无源随机接入的随机分组与编码分混
Stochastic Binning and Coded Demixing for Unsourced Random Access
摘要：
无源随机接入是一种新型的通信模式，旨在处理大量的不协调的用户，偶尔发送非常短的消息。在此模型下，编码压缩感知（CCS）已成为一个低复杂度的计划，表现出良好的错误性能。然而，CCS所面临的挑战之一涉及解开存在于单个因子图上的大量码字。为了缓解这个问题，本文介绍了一种修改后的CCS计划，主动设备随机分割成组，利用单独的采样矩阵低交叉相干的消息传输。在接收端，采用来自压缩解混领域的思想来进行支持恢复，并为每个聚类中的消息消歧创建单独的因子图。这减少了因子图上的活动用户数量，从而在典型场景中显著提高了性能。实际上，编码解混随着组的数量增加而降低错误的概率，直到某一点。研究结果与数值模拟的支持。
英文摘要：
Unsourced random access is a novel communication paradigm designed for handling a large number of uncoordinated users that sporadically transmit very short messages. Under this model, coded compressed sensing (CCS) has emerged as a low-complexity scheme that exhibits good error performance. Yet, one of the challenges faced by CCS pertains to disentangling a large number of codewords present on a single factor graph. To mitigate this issue, this article introduces a modified CCS scheme whereby active devices stochastically partition themselves into groups that utilize separate sampling matrices with low cross-coherence for message transmission. At the receiver, ideas from the field of compressed demixing are employed for support recovery, and separate factor graphs are created for message disambiguation in each cluster. This reduces the number of active users on a factor graph, which improves performance significantly in typical scenarios. Indeed, coded demixing reduces the probability of error as the number of groups increases, up to a point. Findings are supported with numerical simulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
