Machine Learning and Artificial Intelligence for Future Generation Communication Networks

下一代通信网络的机器学习和人工智能

• 批准号: RGPIN-2020-04926
• 负责人: Farsad, Nariman
• 金额: $ 2.11万
• 依托单位: Ryerson University
• 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=749815
• 关键词: Machine; Learning; Artificial; Intelligence; Future

With the implementation of the fifth generation of mobile systems (5G) and the emergence of internet-of-things (IoT), the number of devices connected to the Internet is expected to grow tremendously over the next few decades. At the same time, machine learning (ML) and artificial intelligence (AI) are becoming an integral part of many applications that run on mobile devices. This raises two interesting questions. First, with the complexity of the modern-day communication networks, how can ML and AI be leveraged to improve the design of these systems? And second, how can communication networks help improve distributed ML and AI? This research program develops cutting edge technologies for IoT using ML and AI in three key areas: a) how ML and AI can help improve localization and authentication at the physical layer; b) how ML and AI can be used to detect chemical sources in complex networks of vessels; and c) how deep neural networks can be distributed and trained over IoT devices, edge nodes, and cloud servers in the present of communication links that introduce errors and delay. The ML- and AI-based technologies arising from this proposed research are expected to significantly improve the speed, performance and dynamic adaptability of wireless networks, thereby enabling enhanced communications among IoT devices and infrastructure within multiple sectors. This will have a profound impact in smart cities, smart health monitoring, and other areas where distributed sensing, localization, authentication, and deep learning are required (e.g. manufacturing, robotics and autonomous vehicles).

随着第五代移动系统（5G）的实施和物联网（IoT）的出现，连接到互联网的设备数量预计将在未来几十年大幅增长。与此同时，机器学习（ML）和人工智能（AI）正在成为许多运行在移动设备上的应用程序的组成部分。这就提出了两个有趣的问题。首先，随着现代通信网络的复杂性，如何利用ML和AI来改进这些系统的设计？第二，通信网络如何帮助改进分布式机器学习和人工智能？该研究计划在三个关键领域开发使用ML和AI的物联网前沿技术：a) ML和AI如何帮助改善物理层的本地化和身份验证；b)如何使用ML和AI来检测复杂容器网络中的化学来源；c)在目前引入错误和延迟的通信链路中，如何在物联网设备、边缘节点和云服务器上分布和训练深度神经网络。本研究提出的基于机器学习和人工智能的技术有望显著提高无线网络的速度、性能和动态适应性，从而增强多个部门内物联网设备和基础设施之间的通信。这将对智慧城市、智能健康监测以及其他需要分布式传感、定位、身份验证和深度学习的领域（例如制造业、机器人和自动驾驶汽车）产生深远影响。