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

Grant Free MIMO-NOMA with Differential Modulation for Machine Type Communications