Novel evaluation framework for sensing spread spectrum in cognitive radio

Abstract

The cognitive radio network is designed to cater to the optimization demands of restricted spectrum availability. A review of existing literature on spectrum sensing shows that there is still a broader scope for its improvement. Therefore, this paper introduces an efficient computational framework capable of evaluating the effectiveness of the spread spectrum concept in the context of cognitive radio network in a more scalable and granular way. The proposed method introduces a dual hypothesis using a different set of dependable parameters to emphasize the detection of optimal energy for a low signal quality state over the noise. The proposed evaluation framework is benchmarked using a statistical analysis method not present in any existing approaches toward spread spectrum sensing. The simulated outcome of the study exhibits that the proposed system offers a significantly better probability of detection than the current system using a simplified evaluation scheme with multiple test parameters.