Low-Complexity Iterative Detection Algorithm for Massive Data Communication in IIoT

As the fundamental problem of Industrial Internet of Things, massive data communication based on non-orthogonal multiple access is attractive. An iterative multiuser receiver provides a substantial performance improvement, but suffers from a distortion that the overestimation of output reliability values for bad channels. Furthermore, the main challenge lies in the high computational complexity. This paper develops an improved iterative multiuser receiver with independent channel information. In order to analyze its performance, JS-divergence is introduced to measure the correlation of exchanged information between the detector and the decoder. Low-complexity iterative detection algorithm based on JS-divergence values is proposed in this paper. The simulation results demonstrate that the proposed iterative multiuser receiver reduces the overestimation of reliability values and improves the system performance when Eb/N0 is less than 3 dB. The low-complexity iterative detection algorithm can terminate in advance when JS-divergence values of all users reach to a threshold and reduce the number of outer-loop iterations and computational complexity greatly.