Lifting Wavelet-Assisted EM Joint Estimation and Detection in Cooperative Spectrum Sensing

Spectrum sensing in Cognitive radio (CR) is a way to improve spectrum utilization by detecting spectral holes to achieve a dynamic allocation of spectrum resources. As it is often difficult to obtain accurate wireless environment information in real-world scenarios, the detection performance is limi...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-08, Vol.23 (17), p.7428
Main Authors: Tian, Hengyu, Zhao, Xu, Chen, Shiyong, Wu, Yucheng
Format: Article
Language:English
Subjects:
Algorithms
Communication
cooperative spectrum sensing
Eigenvalues
expect the maximum algorithm
Hypotheses
lifting wavelet
likelihood ratio test
Methods
Wavelet transforms
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Description
Summary:Spectrum sensing in Cognitive radio (CR) is a way to improve spectrum utilization by detecting spectral holes to achieve a dynamic allocation of spectrum resources. As it is often difficult to obtain accurate wireless environment information in real-world scenarios, the detection performance is limited. Signal-to-noise ratio (SNR), noise variance, and channel prior occupancy rate are critical parameters in wireless spectrum sensing. However, obtaining these parameter values in advance is challenging in practical scenarios. A lifting wavelet-assisted Expectation-Maximization (EM) joint estimation and detection method is proposed to estimate multiple parameters and achieve full-blind detection, which uses lifting wavelet in noise variance estimation to improve detection probability and convergence speed. Moreover, a stream learning strategy is used in estimating SNR and channel prior occupancy rate to fit the scenario where the SU has mobility. The simulation results demonstrate that the proposed method can achieve comparable detection performance to the semi-blind EM method.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23177428