文献类型：期刊文献

英文题名：Locally Optimal Detection for Spectrum Sensing Under MA-Based Bernoulli-Gaussian Noise

作者：Yang, Yibang[1];Mou, Jie[1];Huang, Guanna[1];Dai, Jisheng[2];Lai, Huadong[3];Xu, Weichao[1]

机构：[1]Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China;[2]Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China;[3]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Guangdong, Peoples R China

年份：2026

卷号：15

起止页码：4976

外文期刊名：IEEE WIRELESS COMMUNICATIONS LETTERS

收录：SCI-EXPANDED(收录号：WOS:001868056400019)、、EI(收录号：20263721457042)、Scopus(收录号：2-s2.0-105049710986)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62571146 and Grant 62501175; and in part by Guangdong Science and Technology Department under Grant 2024A1515011803, Grant 2026A1515011625, and Grant 2026A1515011437.

语种：英文

外文关键词：Noise; Modeling; Detectors; Signal detection; Silicon; Signal to noise ratio; Correlation; Wireless communication; Gaussian noise; Design methodology; Cognitive radio; locally optimal detection; Bernoulli-Gaussian noise; moving average process

外文摘要：In cognitive radio spectrum sensing, the conventional additive white Gaussian noise assumption is often invalid due to impulsive behavior and temporal correlation in wireless noise. This letter models the noise as Bernoulli-Gaussian noise driven by a higher-order moving average process, capturing both non-Gaussianity and weak dependence. We apply the locally optimal detection framework to this MA-based BGN model, yielding explicit forms of the known-signal LOD and its blind counterpart, which operates without prior signal waveform knowledge. To facilitate practical use, we combine Durbin estimation with the fractional-order absolute-moment-based estimator, making the detector data-adaptive. Simulations demonstrate that the proposed LOD substantially outperforms existing detectors across various SNRs and correlation strengths.