无蜂窝网络的分布式与集中式IDD方案
Decentralized and Centralized IDD Schemes for Cell-Free Networks
AI总结：
针对无蜂窝大规模MIMO系统，提出带接入点选择的迭代干扰消除方案及相关滤波器、检测器与IDD方案，通过LLR优化策略提升性能，验证了分布式架构的优越性。
摘要：
本文针对无蜂窝大规模多输入多输出（CF-mMIMO）系统，提出了带接入点选择（APs-Sel）的迭代干扰消除方案。在信道状态信息（CSI）非理想的假设下，推导了带APs-Sel的集中式与分布式线性最小均方误差（LMMSE）接收滤波器的闭式表达式。此外，我们基于LMMSE接收滤波器开发了一种利用干扰消除和星座点的列表式检测器，随后构建了采用低密度奇偶校验（LDPC）码的基于消息传递的迭代检测与译码（IDD）方案。我们还提出了基于删失和本地对数似然比（LLR）线性组合的LLR优化策略，以提升网络性能。我们在理想CSI（PCSI）和非理想CSI（ICSI）场景下，从误码率（BER）性能、复杂度和信令开销方面对比了集中式与分布式处理方案，验证了带LLR优化的分布式架构的优越性。
英文摘要：
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.
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