Performance analysis of Rice-Lognormal channel model for spectrum sensing

Shadowing is defined as a slower process usually caters into large scale fading. In addition, shadowing effect also produces small scale fading. Thus, introduces basic limits on spectrum sensing methods in cognitive radio. Hence, instead of classical distribution based channels for modeling shadow fading, a composite or mixed distribution is much more demanding. This paper quantifies spectrum sensing in shadow fading condition, with the help of Rice-Lognormal (RLN) distributed fading channel. We outline some closed form approximations for Energy detection based spectrum sensing in RLN channel. Further, we compare sensing performance of RLN channel with Rayleigh, Rice and Lognormal fading channels. Our simulation results interpret the performance improvement in spectrum sensing over RLN channel.