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Reinforcement Learning for Radio Resource Management of Hybrid-Powered Cellular Networks

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Abstract

In this paper, we consider cellular networks powered by both renewable energy and the Smart Grid. We study the problem of minimizing the cost of on-grid energy while maximizing the satisfaction of users with different requirements. We consider patterns of renewable energy generation, traffic variation and real-time price of grid energy. Knowing that these patterns are all time related, we use Q-learning to extract a common pattern as well as to decide the number of radio resource blocks activated to maximize the users' satisfaction and minimize the on-grid energy cost. Results show that using Q-learning achieves a good tradeoff with more than 75% reduction in energy cost and negligible degradation in users' satisfaction.
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Dates and versions

hal-02294149 , version 1 (23-09-2019)

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Hadi Sayed, Ali El Amine, Hussein Al Haj Hassan, Loutfi Nuaymi, Roger Achkar. Reinforcement Learning for Radio Resource Management of Hybrid-Powered Cellular Networks. WiMob 2019 : Twelfth International Conference on Wireless and Mobile Computing, Networking and Communications, IEEE, Oct 2019, Barcelona, Spain. ⟨10.1109/WiMOB.2019.8923481⟩. ⟨hal-02294149⟩
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