Genetic fuzzy scheduler for spectrum sensing in cognitive radio networks

Spectrum sensing is a critical issue in any cognitive radio (CR) system. Many factors can affect the overall system performance and one of them is the order of sensing attempts of multiple channels. As the number of channels under consideration increases, the complexity of scheduling problem increases as well. In addition, the problem is elevated if channels have different behaviors and characteristics which is normally the common case. This paper proposes two scheduling techniques for spectrum sensing operations in cognitive-based IEEE 802.11 system. The first technique adopts Fuzzy Inference System (FIS) approach to set the optimal order of sensing attempts. However, FIS scheduler is static and cannot adapt to the changing behavior of wireless environment. Therefore, Genetic Algorithm (GA) is used to give the proposed FIS scheduler the ability to evolve and adapt to new environment conditions. The second technique proposed by this paper is heuristic-based scheduling algorithm. This algorithm is used as supporting mechanism for the genetic FIS scheduler. Several simulation experiments were conducted. They showed the outperformance of the proposed solution by achieving 300 Mbps increase in goodput.