Smart Communication Networks with Machine Learning

具有机器学习功能的智能通信网络

• 批准号: RGPIN-2019-04070
• 负责人: Shen, Xuemin(Sherman)
• 金额: $ 8.01万
• 依托单位: University of Waterloo
• 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=751655
• 关键词: Smart; Communication; Networks; Machine; Learning

In evolving towards the fifth generation (5G) and beyond, the communication networks have been experiencing a fundamental change in the way they are designed and operated. The shift is mainly due to the ever-increasing service demands, new use cases, and diverse applications, which render the traditional one-size-fits-all approach ineffective and inefficient. Software defined networking (SDN) and network function virtualization (NFV) are two emerging technologies that can be leveraged for a smart, agile, and cost-effective networking solution. Due to the complicate operation of modern communication networks, machine intelligence potentially provides a new avenue to deal with data traffic dynamics and to maximize network performance. This DG program will investigate several fundamental issues to develop innovative engineering solutions for smart and agile communication networks, taking advantage of state-of-the-art machine intelligence tools. First, we will study how to determine the network topology for a virtual network. The topology establishment requires joint routing and service function placement, which is a multi-objective optimization problem and entails high complexity. We will develop a dynamic network topology solution based on traffic load prediction. Second, we will investigate how to efficiently allocate multi-dimensional (computing, transmission, and storage) resources for desired service isolation among multiple virtual networks sharing the common physical substrate; and develop a resource management framework for maximal resource multiplexing while ensuring service quality. Lastly, we will study networking protocol customization for reliable end-to-end data transport. The required protocol depends on network and service functions associated with each service request. As a result, the protocol should be customized and created on demand. We will develop a protocol customization and automation solution. HQP training is an essential component of this DG application. Five PhD students and three postdoctoral fellows will participate in the proposed research. The research program will provide the trainees with valuable knowledge and research experience in communication networks and machine learning, and enable them to emerge as expert additions to the Canadian high technology industry labor force. The research outcomes are expected to have a profound scientific, technological, and social impact, in providing in-depth understanding and new insights for developing innovative cost-effective intelligent algorithms and protocols for future communication networks, to support various new use cases and information services. The race towards developing networking technologies based on machine intelligence will undoubtedly be undertaken by research groups worldwide. Invention of successful new techniques on the subject will foster a vital competitive edge to Canadian telecommunications industry in the international market place.

在向第五代（5G）及以后演进的过程中，通信网络的设计和操作方式发生了根本性的变化。这种转变主要是由于不断增长的服务需求，新的用例和多样化的应用程序，这使得传统的一刀切的方法无效和低效。软件定义网络（SDN）和网络功能虚拟化（NFV）是两种新兴技术，可用于智能、敏捷和经济高效的网络解决方案。由于现代通信网络的复杂操作，机器智能可能提供了一种新的途径来处理数据流量动态和最大限度地提高网络性能。该DG计划将研究几个基本问题，为智能和敏捷的通信网络开发创新的工程解决方案，利用最先进的机器智能工具。 首先，我们将研究如何确定虚拟网络的网络拓扑。拓扑建立需要联合路由和业务功能布局，这是一个多目标优化问题，需要很高的复杂性。我们将开发一个基于流量负载预测的动态网络拓扑解决方案。 其次，我们将研究如何有效地分配多维（计算，传输和存储）资源共享共同的物理基板之间的多个虚拟网络所需的服务隔离，并开发一个资源管理框架，最大的资源复用，同时确保服务质量。最后，我们将研究用于可靠的端到端数据传输的网络协议定制。所需的协议取决于与每个服务请求相关联的网络和服务功能。因此，应根据需要定制和创建协议。我们将开发协议定制和自动化解决方案。HQP培训是此DG应用程序的重要组成部分。五名博士生和三名博士后研究员将参加拟议的研究。该研究计划将为学员提供通信网络和机器学习方面的宝贵知识和研究经验，使他们能够成为加拿大高科技产业劳动力的专家补充。该研究成果将产生深远的科学、技术和社会影响，为未来通信网络开发创新的高性价比智能算法和协议提供深入的理解和新的见解，以支持各种新的用例和信息服务。毫无疑问，开发基于机器智能的网络技术的竞赛将由全球研究小组承担。在这方面发明成功的新技术将促进加拿大电信业在国际市场上的重要竞争优势。