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Journal ArticleDOI

Channel Detection Under Impulsive Noise and Fading Environments for Smart Grid

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.

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Citations
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Journal ArticleDOI

Adaptive Demodulation in Impulse Noise Channels

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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Journal ArticleDOI

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Journal ArticleDOI

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

TL;DR: An overview of the Internet of Things with emphasis on enabling technologies, protocols, and application issues, and some of the key IoT challenges presented in the recent literature are provided and a summary of related research work is provided.
Journal ArticleDOI

Multitarget Bayes filtering via first-order multitarget moments

TL;DR: Recursion Bayes filter equations for the probability hypothesis density are derived that account for multiple sensors, nonconstant probability of detection, Poisson false alarms, and appearance, spawning, and disappearance of targets and it is shown that the PHD is a best-fit approximation of the multitarget posterior in an information-theoretic sense.
Journal ArticleDOI

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

TL;DR: The relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber- physical world, are explored and existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development.
Book

Statistical Multisource-Multitarget Information Fusion

TL;DR: This comprehensive resource provides an in-depth understanding of finite-set statistics (FISST) - a recently developed method which unifies much of information fusion under a single probabilistic, in fact Bayesian, paradigm.
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