one-stream-per-subarray (OSPS) architectures. In the FC architecture each RF antenna port is connected to all antenna elements of the array, while in the OSPS architecture the RF antenna ports are connected to disjoint subarrays. We jointly consider the initial beam acquisition and data communication phases, such that the latter takes place by using the beam direction information obtained by the former. We use the state-of-the-art beam alignment (BA) scheme previously proposed by the authors and consider a family of MU-MIMO precoding schemes well adapted to the beam information extracted from the BA phase. We also evaluate the power efficiency of the two HDA architectures taking into account the power dissipation at different hardware components as well as the power backoff under typical power amplifier constraints. Numerical results show that the two architectures achieve similar sum spectral efficiency, while the OSPS architecture is advantageous with respect to the FC case in terms of hardware complexity and power efficiency, at the sole cost of a slightly longer BA time-to-acquisition due to its reduced beam angle resolution.

A Hardware-Scalable DSP Architecture for Beam Selection in mm-Wave MU-MIMO Systems

Millimeter-wave (mm-Wave) communication, operating from 30- to 300-GHz bands with wider available spectrum, has now emerged as a promising solution for future wireless systems. To reduce the hardware complexity of mm-Wave transceivers, beam-selection techniques combined with beamspace multiple-input multiple-output (MIMO) communications have been proposed in the literature. In such a system, beam selection is performed to exploit the near-sparse nature in the mm-Wave channel and, hence, is crucial to the system performance. This paper presents the world’s first digital signal processing (DSP) architecture design for beam selection operation in an mm-Wave multi-user MIMO system. A computationally efficient beam-selection algorithm is first presented. Based on the proposed algorithm, a hardware-scalable beam selection processor is implemented using coordinate rotation digital computers arithmetic. To establish the validity of the concept, the proposed processor is implemented in a 40-nm CMOS technology, integrating 360k logic gates in an area of 0.5625 mm2, while dissipating 44 mW at 266 MHz in an MU-MIMO system with 16 beams and 8 users.