Deep Reinforcement Learning-Based Carrier Tuning Algorithm for Mobile Communication Networks

With the evolution of mobile communication networks towards 5G and beyond to 6G, managing network resources presents unprecedented challenges, particularly in scenarios demanding high data rates, low latency, and extensive connectivity. Traditional resource allocation methods struggle with network dynamics and complexity, including user mobility, varying network loads, and diverse Quality of Service (QoS) requirements. Deep Reinforcement Learning (DRL), an emerging AI technique, demonstrates significant potential due to its adaptive and learning capabilities. This paper integrates user mobility and network load prediction into a DRL framework and proposes a novel reward function to enhance resource utilization efficiency while meeting real-time QoS demands. We establish a system model involving base stations and receiving terminals to simulate communication services within coverage areas. Comparative experiments analyze the performance of the DRL approach versus traditional methods across metrics such as throughput, delay, and spectral efficiency. Results indicate DRL's superiority in handling dynamic environments and fulfilling QoS needs, especially under heavy loads. This study introduces innovative approaches and tools for future mobile network resource management, paving the way for practical DRL implementations and enhancing overall network performance.

@article{Zhang2024,
  title     = {Deep Reinforcement Learning-Based Carrier Tuning Algorithm for Mobile Communication Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Weimin Zhang and Xinying Zhao},
  doi       = {10.14569/IJACSA.2024.0150937},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150937}
}