Unsourced random access (URA) is a recently proposed multiple access paradigm tailored to the uplink channel of machine-type communication networks. By exploiting a strong connection between URA and compressed sensing, the massive multiple access problem may be cast as a compressed sensing (CS) problem, albeit one in exceedingly large dimensions. To efficiently handle the dimensionality of the problem, coded compressed sensing (CCS) has emerged as a pragmatic signal processing tool that, when applied to URA, offers good performance at low complexity. While CCS is effective at recovering a signal that is sparse with respect to a single basis, it is unable to jointly recover signals that are sparse with respect to separate bases. In this article, the CCS framework is extended to the demixing setting, yielding a novel technique called coded demixing. A generalized framework for coded demixing is presented and a low-complexity recovery algorithm based on approximate message passing (AMP) is developed. Coded demixing is applied to heterogeneous multi-class URA networks and traditional single-class networks. Its performance is analyzed and numerical simulations are presented to highlight the benefits of coded demixing.

中文翻译：

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无源随机访问的编码分离

无源随机接入 (URA) 是最近提出的针对机器类型通信网络的上行链路信道量身定制的多址接入范式。通过利用 URA 和压缩感知之间的紧密联系，大规模多路访问问题可能会被视为压缩感知 (CS) 问题，尽管它的维度非常大。为了有效地处理问题的维度，编码压缩感知 (CCS) 已成为一种实用的信号处理工具，当应用于 URA 时，它以低复杂度提供了良好的性能。虽然 CCS 在恢复相对于单个碱基稀疏的信号方面是有效的，但它无法联合恢复相对于单独碱基稀疏的信号。在本文中，CCS 框架扩展到了分相设置，产生了一种称为编码分相的新技术。提出了一个通用的编码分离框架，并开发了一种基于近似消息传递（AMP）的低复杂度恢复算法。编码分离应用于异构多​​类URA网络和传统单类网络。对其性能进行了分析，并提出了数值模拟，以突出编码分离的好处。