Adaptive Channel Recommendation For Opportunistic Spectrum Access

TL;DR

This paper introduces a dynamic spectrum access scheme where secondary users recommend channels to each other, formulated as an MDP, and proposes a new adaptive search algorithm that outperforms static schemes in various environments.

Contribution

It develops a novel adaptive channel recommendation algorithm based on Model Reference Adaptive Search for opportunistic spectrum access, with proven convergence and significant performance gains.

Findings

Achieves up to 18% performance improvement in homogeneous environments.

Achieves up to 100% performance improvement in heterogeneous environments.

Demonstrates robustness to channel dynamics.

Abstract

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 algorithms achieve up to 18% and 100% performance improvement than the static channel recommendation scheme in homogeneous and heterogeneous channel environments, respectively, and is more robust to channel dynamics.