A Sparse Bayesian Spectrum Sensing Method with Mixed Gaussian Noise

Abstract

The invention discloses a mixed Gaussian noise sparse Bayesian frequency spectrum sensing method which comprises the following steps: 1) constructing a compression frequency spectrum sensing system model affected by the mixed Gaussian noise; 2) exploring sparsity of a master use power spectrum signals according to the compression frequency spectrum sensing system model; 3) utilizing the sparsity of the master user power spectrum signals to reconstruct the master user power spectrum signals and further judging whether channels are occupied or not. The mixed Gaussian noise sparse Bayesian frequency spectrum sensing method disclosed by the invention is based on a virtual reference grid point scheme and utilizes a layered prior variational Bayesian algorithm to reconstruct power spectrum information of the master user power spectrum signals; thus, convenience is brought to secondary users to use limited frequency spectrum resources. According to the mixed Gaussian noise sparse Bayesian frequency spectrum sensing method disclosed by the invention, effects of the mixed Gaussian noise to recognizing a wireless system are taken into consideration; furthermore, the reconstructed master userpower spectrum signals are prevented from being judged again, and effective spectrum resource information can be obtained.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a mixed Gaussian noise sparse Bayesian spectrum sensing method. Background technique [0002] Compressed sensing can break the limitation of Nyquist sampling rate in wideband spectrum sensing, and obtain a small amount of observation data through low-speed sampling for spectral estimation of wideband signals, thereby detecting holes in wideband spectrum. In a cognitive radio system, since the spectrum of the primary user only occupies a small part of the system bandwidth and its geographic location is limited relative to the entire sensing area, rational use of the prior knowledge of the sparsity of the primary user's power spectrum signal can not only The communication equipment can be greatly simplified, and the main user power spectrum signal can be better reconstructed. [0003] The existing reconstruction algorithms for cognitive radio compressed ...

AI Technical Summary

Problems solved by technology

[0003] Existing reconstruction algorithms for cognitive radio compressed spectrum sensing only study the system model affected by additive white Gaussian noise

However, many interferences or noises in real life are not purely additive Gaussian white noise, such as man-made impulse noise, channel interference between cognitive users, and ultra-wideband interference. If Gaussian distribution is still used to fit the will cause large errors

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