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This paper is focused on multiuser load modulation arrays (MU-LMAs) which areattractive due to their low system complexity and reduced cost for millimeterwave (mmWave) multi-input multi-output (MIMO) systems. The existing precodingalgorithm for downlink MU-LMA relies on a sub-array structured (SAS)transmitter which may suffer from decreased degrees of freedom and complexsystem configuration. Furthermore, a conventional LMA codebook with codewordsuniformly distributed on a hypersphere may not be channel-adaptive and may leadto increased signal detection complexity. In this paper, we conceive an MU-LMAsystem employing a full-array structured (FAS) transmitter and propose twoalgorithms accordingly. The proposed FAS-based system addresses the SASstructural problems and can support larger numbers of users. For LMA-imposedconstant-power downlink precoding, we propose an FAS-based normalized blockdiagonalization (FAS-NBD) algorithm. However, the forced normalization mayresult in performance degradation. This degradation, together with theaforementioned codebook design problems, is difficult to solve analytically.This motivates us to propose a Deep Learning-enhanced (FAS-DL-NBD) algorithmfor adaptive codebook design and codebook-independent decoding. It is shownthat the proposed algorithms are robust to imperfect knowledge of channel stateinformation and yield excellent error performance. Moreover, the FAS-DL-NBDalgorithm enables signal detection with low complexity as the number of bitsper codeword increases.

Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications