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

大规模、异构和以用户为中心的无线网络：建模和优化

• 批准号: RGPIN-2019-06357
• 负责人: Tabassum, Hina
• 金额: $ 2.4万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758076
• 关键词: Massive; Heterogeneous; User; Centric; Wireless

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.

下一代无线网络将利用颠覆性的第五代 (5G) 技术，例如无线电传输（6GHz 以下和毫米波）和光学（可见光和自由空间光学）频率、大规模天线阵列和超密集天线此外，为了充分发挥颠覆性 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，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重连接、虚拟化无线电资源以及测试服务基站的智能联盟，以支持各种物联网 (IoT) 应用（例如联网车辆、智慧城市、互联健康、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、集中的网络架构向大规模、异构、集中的网络架构转变。该研究计划的总体目标是开发新颖的数学模型，以表征和优化采用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，并定制开发的性能模型。针对联网车辆和物联网网络等特定应用，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，因为它们捕捉了无线、多样化通道的多重连接的影响。该研究项目的另一个新颖之处是对大规模多输入的共置和无小区配置的综合性能分析。 - CROWD 中的多输出 (MIMO) 此外，除了用户覆盖范围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关注传输延迟、可靠性、回程感知等性能指标的特征。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下建立有效的 BS 分配、频谱和功率分配联盟。事先了解新的网络架构，深入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演进的影响。