Candidate: Conghao Zhou
Title: Data-Driven Network Management for Next-Generation Wireless Networks
Date: August 10, 2022
Time: 10:00 AM
Place: REMOTE
Supervisor(s): Shen, Sherman
Abstract:
With the commercialization and maturity of the fifth-generation (5G) wireless networks, the next-generation wireless network (NGWN) is envisioned to provide seamless connectivity for mobile user terminals (MUTs) and to support a wide range of new applications with stringent quality of service (QoS) requirements. In the NGWN, the network architecture will be highly heterogeneous due to the integration of terrestrial networks, satellite networks, and aerial networks formed by unmanned aerial vehicles (UAVs), and the network environment becomes highly dynamic because of the mobility of MUTs and the spatiotemporal variation of service demands. In order to provide high-quality services in such dynamic and heterogeneous networks, flexible, fine-grained, and adaptive network management will be essential. Recent advancements in deep learning (DL) and digital twins.
(DTs) have made it possible to enable data-driven solutions to support network management in the NGWN. DL methods can solve network management problems by leveraging data instead of explicit mathematical models, and DTs can facilitate DL methods by providing extensive data based on the full digital representations created for individual MUTs. Data-driven solutions that integrates DL and DT can address complicated network management problems and explore implicit network characteristics to adapt to dynamic network environments in the NGWN. However, the design of data-driven network management solutions in the NGWN meets several technical challenges: 1) how the NGWN can be configured to support multiple services with different spatiotemporal service demands while simultaneously satisfying their different QoS requirements; 2) how the multi-dimensional network resources are proactively reserved to support MUTs with different mobility patterns in a resource-efficient manner; and 3) how the heterogeneous NGWN components, including base stations (BSs), satellites, and UAVs, jointly coordinate their network resources to support dynamic service demands, etc.