A random signal detection method based on non-reconfiguration sequential compression in cognitive network

Abstract

The invention provides a random signal detection method integrating an individual cognition user and multiple users in a distributive manner on the basis of non-reconstruction sequential compression. The method comprises the steps of sampling and projection transforming a signal of a broadband main user in a cognitive radio network, calculating the detection statistic quantity and the likelihood ratio by directly utilizing a low-speed observation sequence, and comparing the likelihood ratio with a judgment threshold; resampling if the detection precision cannot meet the requirement of the user, forming a novel low-speed observation vector through the preliminary low-speed observation vector, and rejudging; repeating the steps until the precision meets the requirement of the user. By adopting the method, not only can the advantage that the compression sampling data processing quantity is low be maintained, but also the signal reconstruction can be completely avoided, more importantly any prior information of a main user signal is not needed, and the compression ratio can be self-adaptively adjusted. On the premise of guaranteeing the detection precision, the observation number can be reduced as far as possible, and the calculation quantity is reduced, so that the time expenditure can be saved, and the detection real-time property can be improved. The effectiveness and accuracy of the method are verified in a simulation manner.

Description

technical field [0001] The invention relates to the field of wireless communication, in particular to a spectrum detection method for cognitive radio, and more particularly to a spectrum detection method for random signals under the condition of unknown spectral sparsity. Background technique [0002] With the rapid development of various wireless applications, existing spectrum resources have become increasingly difficult to meet people's increasing wireless access needs. For this reason, Cognitive Radio Network (CRN) technology came into being. It is an intelligent wireless communication system with environmental awareness and the ability to adjust communication parameters autonomously to adapt to changes in the external wireless environment, thereby achieving the two goals of providing reliable communication and efficient use of radio frequency spectrum. Among them, the role of spectrum sensing is to determine the occupancy of the spectrum as quickly and accurately as po...

AI Technical Summary

Problems solved by technology

However, in practice, signal sparsity is often unknown or even time-varying

When the preset signal sparsity is greater than the actual situation, redundant observation sequences will be generated. These redundant observation sequences have no obvious effect on further improving the detection performance, but will cause additional compression measurement and subsequent processing time overhead; on the contrary, The preset compression ratio may be too low, resulting in fewer observation sequence samples, which is difficult to meet the user's detection accuracy requirements

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