Estimation of Distribution of Primary Channel Periods Based on Imperfect Spectrum Sensing in Cognitive Radio

Abstract

Spectrum occupancy modeling plays an important role in improving the performance of Cognitive Radio (CR) systems, because most of the methods to improve Dynamic Spectrum Access (DSA) performance need to make assumptions about the channel periods’ distribution. However, Secondary Users (SUs) cannot directly observe the accurate channel periods’ distribution due to the sensing errors caused by noise, which will lead to inaccurate analysis of transmission efficiency or throughput. In this paper, we analyze the influence of different types of sensing errors on the observed channel periods, and establish the relationship between the Probability Mass Function (PMF) of idle periods under Imperfect Spectrum Sensing (ISS) and the results under Perfect Spectrum Sensing (PSS). In addition, we derive a closed-form expression for estimating the PMF of channel periods from the sensing results under ISS. Simulation results show that the proposed estimation method is more accurate than the existing methods, and is not affected by the sensing period, channel mean period and sensing error probability.