Adaptive Channel Recommendation for Dynamic Spectrum Access

—We propose a dynamic spectrum access scheme where secondary users recommend “good” channels to each other and access accordingly. We formulate the problem as an average reward based Markov decision process. We show the existence of the optimal stationary spectrum access policy, and explore its structure properties in two asymptotic cases. Since the action space of the Markov decision process is continuous, it is difficult to find the optimal policy by simply discretizing the action space and use the policy iteration, value iteration, or Q-learning methods. Instead, we propose a new algorithm based on the Model Reference Adaptive Search method, and prove its convergence to the optimal policy. Numerical results show that the proposed algorithm achieves up to 18% performance improvement than the static channel recommendation scheme and 10% performance improvement than the Q-learning method, and is robust to channel dynamics.

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Xu Chen, Jianwei Huang, Husheng Li
• Keywords: CORR 2011, Education, Markov Decision Process, Optimal Policy, Spectrum Access