A Chaotic Genetic Algorithm for Radio Spectrum Allocation Olawale David Jegede, Ken Ferens, Witold Kinsner Dept. of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada {jegedeo@cc.umanitoba.ca, Ken.Ferens@ad.umanitoba.ca, Witold.Kinsner@ad.umanitoba.ca} Abstract—A Chaotic Genetic Algorithm (CGA) for Cognitive Radio spectrum allocation procedure is presented. The development of the Cognitive radio system puts emphasis on the efficient utilization of spectrum for both primary and secondary users. Secondary users make use of the spectrum without degrading the quality of service of the primary user(s). We assume that spectrum sensing has been done; thus a secondary user can specify the Quality of Service (QoS) requirements for a particular application at any given time. A Genetic Algorithm is used for the spectrum allocation. We have compared the performance of a Traditional Genetic Algorithm (TGA) with the chaotic counterpart. The simulation shows that the CGA converges faster with better fitness than the TGA. The simulation has been modeled using MATLAB. Keywords— Cognitive Radio, Quality of Service, Genetic Algorithm, Traditional Genetic Algorithm, Chaotic Genetic Algorithm, Adaptive Genetic Algorithm. INTRODUCTION I. In the past two decades, the use of wireless applications has increased rapidly eventually leading to an increased demand of bandwidth. This higher demand of bandwidth has resulted in two main problems: spectrum scarcity and underutilization. Cognitive Radio (CR) concept was introduced to solve this problem. Cognitive radio involves secondary users borrowing free spectrum not being used by the primary users without degrading the quality of service of the primary user’s communication. The CR therefore must be able to sense available spectrum, establish and maintain quality of service (QoS) requirements for user’s application, meet service level agreement (SLA) and understand its own operational capabilities such as radio parameters [1]. The underlying objective of this work is to use a chaotic genetic algorithm (CGA) to implement a spectrum allocation process in which decisions to assign a spectrum are made according to the radio user’s QoS requirements. Genetic algorithm (GA) is a subset of evolutionary algorithms that models biological processes to optimize highly complex functions. A GA allows a population composed of many individuals to evolve under specified selection rules to a state that maximizes the “fitness” (i.e. minimize the objective function). The main advantage of using GA over other stochastic techniques is its parallelism, which speeds up the simulation results leading to faster convergence. It is important that a solution is found in good time because time plays an important role in real time applications especially for a CR. Some other significant advantages of using of the GA include its ability to deal with a large number of variables [1]. While GA can provide a single solution, it can also provide a list of optimum solutions; this is particularly good for multi-objective problems. Continuous or discrete variables can be optimized with the GA and it can also encode variable so that the optimization is done with the encoded variables. Moreover, genetic algorithms are less likely to get stuck in local minima owing to its crossover and mutation processes. Therefore, it is a suitable approach to the spectrum allocation problem. For the purpose of distinguishing between a chaotic genetic algorithm and a typical GA, the typical GA will be referred to as traditional genetic algorithm (TGA). Traditional Genetic Algorithms use a random process to generate parameter values for the selection, crossover and mutation processes. Random number generators are designed to result in either uniform distributions or Gaussian distributions [2]. We conjecture that selection, crossover and mutation in genetics are driven by a random non-linear dynamics process rather than a random process. Therefore in the spectrum allocation process, a chaotic logistic map is incorporated into the initial population generation as well as in the crossover and mutation processes of TGA. We have compared results obtained through the chaotic process with that obtained using the traditional genetic algorithm process. A coupled chaotic genetic algorithm (CGA) strategy is therefore proposed [3]. Chaotic phenomena, which exists in nonlinear systems is an irregular motion, seemingly unpredictable random behavior under deterministic conditions [4]. Introducing chaos into the whole process of a traditional genetic algorithm may help improve convergence time and accuracy. The CGA takes full advantage of the chaotic characteristics of the