ANFIS with Subtractive Clustering-Based Extended Data Rate Prediction for Cognitive Radio

Cognitive radio has emerged as intelligent wireless technology for solving the ever-growing demand of radio spectrum.Cognitive radio is a context aware radio, capable of observing the channel and networks parameters and make autonomously decisions on the best transceiver configuration. Cognitive radio can be made adaptive by utilizing intelligent software techniques. In this paper, we propose Subtractive Clustering with ANFIS based adaptive technique so that it works intelligently to select particular radio configuration. The system considers different time zones and subtractive clustering is used to assist ANFIS in selecting optimum number of rules and membership function. The performance of this is seen to be better than the neural network and ANFIS scheme. KeywordsCognitive radio (CR), spectrumhole, cognition cycle, ANFIS, subtractive clustering, extended scheme