Hierarchical cooperative combined spectrum sensing algorithm

Abstract

The present invention relates to the field of cognitive radio communication. The present invention provides a multi-layer distributed joint spectrum sensing method based on the Dirichlet process to realize dynamic spectrum sensing, and finds optimal sensing information by fusing sensing data collected by secondary users of multiple hierarchical centers. The Dirichlet process is used to realize the automatic grouping of data. The Bayesian model estimates a shared hyperparameter and the corresponding divergence probability in each group. The standard Viterbi algorithm is used to obtain the hyperparameters, and the hyperparameters are compared with the decision threshold. The comparison is performed to obtain the final spectrum decision result to determine whether the channel is available. The design fully considers the spatial diversity information of compressed sensing data, which reduces the uncertainty of a single secondary user on compressed sensing data, so that the normalized mean square error performance is better, and the algorithm can effectively mine the compressed sensing data information of each hierarchical center , to obtain a higher probability of correct detection and a smaller probability of false alarm, and improve the performance of spectrum sensing.

Description

technical field [0001] The invention relates to the field of cognitive radio communication, in particular to the realization of spectrum sensing of primary and secondary users. Background technique [0002] With the rapid development of wireless communication technology, spectrum resources are becoming increasingly tight. Traditional wireless networks adopt a fixed spectrum allocation policy, which is regulated and allocated to specific operators and service providers through specialized agencies. However, the results show that the use of this allocation for communication spectrum is very inefficient. The spectrum shortage is largely due to the inefficient fixed spectrum allocation mechanism. Therefore, the researchers proposed cognitive radio technology. The basic idea is: in Under the premise of not causing harmful interference to the primary user PU (Primary User) who owns the spectrum, the secondary user SU (Secondary User) connects to the temporarily idle primary user ...

AI Technical Summary

Problems solved by technology

At present, spectrum sensing needs to face the following main challenges: First, the sampling process must satisfy the Nyquist law in order to recover information correctly at the receiving end, and the current digital-to-analog converter with a limited sampling rate is difficult to implement for high-frequency signals , and the sampling overhead is also very large; secondly, the traditional joint spectrum sensing algorithm has a large communication overhead and poor stability, and it is difficult for spatially distributed SUs to perform spectrum sensing operations synchronously, and data fusion at the same time is prone to errors, and PUs distributed in space Each position and the transmission power is limited, different positions SU may have different spectrum detection results

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