Grant-Free Communications With Adaptive Period for IIoT: Sparsity and Correlation-Based Joint Channel Estimation and Signal Detection

Yuanchen Wang, Xu Zhu, Eng Gee Lim, Zhongxiang Wei, Yufei Jiang

Abstract: In this article, we investigate the grant-free communications with adaptive period for Industrial Internet of Things, where only a fraction of devices is active at a time. To the best of our knowledge, this is the first work to exploit the noncontinuous temporal correlation of the received signal for joint user activity detection (UAD), channel estimation, and signal detection, while all the previous work requires continuous transmission. Two schemes are proposed toward this purpose, namely, periodic block orthogonal matching pursuit (PBOMP) and periodic block sparse Bayesian learning (PBSBL), which outperform the previous schemes in terms of the success rate of UAD, bit error rate, and accuracy in period estimation and channel estimation. The Cramér–Rao lower bounds (CRLBs) of channel estimation by PBOMP and PBSBL are derived. It is shown that the two proposed approaches have close CRLBs and normalized mean-square error at high SNR.