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

未来的无线网络，例如5G及更高版本，预计将具有不同的网络体系结构、密集的服务接入点部署和具有不同服务质量(Qos)要求的多样化服务请求。未来无线网络的这些特征将基于网络基础设施、资源和控制的进步。在基础设施方面，地面和卫星通信的融合预计将在全球范围内提供无处不在的服务覆盖。在资源层面，通信、缓存和计算资源的整合，特别是在无线网络边缘，有望通过使服务更接近用户来缓解网络数据流量爆炸性增长带来的压力。在网络控制方面，网络切片和网络功能虚拟化是满足多样化服务需求的有前途的资源管理方法。 然而，实现这一未来无线网络愿景的许多挑战依然存在。首先，在一个实际的边缘网络中，由于多样化和动态的服务需求，自适应和可扩展的网络切片和网络功能部署变得非常具有挑战性。此外，现有的研究没有为在综合地面卫星系统中提供足够的服务提供解决方案，该系统需要考虑成本、用户服务质量和网络负载平衡的复杂决策过程。最后但并非最不重要的一点是，由于用户服务需求可能是不确定的和网络先验未知的，有效的网络资源管理需要对用户请求进行预测，并主动联合分配通信、缓存和计算资源。 因此，我们的研究计划将研究三个主题：地面卫星综合系统中的协同服务交付、异质边缘网络中可扩展和自适应的网络切片以及基于机器学习的服务请求和联合资源管理。借助人工智能、机制设计和优化等方面的强大工具，我们提出的研究将有助于下一代异质无线网络中智能可靠的服务提供和资源管理。

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.