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，例如通过无线电(低于6 GHz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高密度基站(BS)部署。此外，为了充分挖掘颠覆性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。新的UCN将使用户能够高效地访问不同的网络资源，例如通过支持用户端的多连接、无线资源虚拟化和智能服务BSS联盟。因此，要支持各种物联网(IoT)应用(例如，互联车辆、智能城市、互联医疗、智能可穿戴设备、智能电网等)随着移动宽带连接的发展，现有的无线网络将从小规模、同构、集中的网络结构演变为大规模、异质、以用户为中心的网络。这项研究计划的首要目标是，开发能够表征和优化大规模天线阵列和共存的无线电和光无线部署(GUMP)支持的UCN性能的新型数学模型，定制针对互联车辆和物联网网络等特定应用开发的性能模型，以及开发支持机器学习的无线电资源管理(RRM)算法。开发的性能模型将是新颖的，因为它们捕捉了无线节点的多连接、光和无线电频率的不同信道传播、准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。该研究项目的另一个新奇之处是对群体中大规模多输入多输出(MIMO)的共址和无小区配置进行了全面的性能分析。此外，除了用户覆盖或网络遍历容量等传统性能指标外，拟议的研究计划还将重点描述传输延迟、可靠性、回程感知吞吐量和移动性感知覆盖等性能指标。此外，支持机器学习的RRM算法将为UCN设计，以在具有延迟和可靠性限制的情况下，更有效地联合BSS、频谱和功率分配。这一研究计划将使网络运营商能够预先了解新网络架构的性能，深入了解重要网络参数和性能权衡的相互作用，在中央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.