Channel Detection under Impulsive Noise and Fading Environments for Smart Grid

The advanced 6G Technology benefits the Internet of Things (IoT) in various applications. As one essential application scenario, Smart Grid (SG) incorporates communication and management techniques and promises an efficient and intelligent power system, whereby Cognitive Radio (CR) is believed to be an essential tool for better resource utilization in power generation and delivering processes. In the CR-assisted IoT in SG scenarios, Channel Detection (CD) will play an essential role to accurately sense the available channel resource. However, for SG scenarios, high-accuracy CD may become a challenging task in complex power supply environments with unexpected Impulsive Noise (IN) and channel fading, which will significantly affect the signal statistical property. To address this problem, we propose a novel CD mechanism in the context of the wireless environment with IN and random channel fading. To be specific, taking the wireless channel status, IN and time-variant fading into account, a novel Quaternary Hypothesis Testing Model (QHTM) is formulated to describe the detection task, and by which a new Dynamic State-space Model (DSM) is developed to capture the dynamical behavior of the CD system. On this basis, a joint detection and estimation algorithm based on Bayesian statistical inference is devised to accomplish the CD task. Benefiting from the joint posteriori distribution estimation procedure, our algorithm can not only accurately detect the unknown channel status, but also estimate the real-time channel state information (CSI), thereby eliminating their effects on the detection performance. Numerical simulation results validate the proposed CD mechanism.