ResearchSpace

Integration of massive MIMO and machine learning in the present and future of power consumption in wireless networks: A review

Show simple item record

dc.contributor.author Nwachukwu, SE
dc.contributor.author Chepkoech, M
dc.contributor.author Lysko, Albert A
dc.contributor.author Awodele, K
dc.contributor.author Mwangama, J
dc.contributor.author Burger, Chris R
dc.date.accessioned 2023-07-04T12:47:03Z
dc.date.available 2023-07-04T12:47:03Z
dc.date.issued 2022-08
dc.identifier.citation Nwachukwu, S., Chepkoech, M., Lysko, A.A., Awodele, K., Mwangama, J. & Burger, C.R. 2022. Integration of massive MIMO and machine learning in the present and future of power consumption in wireless networks: A review. http://hdl.handle.net/10204/12874 . en_ZA
dc.identifier.isbn 978-1-6654-9739-8
dc.identifier.isbn 978-1-6654-9740-4
dc.identifier.uri DOI: 10.1109/RTSI55261.2022.9905123
dc.identifier.uri http://hdl.handle.net/10204/12874
dc.description.abstract The steady increase in data traffic rates and systems’ complexity have contributed to the information and communication technologies (ICT) sector’s increased energy consumption and CO 2 emissions. These pose a significant challenge to the telecommunication industry and the environment. This challenge has necessitated considering energy efficiency as a critical design pillar in 5G and future wireless networks. As a result, current research efforts for future wireless networks focus on minimising energy usage and improving efficiency. This work investigates several energy optimisation techniques in the present and future wireless networks, their contributions, advantages, and limitations. Based on the review of different techniques, we discuss the architecture of the massive MIMO (mMIMO) technique, including its operation and requirements. We also present the performance evaluation of mMIMO using different precoding algorithms, which is crucial for energy efficiency in future wireless networks. We further review incorporating intelligence using a Machine Learning (ML) approach in switching off underused mMIMO arrays to minimise energy usage. Finally, we discuss several critical open research issues in mMIMO and ML that make future research and implementation possible in next-generation wireless networks. en_US
dc.format Fulltext en_US
dc.language.iso en en_US
dc.relation.uri https://ieeexplore.ieee.org/document/9905123 en_US
dc.source 2022 IEEE 7th Forum on Research and Technologies for Society and Industry Innovation (RTSI), Paris, France, 24-26 August 2022 en_US
dc.subject Machine learning algorithms en_US
dc.subject 5G mobile communication en_US
dc.subject Wireless networks en_US
dc.subject Precoding en_US
dc.subject Massive MIMO en_US
dc.subject Machine learning en_US
dc.subject Energy efficiency en_US
dc.title Integration of massive MIMO and machine learning in the present and future of power consumption in wireless networks: A review en_US
dc.type Conference Presentation en_US
dc.description.pages 7 en_US
dc.description.note Preprint vetsion of the paper presented at the 2022 IEEE 7th Forum on Research and Technologies for Society and Industry Innovation (RTSI), Paris, France, 24-26 August 2022. en_US
dc.description.cluster Next Generation Enterprises & Institutions en_US
dc.description.impactarea Spectrum Access Mgmt Innov en_US
dc.identifier.apacitation Nwachukwu, S., Chepkoech, M., Lysko, A. A., Awodele, K., Mwangama, J., & Burger, C. R. (2022). Integration of massive MIMO and machine learning in the present and future of power consumption in wireless networks: A review. http://hdl.handle.net/10204/12874 en_ZA
dc.identifier.chicagocitation Nwachukwu, SE, M Chepkoech, Albert A Lysko, K Awodele, J Mwangama, and Christiaan R Burger. "Integration of massive MIMO and machine learning in the present and future of power consumption in wireless networks: A review." <i>2022 IEEE 7th Forum on Research and Technologies for Society and Industry Innovation (RTSI), Paris, France, 24-26 August 2022</i> (2022): http://hdl.handle.net/10204/12874 en_ZA
dc.identifier.vancouvercitation Nwachukwu S, Chepkoech M, Lysko AA, Awodele K, Mwangama J, Burger CR, Integration of massive MIMO and machine learning in the present and future of power consumption in wireless networks: A review; 2022. http://hdl.handle.net/10204/12874 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Nwachukwu, SE AU - Chepkoech, M AU - Lysko, Albert A AU - Awodele, K AU - Mwangama, J AU - Burger, Christiaan R AB - The steady increase in data traffic rates and systems’ complexity have contributed to the information and communication technologies (ICT) sector’s increased energy consumption and CO 2 emissions. These pose a significant challenge to the telecommunication industry and the environment. This challenge has necessitated considering energy efficiency as a critical design pillar in 5G and future wireless networks. As a result, current research efforts for future wireless networks focus on minimising energy usage and improving efficiency. This work investigates several energy optimisation techniques in the present and future wireless networks, their contributions, advantages, and limitations. Based on the review of different techniques, we discuss the architecture of the massive MIMO (mMIMO) technique, including its operation and requirements. We also present the performance evaluation of mMIMO using different precoding algorithms, which is crucial for energy efficiency in future wireless networks. We further review incorporating intelligence using a Machine Learning (ML) approach in switching off underused mMIMO arrays to minimise energy usage. Finally, we discuss several critical open research issues in mMIMO and ML that make future research and implementation possible in next-generation wireless networks. DA - 2022-08 DB - ResearchSpace DP - CSIR J1 - 2022 IEEE 7th Forum on Research and Technologies for Society and Industry Innovation (RTSI), Paris, France, 24-26 August 2022 KW - Machine learning algorithms KW - 5G mobile communication KW - Wireless networks KW - Precoding KW - Massive MIMO KW - Machine learning KW - Energy efficiency LK - https://researchspace.csir.co.za PY - 2022 SM - 978-1-6654-9739-8 SM - 978-1-6654-9740-4 T1 - Integration of massive MIMO and machine learning in the present and future of power consumption in wireless networks: A review TI - Integration of massive MIMO and machine learning in the present and future of power consumption in wireless networks: A review UR - http://hdl.handle.net/10204/12874 ER - en_ZA
dc.identifier.worklist 26287 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record