Abstract: To meet the exponentially increasing requirements of bandwidth, throughput, latency and jitter, cellular technologies have experienced a progressive evolution from the 1st generation (1G) to the 5th generation (5G). However, with the incorporation of new hardware to support additional applications and devices, the energy consumption of mobile networks has experienced a proportional rise from one generation to the next. A significant demarcation from the conventional trend in energy consumption is expected to be introduced by 5G which already consumes four times more energy than 4G. Moreover, the amount of user data is predicted to be four times more in 2025 than the current data volume on current mobile networks, as per a Mobility Report by Ericsson. Consequently, energy efficiency is a major concern in 5G in contrast to earlier generations. In parallel, the conceptualization of 6G has already begun with the prospect of connecting everything, providing ubiquitous sensor integration, communication, computation and control, as well as transmission over mmWave and THz bands. Such a network evolution will lead to further densification of cells as it will require massive deployment of tiny cells that will overlay on the
existing macro cellular networks. 6G will therefore exert an unprecedented pressure on energy efficiency and sustainability due to its high network and technical complexity. In order to address the energy efficiency issues in 5G and future 6G networks, several machine learning techniques can be employed. For example, in 5G, machine learning techniques can be employed to optimize the processes at the core
network, access network and edge network, hence improving the overall energy efficiency. In 6G, AI based techniques can effectively improve energy efficiency by applying them to the three service classes being proposed for 6G, namely, Cellular Network Communications (CNC), Machine Type Communications (MTC), and Computation Oriented Communications(COC). In this presentation, a review of the most
important AI and machine learning techniques, that can be applied to enhance energy efficiency in 5G and future 6G networks, will be performed.

Biography: Dr. T.P. Fowdur received his BEng (Hons) degree in Electronic and Communication Engineering with first class honours from the University of Mauritius in 2004. He was also the recipient of a Gold medal for having produced the best degree project at the Faculty of Engineering in 2004. In 2005 he obtained a full-time PhD scholarship from the Tertiary Education Commission of Mauritius and was awarded his PhD degree in Electrical and Electronic Engineering in 2010 by the University of Mauritius. He is also a Registered Chartered Engineer of the Engineering Council of the UK, member of the Institute of Telecommunications Professionals of the UK and the IEEE. He joined the University of Mauritius as an academic in June 2009 and is presently an Associate Professor at the Department of Electrical and Electronic Engineering of the University of Mauritius. His research interests include Mobile and Wireless Communications, Multimedia Communications, Networking and Security, Telecommunications Applications Development, Internet of Things and AI. He has published several papers in these areas and is actively involved in research supervision, reviewing of papers and also organizing international conferences.