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.

翻译：本文聚焦于多用户负载调制阵列（MU-LMA），该技术因其在毫米波（mmWave）多输入多输出（MIMO）系统中具有较低系统复杂度和成本而备受关注。现有下行MU-LMA预编码依赖子阵列结构（SAS）发射机，但此类结构存在自由度下降与系统配置复杂等问题。此外，传统LMA码本中均匀分布于超球面的码字缺乏信道自适应性，且可能增加信号检测复杂度。本文设计了一种采用全阵列结构（FAS）发射机的MU-LMA系统，并据此提出两种算法。所提FAS基系统可解决SAS结构缺陷，并支持更多用户接入。针对LMA固有的恒功率下行预编码约束，我们提出了基于FAS的归一化块对角化（FAS-NBD）算法。然而，强制归一化可能导致性能损失。该性能退化与上述码本设计难题难以通过解析方法解决。为此，我们进一步提出深度学习增强型（FAS-DL-NBD）算法，实现自适应码本设计和独立于码本的译码。研究表明，所提算法对信道状态信息的不完备估计具有鲁棒性，并能实现优异的误码性能。此外，随着每个码字比特数的增加，FAS-DL-NBD算法可实现低复杂度信号检测。