Probabilistic Opportunistic Access of Cognitive Radio Network using Partially Observable Markov Decision Process

Abstract

Growing demands of spectrum utilization make us to think about cognitive radio. Cognitive radio is smart enough to adjust its parameters according to spectrum use thereby increasing the efficiency of spectrum access. It means it should have the ability to make optimistic decisions dynamically in a multi operated network. Partially observable markov decision process is the technique of making optimistic decisions among various alternatives. MDPs are useful for studying a wide range of optimization problems solved via dynamic programming and reinforcement learning. In this paper, we demonstrate how POMDP is useful while using cognitive radio network.