Journal ArticleDOI
Channel Detection Under Impulsive Noise and Fading Environments for Smart Grid
Yiwen Tao,Bin Li,Chenglin Zhao +2 more
TLDR
A joint detection and estimation algorithm based on Bayesian statistical inference is devised to accomplish the CD task, which 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.Abstract:
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.read more
Citations
More filters
Journal ArticleDOI
Adaptive Demodulation in Impulse Noise Channels
Kristoffer Hägglund,Erik Axell +1 more
TL;DR: The proposed adaptive demodulation techniques can be used to improve decoding and demodulated performance in many real-life situations where non-Gaussian interference commonly occurs.
Journal ArticleDOI
Adaptive Demodulation in Impulse Noise Channels
TL;DR: In this article , the authors proposed two algorithms for adaptive demodulation in impulse noise, which compute appropriate log-likelihood ratios based on interference classification and estimation of Middleton's Class A noise model as well as the symmetric S model.
Journal ArticleDOI
A Random Access Protocol for Crowded Massive MIMO Systems Based on a Bayesian Classifier
TL;DR: In this article , a Bayesian classifier is proposed to identify the strongest user in a decentralized way, aiming to resolve the collisions in a decentralised way at the UEs' side.
Journal ArticleDOI
A Random Access Protocol for Crowded Massive MIMO Systems Based on a Bayesian Classifier
TL;DR: In this article , a Bayesian classifier is proposed to identify the strongest user in a decentralized way, aiming to resolve the collisions in a centralized way at the UEs' side.
References
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