PROMPT: Learning dynamic resource allocation policies for network applications

Penney, Drew; Li, Bin; Sydir, Jaroslaw; Chen, Lizhong; Tai, Charlie; Lee, Stefan; Walsh, Eoin; Long, Thomas G.

doi:10.1016/j.future.2023.03.016

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Potential Applications Of Machine Learning In Wireless Network1

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2023

2024

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Cited by 2 publications

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“…Machine learning (ML) technologies offer significant benefits for wireless networks, enhancing self-learning, adaptability, and generalization. ML outperforms traditional models in tasks like channel and user behavior prediction, with deep learning surpassing Kalman filters in accuracy [4]. Initiatives like Ericsson's traffic prediction system leverage ML for more precise base station traffic forecasts, achieving errors below 10%.…”

Section: Potential Applications Of Machine Learning In Wireless Networkmentioning

confidence: 99%

Machine-learning-based wireless network resource allocation strategy

Liu

2024

Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024)

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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.

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Section: Potential Applications Of Machine Learning In Wireless Networkmentioning

confidence: 99%