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- W3127786530 endingPage "318" @default.
- W3127786530 startingPage "318" @default.
- W3127786530 abstract "This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed." @default.
- W3127786530 created "2021-02-15" @default.
- W3127786530 creator A5022887237 @default.
- W3127786530 creator A5026442244 @default.
- W3127786530 creator A5050639868 @default.
- W3127786530 creator A5077730491 @default.
- W3127786530 date "2021-01-29" @default.
- W3127786530 modified "2023-10-11" @default.
- W3127786530 title "A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer" @default.
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