Spectrum selection and decision using neural and fuzzy optimization approaches

In Cognitive Radio Network, after sensing process, the selection and decision for a reliable channel from the list of free channels is important for assignment to Cognitive Users (CUs) for communication with Quality of Service (QoS). In this paper a consistent spectrum selection and decision scheme with two-fold neural network has been proposed for selection and decision process and its performance is compared with the schemes of Genetic algorithm and Back Propagation Neural Network (BPNN). BPNN- Adaptive Neuro Fuzzy Inference System (ANFIS) is a two-fold spectrum selection and decision approach which combines both BPNN and ANFIS techniques. A channel with the required QoS is selected based on the parameters such as Primary User (PU) states, signal strength, spectrum demand, velocity and distance. The simulation analysis shows that the BPNN–ANFIS technique reduces probability of blocking and dropping and therefore the accuracy of reliable channel selection obtained for the CUs use is more than 92%. The blocking probability of the proposed technique ranges from 1 to 3% which is much lower than the Genetic Algorithm (9–50%) and BPNN (8–40%). The maximum dropping probability of the proposed technique is only 4% and this is lower compared to 20% dropping in the other two techniques.