Time domain averaging and correlation-based improved spectrum sensing method for cognitive radio

Shenghong Li,Guoan Bi

doi:10.1186/1687-6180-2014-46

Abstract

Based on the combination of time domain averaging and correlation, we propose an effective time domain averaging and correlation-based spectrum sensing (TDA-C-SS) method used in very low signal-to-noise ratio (SNR) environments. With the assumption that the received signals from the primary users are deterministic, the proposed TDA-C-SS method processes the received samples by a time averaging operation to improve the SNR. Correlation operation is then performed with a correlation matrix to determine the existence of the primary signal in the received samples. The TDA-C-SS method does not need any prior information on the received samples and the associated noise power to achieve improved sensing performance. Simulation results are presented to show the effectiveness of the proposed TDA-C-SS method.

Highlights

Cognitive radio (CR) networks allow unlicensed users to opportunistically exploit the underutilized spectrum bandwidth of the licensed users
Spectrum sensing is a key operation performed by the CR networks to determine the spectrum holes of the spectrum allocated to a primary user
The energy detection methods need the prior knowledge of noise power and are vulnerable to the noise uncertainty

Summary

Introduction

Cognitive radio (CR) networks allow unlicensed (or secondary) users to opportunistically exploit the underutilized spectrum bandwidth of the licensed (or primary) users. By making use of time domain averaging, the SNR of the received samples is increased. 3.1 Sample time domain averaging Time domain averaging is an effective method to decrease noise power for periodic signal detection [13] (and therein [13]). For the obtained data sample sequence z(n), n ∈[ 1, N0] , the time average operation is defined by e(n) =. M = 5, for example, the SNR of the time averaged output is increased by about 10 dB This gain in SNR is very valuable for effective spectrum sensing in the environment of strong noise

Correlation operation and sensing decision

Simulation for case I