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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 sin wireless communication network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to helpnon-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) like modulation and scheduling of energy allocation We first categorize these works into: radio analysis, energy analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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How to Cite
R.Kalaivani, & J.Gayathri. (2023). Efficient Modulation and Coding Scheme Selection Using Machine Learning. International Journal of Intellectual Advancements and Research in Engineering Computations, 11(1), 11–16. Retrieved from