Service Provisioning and Resource Management for Next-Generation Heterogeneous Wireless Networks

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Future wireless networks, e.g., 5G and beyond, are expected to feature heterogeneous network architecture, dense deployment of service access points, and diverse service requests with various Quality of Service (QoS) requirements. These characteristics of future wireless networks will be based upon advancements in network infrastructure, resources and control. At the infrastructure level, the integration of terrestrial and satellite communications is anticipated to provide ubiquitous service coverage over the globe. On the level of resources, the integration of communication, caching and computing resources, especially on the edge of the wireless network, is expected to alleviate pressure from explosive growth in network data traffic by bringing services closer to users. In regards to network control, network slicing and network function virtualization are emerging as promising resource management approaches to fulfill diversified service requirements. Nevertheless, many challenges toward achieving this vision of future wireless networks remain. First, adaptive and scalable network slicing and network function deployment become very challenging due to diversified and dynamic service requests in a practical edge network. Moreover, existing research does not offer adequate solutions for service delivery in an integrated terrestrial-satellite system that demands a complex decision making process that considers cost, user QoS, and network load balancing. Last but not the least, as user service demands can be uncertain and unknown to the network a priori, effective network resource management calls for the prediction of user requests and proactive joint allocation of communication, caching, and computing resources. Accordingly, our research program will investigate three research topics: cooperative service delivery in integrated terrestrial-satellite systems, scalable and adaptive network slicing in heterogeneous edge networks, and machine learning based service request and joint resource management. With powerful tools from artificial intelligence, mechanism design, and optimization, our proposed research will contribute to intelligent and reliable service provisioning and resource management in next-generation heterogeneous wireless networks.