Power allocation based on reinforcement learning in 5G/B5G multi-cell networks

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 5Gs 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.