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Power allocation based on reinforcement learning in 5G/B5G multi-cell networks
- 1 School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, P.R.China
* Author to whom correspondence should be addressed.
Abstract
The fifth generation of wireless communication technology (5G), has revolutionized the digital landscape with its ultra-fast speeds, massive connectivity, and reduced latency; Beyond 5G (B5G), represents the evolutionary steps towards sixth-generation networks which aims to build upon 5G’s capabilities by integrating advanced technologies like ultra-dense network, edge computing, and enhanced spectral efficiency. This paper investigates the application of two distinct exponential reward functions in reinforcement learning algorithms for power allocation in ultra-dense networked base stations. The primary objective is to maximize the overall network capacity and spectral efficiency. The performance of the proposed reinforcement learning algorithms is compared with the traditional water-filling algorithm, as well as against the other to highlight the differences in learning outcomes resulting from the choice of reward functions. The results show that the exponential function model with reciprocal exponent is superior to the previous two in spectral efficiency and convergence speed and provide valuable insights into the effectiveness of using reinforcement learning for complex resource allocation problems in modern communication networks.
Keywords
Reinforcement learning, ultra-dense networks, reward functions, power allocation
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Cite this article
Ren,K. (2025). Power allocation based on reinforcement learning in 5G/B5G multi-cell networks. Theoretical and Natural Science,95,53-62.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Applied Physics and Mathematical Modeling
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