Performance Improvement in Wideband Spectrum Sensing Under Fading: Use of Diversity

Diversity is used to combat fading and shadowing in narrowband spectrum sensing. In this paper, we propose two new detection algorithms for wideband spectrum sensing that use square law combining (SLC) diversity to improve the detection performance. We analyze the performance of these algorithms under Nakagami fading. The proposed schemes include channel by channel square law combining (CC-SLC) and ranked square law combining (R-SLC). We first provide the asymptotic analysis for decision statistic of received energy to simplify the analysis. The approximated probability density function (pdf) to decision statistic is used to derive the pdf of received energy for $P$ number of diversity branches. The performance of the proposed algorithms is measured in terms of probability of insufficient spectrum opportunity ( $P_{\text{ISO}}$ ) and probability of excessive interference opportunity ( $P_{\text{EIO}}$ ). The analysis provided in this paper is general and can be used with any fading model and diversity scheme. Experiments are carried out using theoretical analysis and also verified by using Monte-Carlo simulation. Our analysis shows that the proposed algorithms using diversity outperform the channel by channel and ranked channel detection algorithms used under no diversity. Our results also indicate that among the proposed detection algorithms R-SLC performs better.