A Maximum Eigenvalue Spectrum Sensing Method Using Previous Sensing Slot Data

Abstract

The invention discloses a maximum eigenvalue spectrum sensing method using previous sensing time slot data, which performs multiple samplings on signals from multiple channels for the current sensing time slot and multiple past sensing time slots, and obtains A plurality of samples; then multiple samples obtained by each sampling in the corresponding sensing time slot constitute a sample vector; then calculate the covariance matrix corresponding to the corresponding sensing time slot according to all the sample vectors obtained in the corresponding sensing time slot; after that Sort the traces of the covariance matrix corresponding to all previous sensing time slots in ascending order, and use the sorted traces to estimate the noise power; then according to the maximum eigenvalue and noise power of the covariance matrix corresponding to the current sensing time slot , to calculate the test statistic; finally, by comparing the test statistic with the judgment threshold, determine whether there is an authorized user signal in the current sensing time slot; the advantage is that it does not need to know the noise power itself, and the sensing performance is good.

Description

technical field [0001] The invention relates to a cognitive radio spectrum sensing technology, in particular to a maximum eigenvalue spectrum sensing method using previous sensing time slot data. Background technique [0002] The rapid development of diversified mobile communication services has greatly enriched and facilitated people's work and life. However, correspondingly, the number of wireless devices and the demand for mobile data traffic have exploded, which has led to a shortage of spectrum resources. In recent years, the problem of shortage of spectrum resources has gradually emerged, and it will become more serious in the foreseeable future. However, this is not caused by insufficient physical spectrum resources, but because the existing fixed spectrum allocation strategy makes many spectrum resources unable to be fully utilized, which greatly reduces spectrum utilization. Therefore, improving spectrum utilization becomes the key to solving this problem. In resp...

AI Technical Summary

Problems solved by technology

However, the traditional maximum eigenvalue spectrum sensing method needs to know the noise power, and the noise power is often unknown in practice. When the noise power is unknown, the traditional maximum eigenvalue spectrum sensing method uses an artificially set upper bound value of the noise power instead of noise power, which causes the problem of noise power uncertainty, which can seriously degrade the performance of spectrum sensing

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