No abstract

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.

Spectrum sensing strategy is key to realize cognitive radio. However, spectrum sensing error would affect the access strategy of secondary users in cognitive networks. This paper addresses the spectrum sensing strategy under imperfect spectrum sensing, and proposes opportunistic spectrum access strategies for the imperfect spectrum sensing and fading channels respectively. By setting the optimal operating point of the spectrum detection and updating the confidence vector, we turn the spectrum access optimizations under the imperfect spectrum sensing and fading channels into ones under the perfect spectrum sensing based on the partially observable Markov decision process model. Simulation results show that the strategies proposed could make the secondary users achieve about 10% margin in throughput and the cognitive networks have higher spectrum utilization.