On the sensing sample size for the estimation of primary channel occupancy rate in cognitive radio

Abstract

Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems can benefit from the knowledge of the activity statistics of primary channels. A particularly relevant statistic is the Channel Occupancy Rate (COR) of a primary channel, which represents the probability that a channel is occupied by a primary user. The COR can be estimated based on a set of binary (idle/busy) spectrum sensing decisions. However, an important practical question is how many sensing observations are necessary (i.e., the sensing sample size) in order to estimate the COR of a primary channel to a certain desired level of accuracy. This work analyses the problem of estimating the COR of a primary channel based on spectrum sensing decisions and derives a tight closed-form expression for the required sensing sample size. Moreover, an iterative algorithm is proposed to accurately determine the sensing sample size required to estimate the primary COR to a desired level of accuracy. The obtained results demonstrate the ability of the proposed algorithm to make an arbitrarily accurate estimation of the real COR of unknown primary channels using the essential minimum number of sensing samples.