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

下一代无线网络将利用颠覆性的第五代（5G）技术，如无线电（sub-6GHz和毫米波）和光学（可见光和自由空间光学）频率传输、大规模天线阵列和超密集基站（BS）部署。此外，为了充分利用颠覆性5G技术的潜力，下一代网络将采用以用户为中心的网络（UCN）配置。统一网络网络将使用户能够有效地访问不同的网络资源，例如，通过支持用户端的多连接、虚拟化无线电资源和建立服务BSs的智能联盟。因此，为了支持各种各样的物联网（IoT）应用（例如，互联汽车、智慧城市、互联健康、智能可穿戴设备、智能电网等）以及移动宽带连接，现有的无线网络将从小规模、同质和集中式网络架构转变为大规模、异构和以用户为中心的网络。

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.