Cognitive Radio Decision Engine Based On Multi-Objective Genetic Algorithm

With the fast development of wireless communication technology in various areas and the rising of people's demand on wireless communication services, spectrum scarcity issues become more prominent. Meanwhile, the existing fixed spectrum allocation system resulting in a lower utilization of spectrum resources. Cognitive radio is proposed and considered an effective technique to solve this problem; it has changed the traditional static spectrum allocation management system and can intelligently sense and utilize idle spectrum, achieving spectrum reuse, which greatly improved spectrum utilization.Cognitive radio is able to adjust the transmission control parameters according to the perceived channel conditions, user requirements and other information given by sensed wireless environment. This parameter adjustment function is often described as cognitive radio decision engine, which is a dynamic multi-objective optimization problem. Existing genetic algorithms are difficult to assign the weight of each objective and some optimal solutions might be omitted while the linear weighting method is used to simplify the multi-objective optimization problem into a single objective optimization problem. This paper made a deep research to multi-objective problems and multi-objective genetic algorithm. Based on the above, this paper proposed a new multi-objective genetic algorithm based on cloud theory, and introduced it to cognitive radio decision engine problem. Firstly, the algorithm decides a group of fitness functions. It then runs the multi-objective genetic algorithm to get a pareto optimal set. At last, it chooses a most satisfactory solution according to the user's service requirement. A multi-carrier system with 32 sub-carriers is used for simulation analysis, and experimental results show that the proposed algorithm is feasible and effective. Meanwhile, a population adaptation technique is used for genetic algorithm in order to improve the convergence speed and meets the real-time communication requirement.