Deep Learning Assisted Multiuser MIMO Load Modulated Systems for
  Enhanced Downlink mmWave Communications
Ercong Yu Jinle Zhu Qiang Li Zilong Liu Hongyang Chen Shlomo Shamai H. Vincent Poor
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.