Massive, Heterogeneous, and User Centric Wireless Networks: Modeling and Optimization

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The next-generation wireless networks will leverage disruptive fifth-generation (5G) technologies such as transmission over  radio (sub-6GHz and millimeter waves) and optical (visible light and free-space optics) frequencies, massive antenna arrays, and ultra-dense base station (BS) deployments. Furthermore, to fully exploit the potential of disruptive 5G technologies, the next generation networks will have user-centric network (UCN) configuration.  UCNs will enable users to access diverse network resources efficiently, e.g., through supporting multi-connectivity at users' end, virtualizing radio resources, and making smart coalitions of serving BSs. Consequently, to support a diverse range of Internet-of-Things (IoT) applications (e.g., connected vehicles, smart cities, connected health, smart wearables, smart grid,  etc.) along with the mobile broadband connections, the existing wireless networks will be morphing from small-scale, homogeneous, and centralized network architectures to massive, heterogeneous, and user-centric networks.   The overarching objectives of this research program are, to develop novel mathematical models that can characterize and optimize the performance of UCNs  enabled with massive antenna arrays and Coexisting Radio and Optical Wireless Deployment (CROWD), to customize the developed performance models for specific applications such as connected vehicles and IoT networks, and to develop machine learning enabled radio resource management (RRM) algorithms. The developed performance models will be novel as they capture the impact of  multi-connectivity of wireless nodes, diverse channel propagation of optical and radio frequencies, accurate directional antenna models, and mobility of wireless nodes, on the overall networks' performance. Another novelty of this research program is the comprehensive performance analysis of co-located and cell-free configurations of massive multiple-input-multiple-output (MIMO) in CROWD. Also, in addition to conventional performance metrics such as users' coverage or network ergodic capacity, the proposed research program will focus on characterizing performance metrics such as transmission delay, reliability, backhaul-aware throughput, and mobility-aware coverage. Furthermore, machine learning enabled RRM algorithms will be designed for UCNs to make  efficient coalitions of BSs, spectrum and power allocations, with latency and reliability constraints. This research program will allow network operators to understand the performance of new network architectures beforehand, obtain insights on the interplay of important network parameters and performance trade-offs, implement the developed algorithms in a centralized controller, and ultimately observe the impact of new technologies on the evolution of their services and business.