In this paper, we propose iterative interference cancellation schemes with access points selection (APs-Sel) for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Closed-form expressions for centralized and decentralized linear minimum mean square error (LMMSE) receive filters with APs-Sel are derived assuming imperfect channel state information (CSI). Furthermore, we develop a list-based detector based on LMMSE receive filters that exploits interference cancellation and the constellation points. A message-passing-based iterative detection and decoding (IDD) scheme that employs low-density parity-check (LDPC) codes is then developed. Moreover, log-likelihood ratio (LLR) refinement strategies based on censoring and a linear combination of local LLRs are proposed to improve the network performance. We compare the cases with centralized and decentralized processing in terms of bit error rate (BER) performance, complexity, and signaling under perfect CSI (PCSI) and imperfect CSI (ICSI) and verify the superiority of the distributed architecture with LLR refinements.

翻译：本文针对无蜂窝大规模多输入多输出系统，提出了结合接入点选择的迭代干扰消除方案。在假设信道状态信息不完美的条件下，推导了采用接入点选择的中心化与去中心化线性最小均方误差接收滤波器的闭式表达式。进一步，我们基于LMMSE接收滤波器开发了一种利用干扰消除与星座点的列表检测器。随后构建了一种采用低密度奇偶校验码的、基于消息传递的迭代检测解码方案。此外，为提高网络性能，提出了基于信息剔除与局部对数似然比线性组合的LLR优化策略。我们在完美CSI与非完美CSI条件下，从误码率性能、复杂度及信令开销等方面对比了中心化与去中心化处理方案，验证了采用LLR优化的分布式架构的优越性。