Research Article
A Novel Spectrum Correlation Based Energy Detection for Wideband Spectrum Sensing
@INPROCEEDINGS{10.1007/978-3-030-41114-5_17, author={Bo Lan and Tao Peng and PeiLiang Zuo and Wenbo Wang}, title={A Novel Spectrum Correlation Based Energy Detection for Wideband Spectrum Sensing}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part I}, proceedings_a={CHINACOM}, year={2020}, month={2}, keywords={Cognitive radio (CR) Wideband spectrum sensing (WBSS) Energy detection (ED) Spectrum correlation Detection window}, doi={10.1007/978-3-030-41114-5_17} }- Bo Lan
Tao Peng
PeiLiang Zuo
Wenbo Wang
Year: 2020
A Novel Spectrum Correlation Based Energy Detection for Wideband Spectrum Sensing
CHINACOM
Springer
DOI: 10.1007/978-3-030-41114-5_17
Abstract
With the rapid development of wireless communications technology, the problem of scarcity of spectrum resources is becoming serious. Cognitive radio (CR) which is an effective technology to improve the utilization of spectrum resources is getting more and more attention. Spectrum sensing is a key technology in cognitive radio. Wideband spectrum sensing (WBSS) can help secondary users (SUs) find more spectrum holes. However, for the traditional energy detection (ED) algorithm, when the signal-to-noise ratio (SNR) of the primary user (PU) is low, the detection performance is extremely poor owing to the single frequency point detection method. Therefore, the concept of spectrum correlation is proposed. Spectrum correlation algorithm uses the detection window to realize joint detection of multiple frequency points which can improve performance. This paper focuses on how to make the best of spectrum correlation to ensure the detection performance for low SNR signals. We propose an adaptive detection window (ADW) method, whose detection window is adaptively selected based on the estimated SNR of signal. The method can be directly used for wideband spectrum sensing when the approximate position of each signal and its estimated SNR are known. In this context, to show the robustness of the ADW method, a simulation of the sensitivity of the ADW method to the SNR estimation error is performed. Meanwhile, simulations of methods comparison demonstrate that the proposed ADW method outperforms the commonly used iterative energy detection method, frequency correlation methods and histogram-based segmentation method by far.
