Estimation of Channel State Transition Probabilities Based on Markov Chains in Cognitive Radio

—Prediction of spectrum sensing and access is one of the keys in cognitive radio (CR). It is necessary to know the channel state transition probabilities to predict the spectrum. By the use of the model of partially observable Markov decision process (POMDP), this paper addressed the spectrum sensing and access in cognitive radio and proposed an estimation algorithm of channel state transition probabilities. In this algorithm, the historical statistics information of channel is used to estimate the channel state transition probabilities, and the Least Square (LS) criterion is used to minimize the fitting error. It is showed that the channel state transition process is a special Markov chain, in which the channel state has only one state within each slot. The relationship between estimation precision and the number of converging observation samples is derived. The more the historical statistics information is, the higher the estimation accuracy is. Simulation results showed the estimated error of the LS algorithm is smaller than the linear estimation algorithm.