Sequential cooperative spectrum sensing in cognitive radio networks: Optimal selection of secondary users and their spectral measurements

Abstract

In this paper we consider the problem of spectrum sensing in cognitive radio networks which involves detection of primary (licensed) users (PUs) by secondary (unlicensed) users (SUs), who are interested in transmitting their data opportunistically. To facilitate accurate detection of PUs by the fusion center (FC) based on the energy measurements received from the chosen set of SUs, we formulate an optimization problem for selection of SUs and the number of samples they need to collect of the underlying spectrum. By assuming that the FC uses a sequential probability ratio test (SPRT) for performing spectrum sensing we formulate the problem of joint optimization over subset of SUs and the number of samples each of the SU in the chosen subset needs to collect, so that a composite cost function is maximized. For the computation of the optimal subset of SUs and the number of samples each SU has to collect we propose an algorithm based on DSO, in which optimization over the subset of SUs and the number of samples is done successively till convergence to the optimal set of values is achieved. Our simulation results demonstrate the efficacy of the proposed optimization approach based on SPRT as against that of a fixed sample size test at the FC. Specifically, the average number of samples required for an SPRT is much lower than that of a fixed sample size test for given values of probability of detection and probability of false alarm. The simulation results also confirm tracking ability of the proposed DSO based algorithms, in response to variations in the corresponding channel gains between the SUs and the FC.