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In this work, we investigate hybrid analog–digital beamforming (HBF) architectures for uplink cell-free (CF) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. We first propose two HBF schemes, namely, decentralized HBF (D-HBF) and semi-centralized HBF (SC-HBF). In the former, both the digital and analog beamformers are generated independently at each AP based on the local channel state information (CSI). In contrast, in the latter, only the digital beamformer is obtained locally at the access point (AP), whereas the analog beamforming matrix is generated at the central processing unit (CPU) based on the global CSI received from all APs. We show that the analog beamformers generated in these two HBF schemes provide approximately the same achievable rates despite the lower complexity of D-HBF and its lack of CSI requirement. Furthermore, to reduce the power consumption, we propose a novel adaptive radio frequency (RF) chain-activation (ARFA) scheme, which dynamically activates/deactivates RF chains and their connected analog-to-digital converters (ADCs) and phase shifters (PSs) at the APs based on the CSI. For the activation of RF chains, low-complexity algorithms are proposed, which can achieve significant improvement in energy efficiency (EE) with only a marginal loss in the total achievable rate. [ABSTRACT FROM AUTHOR]

Link adaptation (LA) is an adaptive transmission technique that determines the modulation and coding scheme (MCS) based on channel-state information. In LA, an accurate estimation of the link performance is required to optimally determine the MCS level. In this correspondence, a high-accuracy machine-learning (ML)-aided link-level performance-prediction method for coded multiple-input–multiple-output (MIMO) systems is proposed. The basic concept of this scheme is to apply the ML model to train the relation between the inputs, such as the channel matrix and signal-to-noise ratio, and the output of the block-error rate (BLER). Specifically, we predict the index of the quantized BLER value using a random forest classifier. The simulation results show that the proposed scheme is able to accurately predict the link performance of MIMO systems and outperforms the conventional link performance-prediction schemes. [ABSTRACT FROM AUTHOR]

In this work, we propose a novel dynamic unequal sub-array (DUESA) architecture for massive multiple-input multiple-output (MIMO) systems with partially connected hybrid beamforming (PC-HBF) that relaxes the antenna-allocation constraint in the conventional unequal sub-array (UESA) architecture. The proposed DUESA architecture dynamically allocates antennas to sub-arrays based on channel state information, which leads to a combinatorial problem to optimize the sum rate. The exact solution to this problem requires an exhaustive search; however, the total number of candidates to search through can be extremely large in massive MIMO systems. To address this challenge, we propose three sub-optimal algorithms. The first is a tabu search-based algorithm, which examines the neighbors of the current solution to antenna-to-sub-array allocation and moves to the best one in each iteration. The second is a sequential antenna-reallocation algorithm with reduced complexity, which updates an antenna-to-sub-array assignment in each iteration. The third algorithm, that is, greedy antenna allocation, is composed of two sub-optimal algorithms. In its first part, only a single antenna is allocated to each sub-array based on a conventional algorithm. Then, in the second part, the antennas are added sequentially to each sub-array to optimize the system sum rate. The numerical results demonstrate that the DUESA architecture achieves substantially higher total achievable rates than conventional PC-HBF schemes. Furthermore, it has approximately 10%–20% higher energy efficiency than the conventional UESA architecture. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]