Non-Cooperative Cognitive Radio Optimization using Fuzzy Logic and Monte-Carlo Algorithms

Abstract: Cognitive Radio threshold optimization has been an on-going research concern. With mature energy detector algorithms, the CR implemented in this work uses an energy detector as the primary sensing module for the primary user signal. In order to achieve this detection in an efficient way, various techniques have been proposed in literature for optimization and different levels of optimization have been achieved. For this work, threshold optimization has been approached using two techniques for optimization, with Monte-Carlo and ANFIS fuzzy algorithms as the techniques of choice. The inherent gains observed from the techniques used show relative suitability to the task of threshold optimization in a CR system that is software based and shielded from the outside environment. From this work, it was seen that with a seed threshold setting of 14dBm, the optimization techniques used gave close correspondence for Monte-Carlo and Fuzzy logic. Further studies will require the integration of suitable hardware into the system to gain better knowledge of the CR behaviour and the impact of deduced optimal working threshold.

Keywords: Cognitive radio, energy detector, threshold optimization, Monte-Carlo, fuzzy logic, ANFIS.