Next Generation Software-defined Intelligent Radio Access Network (SIRAN) - Leveraging Deep Learning for Autonomous and Intelligent Service Provisioning

下一代软件定义智能无线接入网络 (SIRAN) - 利用深度学习实现自主和智能服务提供

• 批准号: RGPIN-2019-06348
• 负责人: Leung, Victor
• 金额: $ 6.63万
• 依托单位: University of British Columbia
• 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=752168
• 关键词: Next; Generation; Software; defined; Intelligent; Next; Generation; Software; defined; Intelligent

Next generation 5G and beyond wireless networks will rely on software-defined intelligent radio access network (SIRAN) equipment that can be readily reconfigured to satisfy the diverse and changing needs of mobile network operators (MNOs) to provide high-quality services for a wide range of applications, from enhanced mobile broadband communications, to massive machine-type communications, to ultra-reliable low-latency communications. SIRAN enables provisioning of "slices" of communication and computing resources to guarantee quality of service (QoS) and users' quality of experience (QoE). Furthermore, scalable SIRANs enables heterogeneous networks (HetNets) that integrate macro-, micro-, pico- and/or femto-cells. Such unprecedented flexibility and programmability of SIRANs together with the variability and dynamicity of the network service demands, user traffic characteristics, and user location and mobility present great challenges to the efficient operation and management of SIRANs by optimizing the utilization of network resources including frequency spectrum, channel bandwidth and transmission time schedule, transmission power, computation and storage resources, and energy consumption, while satisfying the QoS/QoE requirements of services and applications. Classical modeling and optimization techniques have difficulty dealing with future multi-service HetNets when multiple operational parameters need to be simultaneously optimized while system conditions are dynamically changing. Our overall objective is to fill this gap by developing techniques to manage in real-time the allocation of SIRAN resources (e.g., communication, caching, computing). We will leverage contemporary machine learning, particularly deep learning techniques to optimize resource utilization while satisfying the required QoS/QoE. Our proposed techniques and solutions will enable autonomous and intelligent network service provisioning that takes advantage of the programmability of SIRANs. We shall develop both model-free as well as combined modeling/model-free techniques, driven by deep-learning engines to quickly adapt system operation towards the desired optimal operation region based on service requirements under dynamically varying network and user traffic conditions. The techniques developed in this project will form the basis of future industry-partnership projects in collaboration with MNOs to collect network data that enables these techniques to be evaluated based on practical network conditions, and to develop testbeds for experimental verification of our work and proof-of-concept technology transfer. This project will provide an excellent opportunity to train the next generation wireless networking engineers and researchers who are knowledgeable on the use of contemporary machine intelligence techniques to address the complexity and dynamic nature of the next generation wireless networks.

下一代5G及以后的无线网络将依靠软件定义的智能无线电访问网络（SIRAN）设备，可以轻松地重新配置以满足移动网络运营商（MNOS）的多样化和不断变化的需求，以为广泛的应用程序提供高质量的服务，从增强的移动宽带通信，从增强的机器机器通信到大型机器型通信，到超级超级级别的低位级别，到超级级别的通信。 Siran可以提供通信和计算资源的“切片”，以保证服务质量（QoS）和用户的经验质量（QOE）。此外，可扩展的Sirans启用了整合宏观，微型，Pico-和/或femto细胞的异质网络（HETNET）。 Such unprecedented flexibility and programmability of SIRANs together with the variability and dynamicity of the network service demands, user traffic characteristics, and user location and mobility present great challenges to the efficient operation and management of SIRANs by optimizing the utilization of network resources including frequency spectrum, channel bandwidth and transmission time schedule, transmission power, computation and storage resources, and energy consumption, while satisfying the QoS/QoE requirements of services and applications.当需要在系统条件动态变化时需要同时优化多个操作参数时，经典的建模和优化技术很难处理未来的多功能HETNET。我们的总体目标是通过开发实时管理SIRAN资源（例如通信，缓存，计算）的技术来填补这一空白。我们将利用当代的机器学习，尤其是深度学习技术来优化资源利用，同时满足所需的QoS/QoE。我们提出的技术和解决方案将实现自主和智能网络服务提供，利用Sirans的可编程性。我们将根据深入学习引擎的驱动，并根据动态变化的网络和用户流量条件，基于服务需求，将无模型和合并的模型/无模型/无模型技术转化为所需的最佳操作区域。该项目开发的技术将与MNO合作构成未来行业合作伙伴项目的基础，以收集能够根据实际网络条件进行评估这些技术的网络数据，并开发测试台以实验我们的工作和概念概念证明技术转移。该项目将为培训下一代无线网络工程师和研究人员提供一个绝佳的机会，这些工程师和研究人员了解使用当代机器智能技术来解决下一代无线网络的复杂性和动态性质。