Sparse Vector Coding Based Massive Grant-Free Access for Short-Packet Communication in IIoT

In industrial Internet of Things (IIoT), short-packet communication requires higher reliability and lower latency, enabling Grant-free (GF) access in massive machine-type communication (mMTC) to gain a tremendous research interest. However, in practical applications, the accurate channel state information (CSI) required by coherent GF (C-GF) is challenging to acquire. Concurrently, the spectral efficiency and reliability of the non-coherent (NC-GF) scheme are areas necessitating enhancement. Inspired by sparse vector coding (SVC), this paper introduces an innovative SVC-GF scheme. This scheme redefines joint user activity detection (UAD) and data decoding as a bifurcated sparse recovery issue. A novel block refinement orthogonal matching pursuit and multipath matching pursuit (BROMP-MMP) algorithm is designed to resolve this intricate problem. Simulation results demonstrate that the proposed SVC-GF scheme outperforms traditional C-GF and NC-GF schemes in terms of probability of successful detection and average block error rate (BLER) performance, heralding a paradigm shift for massive GF access.