Multi-user multi-channel collaborative spectrum sensing method based on filter bank

Abstract

The invention relates to a multi-user multi-channel collaborative spectrum sensing method based on a filter bank. Firstly, a multi-user multi-channel collaborative sensing system model is set up, wherein the multi-user multi-channel collaborative sensing system model comprises a PU, a PU-SU channel, an SU, an SU-FC channel and an FC. Under this model, the SU receives perception signals transmitted by the PU-SU channel through a receiver based on the filter bank. Then, the local perception information received through the SU-FC channel is reported to the FC. Finally, the FC makes the final judgment for the received local perception information through a linear weighted soft decision method after comprehensively taking the reporting channels, the collaboration mechanism and the space-spectrum diversity into consideration, and judges whether a PU signal or available channels exist or not. The multi-user multi-channel collaborative spectrum sensing method based on the filter bank can effectively coordinate the trade-off relation between the collaboration mechanism and the space-spectrum diversity in a cognitive radio network, so that sensing expenses are reduced and sensing efficiency is improved.

Description

technical field [0001] The invention relates to a multi-user multi-channel cooperative spectrum sensing method, in particular to a filter bank-based multi-user multi-channel cooperative spectrum sensing method. Background technique [0002] In Cognitive Radio Network (CRN), in order to improve the sensing efficiency and frequency utilization, efficient spectrum sensing technology is required, that is, the secondary user (Secondary User, SU) needs to avoid the primary user (Primary, PU ) to find as many free frequency band resources as possible under the premise of generating interference. [0003] The traditional spectrum sensing method is implemented based on one-by-one scanning technology. The basic principle is to use an adjustable narrow-band low-pass filter in the RF front-end to sense each frequency band according to a certain sequence and narrow-band detection algorithm. It mainly includes energy detection method, cyclostationary feature detection method, matched fil...

AI Technical Summary

Problems solved by technology

However, these methods still have many disadvantages: the compressed sensing method requires a high system cost; the wavelet detection method has a relatively serious spectrum diffusion; the spectrum sensing method based on the filter bank has not yet mentioned the multi-channel sensing strategy.

In addition, the above-mentioned traditional spectrum sensing methods and their improved technologies will also be greatly affected by factors such as interference in the wireless communication network, receiver uncertainty, multipath fading, masking effect, etc., resulting in limited sensing efficiency.

Furthermore, the above-mentioned spectrum sensing technology is only aimed at the spectrum sensing problem in the case of single-user and multi-channel, while the practical cognitive radio system is often a multi-user and multi-channel scenario

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