Abstract
In dense femtocell network, the complexity of the resource allocation increases significantly as the network becomes denser, which limits the performance of the network. The usage of reinforcement learning to solve the resource allocation problem showed promising results compared to conventional methods. In this work, we use global Q-learning approach on the macro base station to solve the resource allocation problem in a dense and complex network. We propose a new reward function that can be implemented on a centralized Q-learning and achieve good results in terms of maintaining the quality of service for the macro user and maximizing the sum capacity of the femtocell users. In comparison to other reward functions, the proposed reward function maintained both the QoS for the macro user and fairness among all femtocell users.
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Alenezi, A.M., Hamdi, K. (2019). Global Q-Learning Approach for Power Allocation in Femtocell Networks. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_24
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DOI: https://doi.org/10.1007/978-3-030-33607-3_24
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