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（低于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。 UCN将使用户能够有效地访问各种网络资源，例如，通过在用户端支持多连接、虚拟化无线资源、以及进行服务BS的智能联盟。因此，为了支持各种各样的物联网（IoT）应用（例如，互联车辆、智能城市、互联健康、智能可穿戴设备、智能电网等）沿着移动的宽带连接，现有的无线网络将从小规模、同构和集中的网络架构转变为大规模、异构和以用户为中心的网络。 该研究计划的总体目标是开发新的数学模型，可以表征和优化具有大规模天线阵列和共存无线电和光无线部署（CROWD）的UCN的性能，为特定应用（如联网车辆和物联网网络）定制开发的性能模型，并开发支持机器学习的无线电资源管理（RRM）算法。 所开发的性能模型将是新颖的，因为它们捕获了无线节点的多连接性、光和无线电频率的不同信道传播、精确的定向天线模型以及无线节点的移动性对整体网络性能的影响。该研究计划的另一个新奇是CROWD中大规模多输入多输出（MIMO）的共址和无小区配置的综合性能分析。此外，除了传统的性能指标，如用户的覆盖或网络遍历能力，拟议的研究计划将集中在表征性能指标，如传输延迟，可靠性，回程感知吞吐量，和移动性感知覆盖。此外，将为UCN设计支持机器学习的RRM算法，以在延迟和可靠性约束的情况下进行BS、频谱和功率分配的有效联盟。 该研究计划将使网络运营商能够事先了解新网络架构的性能，了解重要网络参数和性能权衡的相互作用，在集中控制器中实施开发的算法，并最终观察新技术对其服务和业务发展的影响。

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.