Abstract
This paper considers the power control problem in device-to-device (D2D) communication underlaying a cellular network and explores the application of the machine learning (ML) approach in power control for improving the system throughput. Two multi-agent reinforcement learning (MARL) based algorithms are proposed for performing power control of D2D users (DUs): centralized Q-learning algorithm and distributed Q-learning algorithm. In the centralized algorithm, all DU pairs sharing the same RB use a common Q table in the learning process, while in the distributed algorithm each DU pair maintains its own Q table. Simulation results show that both the centralized algorithm and the distributed algorithm can converge to the same optimum Q values, and the distributed algorithm can converge faster than the centralized algorithm. Moreover, both the proposed Q-learning algorithms outperform the random power control algorithm in terms of the system throughput and satisfaction ratio.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Chen, W., Zheng, J. (2019). A Multi-agent Reinforcement Learning Based Power Control Algorithm for D2D Communication Underlaying Cellular Networks. In: Han, S., Ye, L., Meng, W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-22971-9_7
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DOI: https://doi.org/10.1007/978-3-030-22971-9_7
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