Efficient Spectrum Sensing in Cognitive Radio Networks Using Hybridized Particle Swarm Intelligence and Ant Colony Algorithm

Abstract

Cognitive Radio is a technology that enables unlicensed users (referred to as Secondary users) to use the spectrum of the licensed users (i.e. the Primary Users) whenever the licensed user is not transmitting data. This utilizes the spectrum efficiently.  Cognitive Radio Networks (CRNs) detect Primary Users (PUs) using spectrum sensing and utilizes the spectrum holes (or vacant bands) for the transmission of data of Secondary User (SU). Spectrum sensing is carried out in a fixed time period called ‘Time Frame’. This Time Frame is divided into sensing time and transmission time. Higher sensing time will lead to better detection of PU but will lead to lesser transmission time and hence lesser throughput. On the contrary, if transmission time is higher, then sensing time is less, so PU detection will be compromised. It also leads to PU interference. There is a need of a tradeoff between sensing time and transmission time. Thus, there is a need for some optimal sensing time at which there is maximum possible throughput and no interference with the licensed user. This paper proposes a hybridized Ant Colony Optimization (ACO) - Particle Swarm Optimization (PSO) technique for spectrum sensing in cognitive radio networks. The results depict that the proposed technique is better than standalone optimization technique in terms of total error rate, throughput, probability of detection for varying sensing time and probability of false alarm.
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