Machine Learning-Aided Solutions for Efficient Planning, Design, Operation and Adaptation of Beyond 5G Wireless Networks

用于高效规划、设计、运营和适应超 5G 无线网络的机器学习辅助解决方案

• 批准号: RGPIN-2022-03798
• 负责人: Woungang, Isaac
• 金额: $ 3.35万
• 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=749952
• 关键词: Machine; Learning; Aided; Solutions; Efficient

With the rapid growth in the number of smart devices, service types/demands and network complexity with the increased inflow of raw data, leveraging intelligence into future wireless networks to make them more intelligent and automotive has become a crucial need. The upcoming 5G and Beyond (B5G) wireless networks are expected to create a foundation for intelligent and dynamic networks with some isolated Artificial Intelligence (AI) operations, however, full-fledged intelligence and automation with the capabilities of self-configuration, self-optimization and self-healing operations will require significant amount of further research, and seems feasible only in B5G wireless networks, with the incorporation of emerging AI/Machine Learning (ML) techniques. The long-term objective of the proposed research program is to develop AI/ML-assisted resource-efficient, reliable, secure and cost-efficient low-complexity communication technologies/protocols towards enabling full-fledged intelligent and autonomous B5G networks. To accomplish this long-term objective, the short-term objectives are: (1) Develop AI)/ML-assisted solutions for efficient planning, design, operation and adaptation of wireless networks including IoT and aerial networks, while improving various performance metrics of future networks such as energy efficiency, operational efficiency, end-to-end latency, connection density, coverage, security and privacy; (2) Develop ML-assisted solutions for Radio Intelligent Controllers (RICs) of Open Radio Access Network (O-RAN) in order to enhance the radio resource management efficiency and reduce the overall computational complexity, and (3) Develop secure-aware resource management schemes for the 5G edge-cloud infrastructure. The outcomes of the proposed research will significantly benefit the scientific communities in addressing various underlying technical issues in B5G wireless networks, and society in addressing several societal and economic challenges by supporting the ever-increasing massive number of heterogeneous devices and sophisticated applications. Besides, deploying the investigated solutions for RICs in future O-RAN architecture will help Canadian Telecom operators to gain significant benefits in terms of deployment flexibility, agility, resource utilization efficiency and reduction of operating cost. On the other hand, the security of the 5G cloudified infrastructure is vital to ensure the success of the digital economy in Canada. In this sense, the proposed AI-empowered security-aware resource managers will contribute in realizing a safe 5G operation since it will foster the inclusion of security by design strategies when it comes to the design and development of new resource allocation mechanisms for 5G networks, which traditionally emphasize performance metrics such as blocking and dropping probabilities for the offloaded tasks while neglecting the importance of risk awareness to decrease the likelihood of attacks.

随着智能设备数量、服务类型/需求和网络复杂性的快速增长以及原始数据的增加，将智能应用于未来的无线网络，使其更加智能化和自动化已成为一项关键需求。即将到来的5G及以后（B5 G）无线网络预计将为具有一些孤立的人工智能（AI）操作的智能和动态网络奠定基础，然而，具有自配置、自优化和自愈操作能力的全面智能和自动化将需要大量的进一步研究，并且似乎仅在B5 G无线网络中可行。结合新兴的AI/机器学习（ML）技术。拟议研究计划的长期目标是开发AI/ML辅助的资源高效，可靠，安全和具有成本效益的低复杂度通信技术/协议，以实现全面的智能和自主B5 G网络。为了实现这一长期目标，短期目标是：（1）开发AI/ML辅助的解决方案，以有效规划、设计、运营和适应包括物联网和空中网络在内的无线网络，同时提高未来网络的各种性能指标，如能源效率、运营效率、端到端延迟、连接密度、覆盖范围、安全性和隐私性;（2）为开放无线接入网络（O-RAN）的无线智能控制器（RIC）开发ML辅助解决方案，以提高无线资源管理效率并降低整体计算复杂度，以及（3）为5G边缘云基础设施开发安全感知资源管理方案。拟议研究的成果将大大有利于科学界解决B5 G无线网络中的各种潜在技术问题，并通过支持不断增加的大量异构设备和复杂应用来解决社会和经济挑战。此外，在未来的O-RAN架构中部署所研究的RIC解决方案将有助于加拿大电信运营商在部署灵活性、敏捷性、资源利用效率和降低运营成本方面获得显着的好处。另一方面，5G云化基础设施的安全性对于确保加拿大数字经济的成功至关重要。从这个意义上说，拟议的人工智能授权的安全感知资源管理器将有助于实现安全的5G操作，因为它将促进在设计和开发5G网络的新资源分配机制时通过设计策略纳入安全性。传统上强调性能指标，如卸载任务的阻塞和丢弃概率，而忽略了风险意识的重要性，降低攻击的可能性。