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Improved Weighted Covariance Based Detector for Spectrum Sensing in Rayleigh Fading Channel

Lai, Huadong, Liu, Mingxing, Xu, Jinqiang, Luo, Peng, Chen, Changrun, Xu, Weichao (2024) Improved Weighted Covariance Based Detector for Spectrum Sensing in Rayleigh Fading Channel. IEEE Wireless Communications Letters, . ISSN 2162-2337. E-ISSN 2162-2345. (doi:10.1109/lwc.2024.3379528) (KAR id:105482)

Abstract

In this letter, we propose an improved weighted covariance based detector (IWCD) for spatially correlated time-varying Rayleigh fading channel. The proposed method uses adaptive weights that are tailored to the dynamic nature of the channels. These weights can be chosen manually to meet practical requirements or derived theoretically by optimizing some performance index, such that the IWCD outperforms traditional weighted covariance-based detectors (WCDs), which rely heavily on data-aided weights determined by the sample covariance matrix (SCM). Performance merits in terms of the probabilities of false alarm and detection are analyzed in the low signal-to-noise-ratio (SNR) regime. Besides, the optimal weights are derived via maximizing the modified deflection coefficient (MDC). A reasonable estimator of the optimal weights is also constructed armed with the available samples at hand. Theoretical analyses and experimental results demonstrate the superiority of our proposed method over existing works in various scenarios.

Item Type: Article
DOI/Identification number: 10.1109/lwc.2024.3379528
Additional information: © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled keywords: Electrical and Electronic Engineering, Control and Systems Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: National Natural Science Foundation of China (https://ror.org/01h0zpd94)
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 17 Apr 2024 10:21 UTC
Last Modified: 05 Nov 2024 13:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/105482 (The current URI for this page, for reference purposes)

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