Entropy based Spectrum Sensing in Cognitive Radio Networks

Abstract: Spectrum sensing is one of the important tasks of Cognitive Radio Networks (CRN). Though many spectrum sensing techniques are available, sensitivity to noise uncertainty is the basic limitation for these techniques. In this paper, an improved entropy based detection technique in frequency domain is proposed. This work investigates the detection performance using Renyi and Tsallis entropy methods in both single node as well as multi node scenario. Simulations were carried out using QPSK and OFDM signals. The performance is evaluated by considering fading channels like Rician, Rayleigh and Nakagami-m fading. The proposed method could achieve 3 dB improvement compared to the Shannon entropy technique, with and without fading channels. The results have shown that Renyi entropy outperforms Tsallis entropy with significant improvement in SNR wall.

Keywords: Cognitive radio, entropy detection, Shannon entropy, Renyi entropy, fading channels.