Adaptive primary user's signal detection algorithm based on matrix accumulation estimation for cognitive radio

Hongjun Wang,Zifa Zhong,Guangguo Bi,Zaichen Zhang,Shuo Zhang

doi:10.1186/1687-6180-2011-51

Hongjun Wang, Zifa Zhong + Show 3 more

Open Access

https://doi.org/10.1186/1687-6180-2011-51

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Abstract

In this article, a new adaptive primary user's signal detection algorithm for cognitive radio, which integrates frequency-domain block adaptive filtering with a matrix accumulation estimation, is presented with detailed description' of the concept and process of the algorithm. Then, the performance of the algorithm is analyzed and compared by simulation according to the practical requirement of cognitive radio. The result shows that the proposed detection algorithm can detect weak signals quickly and effectively with higher detection probability.

Highlights

The cognitive radio [1,2,3,4,5] can sense and recognize the changes of the electromagnetic environment and borrow a spectrum-hole to transmit information in real-time manner without bringing about any interference to primary users and other users around
The cognition must run through the whole communication process
The chief task of cognitive radio is to scout and determine the spectrum-hole, that is, cognitive radio should have the ability of detecting weak primary user signals from strong background noise [6,7,8]

Summary

Introduction

The cognitive radio [1,2,3,4,5] can sense and recognize the changes of the electromagnetic environment and borrow a spectrum-hole to transmit information in real-time manner without bringing about any interference to primary users and other users around. Our application scene is GSM mobile system, the aim of the research project is to detect the GMSK mobile signal, find out the idle time slot and borrow it to communicate among the cognitive transceivers. Each detection channel of the cognitive radio equipments begins to detect the weak primary user’s signal in the frequencybandwidth assigned by the digital signal processor after startup. The result of the BFFTAA algorithm is the last N items of the Equation (9): Yk(f )

Matrix accumulation estimation

Findings

Conclusion