Nwachukwu, SEChepkoech, MLysko, Albert AAwodele, KMwangama, JBurger, Chris R2023-07-202023-07-202022-08Nwachukwu, 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/10204/12899 .978-1-6654-9739-8978-1-6654-9740-4DOI: 10.1109/RTSI55261.2022.9905123http://hdl.handle.net/10204/12899The 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.FulltextenMachine learning algorithms5G mobile communicationWireless networksPrecodingMassive MIMOMachine learningEnergy efficiencyConference PresentationNwachukwu, 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/10204/12899Nwachukwu, 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/10204/12899Nwachukwu 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/10204/12899 .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/10204/12899 ER -37009