Adaptive Channel Recommendation for Opportunistic Spectrum Access
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AbstractWe propose a dynamic spectrum access scheme where secondary users cooperatively 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 percent performance improvement than the static channel recommendation scheme in homogeneous and heterogeneous channel environments, respectively, and is more robust to channel dynamics.
All Author(s) ListChen X, Huang JW, Li HS
Journal nameIEEE Transactions on Mobile Computing
Detailed descriptionPublished online in IEEE Xplore as an "Early Access Article".
Volume Number12
Issue Number9
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1788 - 1800
LanguagesEnglish-United Kingdom
KeywordsCognitive radio; dynamic spectrum access; model reference adaptive search; recommendation system
Web of Science Subject CategoriesComputer Science; Computer Science, Information Systems; COMPUTER SCIENCE, INFORMATION SYSTEMS; Telecommunications; TELECOMMUNICATIONS

Last updated on 2020-20-10 at 01:18