Studies on development of differential evolution based spectrum allocation algorithms and field programmable gate array omplementation for cognitive radio networks

Abstract

Recent trends in wireless communication technologies claim a rapid in- crease in demand of radio spectrum. In the current spectrum allocation scheme, it is difficult to accommodate the demand of radio spectrum. Moreover the desig- nated spectrum are not efficiently exploited, resulting its poor utilization. Stud- ies have demonstrated that reuse of the un-utilized spectrum provides a signifi- cant improvement in network capacity. Recently, a new dynamic spectrum access paradigm called Cognitive Radio (CR) has gained popularity to solve the short- comings of spectrum under-utilization and spectrum scarcity. In CR technology, unlicensed users (secondary users) make use of the unused spectrum of licensed users (primary users), thereby discovering a new capacity and commercial value from the existing unused spectrum. The main functions of the CR are spec- trum sensing, spectrum management, spectrum mobility and spectrum sharing. Spectrum sensing deals with the detection of vacant spectrum bands known as spectrum holes and these detected holes are assigned to the secondary users (SUs) during spectrum management phase. It uses different spectrum allocation (SA) algorithms for allocating spectrum to SUs. The present thesis mainly concen- trates on spectrum allocation phase. The objectives of SA phase are a) maximize the spectrum utilization, b) minimize interference to primary users (PUs) and neighbor secondary users and c) maintain fairness across the users. To achieve these goals, an efficient SA technique is required for making deci- sions within a stipulated time. For this purpose, various techniques like graph col- oring, game theory, evolutionary algorithms, local bargaining, auction and pricing mechanisms and stochastic search methods have been reported in the literature. The problem of allocating channels amongst the secondary users in the network is considered as a NP-hard problem. In this work, evolutionary algorithms, namely Differential Evolution (DE), firefly and particle swarm intelligence are applied to find an efficient channel assignment solution. Further, the performance of three algorithms in terms of quality of solution and time complexity are compared to find the best solution