大规模MTC通信将成为未来无线通信的重要组成部分，医疗辅助、智能电网、智能汽车和交通管理等将会推进MTC通信的快速发展，数以百万千万设备接入网络，从而实现万物互联。大规模MTC通信关注大量机器类设备与基站之间的连接，与传统通信的区别在于用户数量巨大，然而在每个接入时隙活跃用户数量通常极少，因此现有的多址接入技术和多用户检测算法无法有效支持大规模MTC通信。针对以上问题，本项目拟采用贝叶斯压缩感知的思想，解决MTC通信信号检测问题，拟研究：（1）大规模MTC多址接入系统模型及其特性分析；（2）基于贝叶斯压缩感知的单时隙多址接入多用户信号检测；（3）基于贝叶斯压缩感知的多时隙多址接入多用户信号检测。通过以上研究，探明大规模MTC通信基本特征，提出多址接入方案，设计性能优越、复杂度较低的多用户检测算法，建立信号检测的性能分析体系，为大规模MTC通信的发展提供基础理论支撑，推进6G通信的发展。

Massive MTC will play a significant role in future wireless communications. Medical assistance, smart grid, smart cars and traffic management will promote the rapid development of MTC communications. Millions of devices connect to the network so that everything is connected. Massive MTC communications focus on connections between machine-type equipment and base stations. Different from traditional communications, the total number of users is very large and the active rate in each time slot is typically small. Conventional multiuser detection algorithms cannot effectively support such massive MTC communications. This project focuses on solving the multiuser detection problem using Bayesian compressive sensing theory in MTC communications, mainly focusing on: (1) System model and analysis of multiple access in massive MTC; (2) Bayesian compressive sensing based multiuser signal detection of multiple access for single time slot; (3) Bayesian compressive sensing based multiuser signal detection of multiple access for multiple time slots. Through the above research, we will find out the basic characteristics of massive MTC communications, and propose the multiple access scheme. We will design novel multiuser detection algorithms with superior performance and low complexity. These studies provide theoretical basis for massive MTC in future 6G communications.