A Cooperative Spectrum Sensing Method Based on Consistent Estimation of Eigenvalues

Abstract

The invention discloses a cooperative spectrum sensing method based on feature value consistent estimation. The method comprises the specific realization steps that S1, a macro base station sends an authorized user signal; S2, a low-power small cellular base station receives the signal and carries out continuous sampling; S3, the low-power small cellular base station sends sensing data to a data fusion center and a receiving sample matrix is formed at the data fusion center; S4, the data fusion center obtains judgment statistic according to the receiving sample matrix; and S5, the data fusion center compares the judgment statistic with a judgment threshold, thereby judging whether an authorized user exist or not. According to the method, the spatial structure feature of the sending signal is taken into consideration and compared with a method of directly estimating a covariance matrix, the method has the advantage that the spectrum sensing performance is improved.

Description

technical field [0001] The invention belongs to the field of wireless communication technology, and relates to spectrum sensing in cognitive radio technology and high-dimensional eigenvalue estimation in estimation theory, in particular to a cooperative spectrum sensing method based on consistent eigenvalue estimation. Background technique [0002] With the rapid development of wireless communication services and the wide application of wireless communication devices, mobile data traffic has shown explosive growth. In today's shortage of spectrum resources, traditional macro cellular networks have limited expansion of network capacity, and the innovation of new wireless network architecture has become an inevitable trend to support network capacity. Dense heterogeneous network deploys low-power small cell base stations densely within the coverage of macro cells. On the one hand, it can achieve multi-layer enhanced coverage and edge user performance improvement, and improve s...

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Problems solved by technology

At present, there are relatively few sensing methods for high-dimensional data. The existing maximum-minimum eigenvalue spectrum sensing algorithm based on the covariance matrix shrinkage estimator (OAS-MME, Estimated Covariance MatrixMaximum–Minimum Eigenvalue Detection Based on Oracle Approximating ShrinkageEstimator) uses The shrinkage estimator estimates the statistical covariance matrix to improve the performance of spectrum sensing, but the direct estimation of the high-dimensional covariance matrix fails to make full use of the spatial structure of the transmitted signal, and the spectrum sensing performance needs to be further improved

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