Developments in Cognitive Radio Networks

Bodhaswar T. Maharaj (Sunil) is the current Dean of the Faculty of Engineering, Built Environment, and Information Technology at the University of Pretoria. He is also the SENTECH Chair in Broadband Wireless Multimedia Communications (BWMC) in the Department of Electrical, Electronic and Computer Engineering at the University of Pretoria. He holds a MEng in Electronic Engineering, an MSc in Operational Telecommunications, and a PhD in Electronic Engineering with specialisation in wireless communications. He is a Fellow of the South African Academy of Engineering (SAAE) and Deputy President of the South African Institute of Electrical Engineers (SAIEE). He is Senior Member of IEEE and South Africa Chapter Chair for IEEE Vehicular Technology Society (VTS). He has over 30 years; industry, consulting, research and academic experience and his research interests are in cognitive radio networks, MIMO systems, sensor wireless networks resource allocation and rural broadband communications systems. Dr Maharaj has over 140 peer reviewed journal articles and full conference papers in proceedings and 2 international patents. He has been the conference Chair for IEEE Wireless Africa 2019 conference and the General Conference Chair for the World Engineering Education (WEEF) and Global Engineering Deans Council (GEDC) 2020 Conference.

Introduction.- Part I Fundamentals on Cognitive Radio Networks.- Introduction to Cognitive Radio Networks.- Perspectives on Cognitive Radio Networks.- Spectrum Resource for Cognitive Radio Networks.- Part II Resources to Drive Cognitive Radio Networks.- Resource Optimization Problems in Cognitive Radio Networks.- Tools for Resource Optimization in Cognitive Radio Networks.- Modelling and Analyses of Resource Allocation Optimization in Cognitive Radio Networks.- Part III New Directions in the Development of Cognitive Radio Networks.- Queueing Systems in Resource Allocation Optimization for Cognitive Radio Networks.- Cooperative Diversity for Resource Optimization in Cognitive Radio Networks.- Interference Management and Control in Cognitive Radio Networks using Stochastic Geometry.- Deep Learning Opportunities for Resource Management in Cognitive Radio Networks.- The Role of Cognitive Radio Networks in Fifth-Generation.- Future Opportunities for Cognitive Radio Networks.- Conclusion.