This paper considers a challenging scenario of machine type communications, where we assume internet of things (IoT) devices send short packets sporadically to an access point (AP) and the devices are not synchronized in the packet level. High transmission efficiency and low latency are concerned. Motivated by the great potential of multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) in massive access, we design a grant-free MIMO-NOMA scheme, and in particular differential modulation is used so that expensive channel estimation at the receiver (AP) can be bypassed. The receiver at AP needs to carry out active device detection and multi-device data detection. The active user detection is formulated as the estimation of the common support of sparse signals, and a message passing based sparse Bayesian learning (SBL) algorithm is designed to solve the problem. Due to the use of differential modulation, we investigate the problem of non-coherent multi-device data detection, and develop a message passing based Bayesian data detector, where the constraint of differential modulation is exploited to drastically improve the detection performance, compared to the conventional non-coherent detection scheme. Simulation results demonstrate the effectiveness of the proposed active device detector and non-coherent multi-device data detector.

翻译：本文考虑机器类型通信中的一种具有挑战性的场景，其中假设物联网（IoT）设备零星地向接入点（AP）发送短数据包，且设备在数据包级别上未实现同步。研究关注高传输效率与低时延需求。受多输入多输出非正交多址接入（MIMO-NOMA）在大规模接入中巨大潜力的启发，本文设计了一种免授权MIMO-NOMA方案，特别采用差分调制以规避接收端（AP）昂贵的信道估计。AP接收器需执行活跃设备检测与多设备数据检测。活跃用户检测被建模为稀疏信号共同支撑的估计问题，并设计了一种基于消息传递的稀疏贝叶斯学习（SBL）算法进行求解。由于采用差分调制，本文进一步研究了非相干多设备数据检测问题，提出了一种基于消息传递的贝叶斯数据检测器，通过利用差分调制的约束条件，相较于传统非相干检测方案显著提升了检测性能。仿真结果验证了所提出的活跃设备检测器与非相干多设备数据检测器的有效性。