Joint Identification, Detection and Channel Estimation in Industrial IoT

Abstract

Rapid identification and analysis of faulty or degraded sensors and devices are key challenge in Industrial Internet of Things (IIoT). Wireless communication is emerging as a viable solution to support the massive sensors and devices with sporadic fault due to low construction cost, low latency and high reliability. For future massive machine-type communications, Media-Based Modulation (MBM) is regarded as a promising technique to achieve high data rate with low-complexity hardware implementation. In this paper, only the sensors or devices with fault are activated to transmit data which use MBM to carry more fault information. A compressive sensing (CS)-based joint adaptively fault machine identification, data detection and channel estimation algorithm is proposed by taking full consideration of the prior information such as sparsity structure and physical variables correlation. The simulation results demonstrate that the proposed algorithm is superior to the state-of-the-art identification and detection methods.