  This paper is focused on multiuser load modulation arrays (MU-LMAs) which are
attractive due to their low system complexity and reduced cost for millimeter
wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding
algorithm for downlink MU-LMA relies on a sub-array structured (SAS)
transmitter which may suffer from decreased degrees of freedom and complex
system configuration. Furthermore, a conventional LMA codebook with codewords
uniformly distributed on a hypersphere may not be channel-adaptive and may lead
to increased signal detection complexity. In this paper, we conceive an MU-LMA
system employing a full-array structured (FAS) transmitter and propose two
algorithms accordingly. The proposed FAS-based system addresses the SAS
structural problems and can support larger numbers of users. For LMA-imposed
constant-power downlink precoding, we propose an FAS-based normalized block
diagonalization (FAS-NBD) algorithm. However, the forced normalization may
result in performance degradation. This degradation, together with the
aforementioned codebook design problems, is difficult to solve analytically.
This motivates us to propose a Deep Learning-enhanced (FAS-DL-NBD) algorithm
for adaptive codebook design and codebook-independent decoding. It is shown
that the proposed algorithms are robust to imperfect knowledge of channel state
information and yield excellent error performance. Moreover, the FAS-DL-NBD
algorithm enables signal detection with low complexity as the number of bits
per codeword increases.
