Resource allocation for Edge Computing in Next Generation Wireless Networks

无线网络在满足近期和未来应用的性能要求方面面临着前所未有的挑战。尽管通过管理稀缺的通信资源来提高无线通信能力的研究正在进行，但目前的无线网络基础设施在处理用户日益增长的期望和需求方面遇到了困难。幸运的是，与基本上有限的频谱和电力资源不同，无线网络可以利用越来越丰富、低成本和可扩展的计算和内存资源。因此，在网络边缘集成缓存和计算能力成为无线网络可持续发展的重要关键之一。事实上，这种集成可以改善许多系统性能指标，例如频谱和能源效率、端到端延迟、运营和资本支出以及用户体验质量。该研究计划的总体目标是通过提出无线网络边缘可用的通信、缓存和计算资源分配的创新方法来优化无线网络性能。更具体地说，将在几种新兴无线网络架构的背景下提出和评估新的资源分配方案，考虑到通信、缓存和计算交互所决定的新约束、机遇和挑战。具体来说，我们将考虑两种新兴技术，即毫米波和车联网通信。为此，将使用组合优化和连续优化、博弈论和机器学习理论等理论工具来制定和解决集中和分布式的资源分配问题。在设计低复杂度的启发式和近似算法之前，将首先开发最优方案和基准方案。然后将使用分析和模拟工具评估拟议方案的性能。网络边缘的缓存和计算能力的集成为许多网络参与者带来了各种好处。例如，网络提供商从回程拥塞的大幅减少中受益，而移动设备用户则看到他们的体验质量得到提高，电池寿命得到延长。因此，提出的研究有望通过提供理论和模拟结果以及特别适应的算法解决方案的设计，更好地理解缓存和边缘计算集成所带来的好处。预期的研究结果将引起加拿大各种无线网络行业参与者的兴趣。该研究计划将培养几名本科生和研究生，以满足加拿大工业界对新兴无线通信和网络领域高素质人才的需求。

Wireless networks are facing unprecedented challenges in fulfilling the performance requirements of recent and future applications. Despite ongoing research efforts in enhancing wireless communication capabilities through the management of scarce communication resources, current wireless networking infrastructures encounter difficulties to deal with the users' increasing expectations and needs. Fortunately, unlike the fundamentally limited spectrum and power resources, wireless networks can make use of computing and memory resources that are more and more abundant, low-cost and scalable. Therefore, the integration of caching and computing capabilities at the network edge becomes one of the important keys for wireless networks' sustainability. In fact, this integration results in improving numerous system performance metrics, such as spectral and energy efficiency, end-to-end latency, operating and capital expenditures and users' quality of experience. The general objective of this research program is to optimize wireless network performance by proposing innovative approaches for the allocation of communication, caching and computing resources available at the wireless network edge. More specifically, novel resource allocation schemes will be proposed and evaluated in the context of several emerging wireless networking architectures, taking into account the new constraints, opportunities and challenges dictated by the interaction of communications, caching and computing. Specifically, we will consider two emerging technologies, namely mmWave and vehicle-to-everything communications. To this end, theoretical tools such as those from combinatorial and continuous optimization, game theory and machine learning theory will be used to formulate and solve centralized and distributed resource allocation problems. Optimal and benchmark schemes will be first developed before designing low complexity heuristic and approximation algorithms. The performance of the proposed schemes will be then assessed using both analytical and simulation tools. The integration of caching and computing capabilities at the network edge brings various benefits for many network actors. For instance, network providers benefit from a considerable reduction in the backhaul congestion, whereas mobile device users see their quality of experience enhanced and their battery life prolonged. Therefore, the proposed research is expected to give a better understanding of the benefits resulting from the integration of caching and computing at the edge by providing theoretical and simulation results and the design of especially adapted algorithmic solutions. The anticipated research results will be of interest for various Canadian wireless networks industry actors. This research program will prepare several undergraduate and graduate students to meet the needs of the Canadian industry for highly qualified personnel in emerging areas of wireless communications and networks.