WiMax - a critical view of the technology and its economics

University of the Witwatersrand Faculty of Engineering and the Built Environment School of Information and Electrical EngineeringMobile Broadband is now more of a necessity than a luxury, especially amongst the younger generation, irrespective of where they live. Mobile WiMax and LTE, the latest and fastest Mobile Broadband technologies, mark significant improvements over 3G networks because they use IP (Internet Protocol) end-to-end. To end-users, this means faster network speeds, better quality services, and increased coverage area. To the Network Operators, this means simplified network architectures, efficient use of resources, and improved security. In this report, the different issues and challenges related to deploying Mobile WiMax (802.16e or 802.16m) in rural South Africa, were identifed and explored. In this project, Atoll, SONAR, and Touch Point analysis tools were used to determine which Mobile Broadband technology is economically and technically suited for rural South Africa. It was found that LTE yields superior performance results than WiMax, which in turn yields superior performance results to all other existing 3G technologies. However it will take time for LTE to reach rural areas therefore WiMax can be considered as a solution to extend Broadband services to rural South Africa and thus assist in bridging the digital divide. Recommendations on how best to deploy Mobile WiMax are made based on observations made from the experimental work.MT201

Towards a multi-agent reinforcement learning approach for joint sensing and sharing in cognitive radio networks

The adoption of the Fifth Generation (5G) and beyond 5G networks is driving the demand for learning approaches that enable users to co-exist harmoniously in a multi-user distributed environment. Although resource-constrained, the Cognitive Radio (CR) has been identified as a key enabler of distributed 5G and beyond networks due to its cognitive abilities and ability to access idle spectrum opportunistically. Reinforcement learning is well suited to meet the demand for learning in 5G and beyond 5G networks because it does not require the learning agent to have prior information about the environment in which it operates. Intuitively, CRs should be enabled to implement reinforcement learning to efficiently gain opportunistic access to spectrum and co-exist with each other. However, the application of reinforcement learning is straightforward in a single-agent environment and complex and resource intensive in a multi-agent and multi-objective learning environment. In this paper, (1) we present a brief history and overview of reinforcement learning and its limitations; (2) we provide a review of recent multi-agent learning methods proposed and multi-agent learning algorithms applied in Cognitive Radio (CR) networks; and (3) we further present a novel framework for multi-CR reinforcement learning and conclude with a synopsis of future research directions and recommendations