Massive communication is one of key scenarios of 6G where two magnitude higher connection density would be required to serve diverse services. As a promising direction, unsourced multiple access has been proved to outperform significantly over orthogonal multiple access (OMA) or slotted-ALOHA in massive connections. In this paper we describe a design framework of unsourced sparse multiple access (USMA) that consists of two key modules: compressed sensing for preamble generation, and sparse interleaver division multiple access (SIDMA) for main packet transmission. Simulation results of general design of USMA show that the theoretical bound can be approached within 1~1.5 dB by using simple channel codes like convolutional. To illustrate the scalability of USMA, a customized design for ambient Internet of Things (A-IoT) is proposed, so that much less memory and computation are required. Simulations results of Rayleigh fading and realistic channel estimation show that USMA based A-IoT solution can deliver nearly 4 times capacity and 6 times efficiency for random access over traditional radio frequency identification (RFID) technology.

翻译：大规模通信是6G的关键场景之一，需要提升两个数量级的连接密度以支撑多样化服务。作为一种前景广阔的技术方向，无源多址接入已被证实在大规模连接场景下显著优于正交多址接入（OMA）或时隙ALOHA协议。本文提出一种无源稀疏多址接入（USMA）的设计框架，该框架包含两个核心模块：用于前导序列生成的压缩感知技术，以及用于主数据包传输的稀疏交织分多址（SIDMA）方案。通用USMA设计的仿真结果表明，采用卷积码等简单信道编码即可在理论界1~1.5 dB范围内实现性能逼近。为验证USMA的可扩展性，本文针对环境物联网（A-IoT）提出定制化设计方案，大幅降低了存储与计算需求。基于瑞利衰落信道与真实信道估计的仿真结果表明，相较于传统射频识别（RFID）技术，采用USMA的A-IoT解决方案可将随机接入容量提升近4倍，效率提升近6倍。