Modeling of Learning Inference and Decision-Making Engine in Cognitive Radio

Abstract

Cognitive radio (CR) is an intelligent wireless communication system and the core of it is the cognitive engine. Cognitive engine is expected to implement cognitive learning, inference, decision-making through the artificial intelligence technology to decide a specific radio configuration (i.e. carrier frequency, modulation type, power, etc.) according to the changing of environment. In this paper, a cognitive radio learning inference and decision-making engine based on Bayesian network (BN) is proposed to obtain the optimum configuration rules adapt to the variation of the environment with the learning and inference algorithm of Bayesian network. Simulation results show the feasibility and validity of modeling the cognitive learning inference and decision-making engine with Bayesian network.