标题： 稀疏回归码在无需信道状态信息的情况下利用多用户多样性 显示英文标题

标题： Sparse Regression Codes exploit Multi-User Diversity without CSI

摘要： 我们研究在非协作平坦衰落信道中的多接入信道的稀疏回归码（SPARC）。我们提出了一种新颖的实用解码器，称为最大似然匹配追踪（MLMP），它通过部分最大似然度量贪婪地找到用户码字的支持集。与传统的基于逐次取消的贪心算法相反，MLMP作为逐次合并的能量检测器工作。我们还提出了MLMP的改进方法，以提高高码率下的性能。我们的短块长度研究表明，即使没有任何信道状态信息，SPARC与MLMP解码器在某些场景下实现了多用户分集，与相应的单用户情况相比，多用户的错误性能更好。我们还表明，SPARC与MLMP的性能优于传统的稀疏恢复算法和使用极化码的导频辅助传输。 显示英文摘要

摘要： We study sparse regression codes (SPARC) for multiple access channels with multiple receive antennas, in non-coherent flat fading channels. We propose a novel practical decoder, referred to as maximum likelihood matching pursuit (MLMP), which greedily finds the support of the codewords of users with partial maximum likelihood metrics. As opposed to the conventional successive-cancellation based greedy algorithms, MLMP works as a successive-combining energy detector. We also propose MLMP modifications to improve the performance at high code rates. Our studies in short block lengths show that, even without any channel state information, SPARC with MLMP decoder achieves multi-user diversity in some scenarios, giving better error performance with multiple users than that of the corresponding single-user case. We also show that SPARC with MLMP performs better than conventional sparse recovery algorithms and pilot-aided transmissions with polar codes.