Machine learning enabled multi-radio access technology selection in 5G networks

Nurudeen Salau; Muhammad Shakir

Machine learning enabled multi-radio access technology selection in 5G networks

School of Computing, Engineering and Physical Sciences

Research output: Contribution to conference › Poster › peer-review

Abstract

A study carried out to assist pedestrian or travelling mobile user among the estimated 5.33 Billion subscribers in the world (2/3 of world population) in deciding the appropriate Network between 4G and 5G based on user's bandwidth requirement, geographical location and mobility for efficient QoS on the part of providers and effective QoE on the part of users using machine learning approach.

Original language	English
Publication status	Published - 28 Jun 2022
Event	SICSA Conference 2022: Sustainability, Recovery and Resilience - Glasgow Caledonian University , Glasgow, United Kingdom Duration: 28 Jun 2022 → 29 Jun 2022 https://sicsaconf.org/ (Conference website.)

Conference

Conference	SICSA Conference 2022
Country/Territory	United Kingdom
City	Glasgow
Period	28/06/22 → 29/06/22
Internet address	https://sicsaconf.org/ (Conference website.)

Cite this

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Y2 - 28 June 2022 through 29 June 2022

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Machine learning enabled multi-radio access technology selection in 5G networks

Abstract

Conference

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SICSA Best poster award 2022

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Machine learning enabled multi-radio access technology selection in 5G networks

Abstract

Conference

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Prizes

SICSA Best poster award 2022

Cite this