The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximum a posteriori (MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities.

翻译：针对多输入多输出（MIMO）超可靠低时延通信（URLLC）系统，由于采用短码信道编码与少量导频符号，其接收机设计面临严峻挑战。传统Turbo接收机中存在的误差传播问题会导致性能退化，且不同模块间信息交换引起的处理时延对URLLC场景同样不利。为解决上述问题，本文倡导在采用短低密度奇偶校验（LDPC）码的MIMO URLLC系统中实施联合信道估计、检测与译码（JCDD）方案。具体而言，我们分别基于高斯MIMO信道与稀疏毫米波MIMO信道的最大后验概率（MAP）准则，提出两种新型JCDD问题建模方案，该方案融合了导频信息、比特-符号映射、LDPC码约束及信道统计特性。通过采用交替方向乘子法（ADMM）算法解决这两个具有挑战性的大规模非凸问题，并在每次ADMM迭代中实现闭式求解。进一步地，通过展开所推导的ADMM算法并引入可训练参数，构建了两种JCDD神经网络——JCDDNet-G与JCDDNet-S。仿真结果表明，所提出的可训练JCDD接收机在可接受的计算复杂度下，性能优于传统Turbo接收机。