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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716008
• 关键词: 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赖于软件定义的智能无线电接入网络（SIRAN）设备，这些设备可以随时重新配置，以满足移动的网络运营商（MNO）的多样化和不断变化的需求，为各种应用提供高质量的服务，从增强型移动的宽带通信到大规模机器类型通信，再到超可靠的低延迟通信。SIRAN能够提供通信和计算资源的“切片”，以保证服务质量（QoS）和用户体验质量（QoE）。此外，可扩展SIRAN实现了集成宏小区、微小区、皮科和/或毫微微小区的异构网络（HetNet）。SIRAN的这种前所未有的灵活性和可编程性连同网络服务需求、用户业务特性以及用户位置和移动性的可变性和动态性一起，通过优化包括频谱、信道带宽和传输时间调度、传输功率、计算和存储资源的网络资源的利用，对SIRAN的有效操作和管理提出了巨大的挑战。和能耗，同时满足服务和应用的QoS/QoE要求。 经典的建模和优化技术很难处理未来的多服务HetNet时，多个操作参数需要同时优化，而系统条件是动态变化的。我们的总体目标是通过开发实时管理SIRAN资源分配的技术来填补这一空白（例如，通信、高速缓存、计算）。我们将利用现代机器学习，特别是深度学习技术来优化资源利用率，同时满足所需的QoS/QoE。我们提出的技术和解决方案将使自主和智能的网络服务提供，利用SIRAN的可编程性。我们将开发无模型以及组合建模/无模型技术，由深度学习引擎驱动，以根据动态变化的网络和用户流量条件下的服务需求快速调整系统操作，使其适应所需的最佳操作区域。该项目中开发的技术将成为未来与移动网络运营商合作的行业合作项目的基础，以收集网络数据，使这些技术能够根据实际网络条件进行评估，并开发测试平台，以实验验证我们的工作和概念验证技术转让。 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如何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。