Multi-channel Access Algorithm with Channel State Information Unknown

The partially observed Markov decision process (POMDP) is very important in cognitive radio network. We can formulate the opportunistic spectrum access in the framework of POMDP. However, no attention was paid to the algorithm in the presence of unknown environment. For the rapidly changing cognitive network with unknown environment, we firstly establish the channel state model based on MLA and prove the MLA convergence. We consider a more practical scenario where the exact channel state transition probabilities are unknown and the sensing error exists. Secondly, based on the model has been established, we proposed the multi-channel access algorithm in one slot and put forward discount factor to achieve the reasonable tradeoff between the cost of sensing overhead and access. The simulation results showed that the performance of proposed multi-channel access algorithm outperform the single-channel optimal access.