Spectrum sensing in cognitive radio networks

Abstract

In this thesis, we consider the problem of secondary users’ selection for cooperative spectrum sensing in cognitive radio networks. We assume that the secondary users involved in sensing transmit their spectral energy measurements to the fusion center. The fusion center performs maximal ratio combining (MRC) before taking a decision on the presence or absence of primary users. MRC reduces the complexity of decision rule and also facilitates in computation of the expressions for probability of detection and false alarm. Due to the discrete nature of the parameter set (which is nothing but the indices of the secondary users) and the fact that we have to work with estimates of the underlying objective function we pose the problem of secondary users’ selection under the discrete stochastic optimization framework. For this purpose we assume the objective function to be the probability of detection with the probability of false alarm set to a desired value. Owing to the high computational complexity associated with the exhaustive search, we propose an algorithm that appeared in operations research literature which spends most of its time near the global optimizer. The algorithm can also naturally track the optimal secondary users subset due to variations in channel gains. We also extend the above optimization framework to even include the case of antenna selection for cases when the secondary users are equipped with multiple antennas <p/>for sensing. Our simulation results demonstrate the efficacy of the proposed approach.