Machine-learning-based wireless network resource allocation strategy

Yuheng Liu

doi:10.1117/12.3033288

29 July 2024 Machine-learning-based wireless network resource allocation strategy

Yuheng Liu

Proceedings Volume 13214, Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024); 132140Y (2024) https://doi.org/10.1117/12.3033288
Event: Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024), 2024, Guangzhou, China

Abstract

This paper introduces HetGNN, a novel machine learning framework for wireless network resource allocation, blending graph neural networks with long short-term memory networks to adeptly manage graph-structured and temporal data. An adaptive resource allocation algorithm, rooted in Actor-Critic reinforcement learning, dynamically refines strategies, ensuring efficient interaction between agents and their environment. Comparative analyses with benchmark algorithms on a simulation platform highlight HetGNN's superiority in enhancing spectrum and energy efficiency, alongside user experience. The study paves the way for advanced applications in 5G/6G networks, emphasizing the integration of federated and transfer learning for future intelligent wireless network developments.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuheng Liu "Machine-learning-based wireless network resource allocation strategy", Proc. SPIE 13214, Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024), 132140Y (29 July 2024); https://doi.org/10.1117/12.3033288

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