ABSTRACT
Diversity is used to combat fading and shadowing in narrowband spectrum sensing. In this chapter, detection algorithms for wideband spectrum sensing are discussed that use square law combining (SLC) and square law selection (SLS) 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), ranked square law combining (R-SLC), and ranked square law selection (R-SLS). The asymptotic analysis for the decision statistic of received energy is carried out 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 algorithms is measured in terms of probability of insufficient spectrum opportunity (PISO) and probability of excessive interference opportunity (PEIO). The analysis provided in this chapter 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. The 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.
