Collaborative Research: NeTS: Medium: EdgeRIC: Empowering Real-time Intelligent Control and Optimization for NextG Cellular Radio Access Networks

合作研究：NeTS：媒介：EdgeRIC：为下一代蜂窝无线接入网络提供实时智能控制和优化

• 批准号: 2312979
• 负责人: Dinesh Bharadia
• 金额: $ 50万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312979&HistoricalAwards=false
• 关键词: Collaborative; Research; NeTS; Medium; EdgeRIC

NextG cellular networks must support a wide variety of emerging applications, such as augmented reality, autonomous vehicles and remote healthcare, which require radio access with latency, throughput and reliability guarantees hitherto unavailable. Simultaneously, the wireless environment is becoming increasingly dynamic over diverse spectrum bands, user mobility and variable traffic patterns. Complex cross layer interactions imply tractable models are unavailable, and a machine learning approach to optimal resource utilization is critical. This project first develops an open, simple and capable platform, entitled EdgeRIC that supports fine-grain decision making at multiple timescales across the cellular network stack, and second, develops a structured machine learning based approach over this platform that optimally utilizes all system resources to maximize diverse application performance. The project is enhanced by an education plan focusing on machine learning and wireless networking and coordinating workshops and tele-seminars for the research community and industry professionals to disseminate their ideas. Simultaneously, outreach in the form of summer camps and seminars for high school students focusing on machine learning enhances the impact of this project in STEM fields.The project aims at enabling intelligent decision making and control in cellular networks at realtime ( 1ms), while supporting training and adaptation at near-realtime (10ms - 1s) and non-realtime ( 1s). It brings together mathematical methods to develop and analyze reinforcement learning (RL) algorithms and systems development to integrate them into the cellular stack. The project addresses the key challenges of doing so via three main themes. The first focuses on realtime RL algorithms that schedule resources based on the relative priorities of applications, using the structure of the optimal policy to promote fast and scalable learning. The second theme focuses on robust and fast adaptation of these policies, which must operate over dynamic environments and application needs. The third theme addresses scalable learning to determine hierarchical policies operating across the network layers and sites. The themes all come together on a platform, entitled EdgeRIC for implementing multi-modal learning algorithms using the standardized OpenAIGym toolkit. The immediate impact of this project is in creating multi-timescale learning and control for the next generation of cellular networks. This project also advances the fundamental theory of meta and federated RL. The project supports seminars and summer camps for outreach, development of new courses focusing on machine learning for wireless communication, and coordination of workshops and tele-seminars for the research community and industry professionals to disseminate research ideas.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NextG蜂窝网络必须支持各种新兴应用程序，例如增强现实，自动驾驶汽车和远程医疗保健，这些应用需要具有延迟，吞吐量和可靠性的无线电访问，迄今无法获得延迟，吞吐量和可靠性保证。同时，无线环境在不同的频谱频段，用户移动性和可变流量模式上变得越来越动态。复杂的跨层交互意味着不可用的模型是不可用的，而机器学习方法的最佳资源利用方法至关重要。 该项目首先开发了一个开放，简单且功能强大的平台，标题为“ Edgeric”，该平台支持蜂窝网络堆栈中多个时间尺度上的精细颗粒决策制定，其次，在此平台上开发了一种基于结构化的机器学习方法，该方法可以最佳地利用所有系统资源来最大程度地提高多样化的应用程序性能。 一项教育计划的重点是机器学习和无线网络，并协调研究社区和行业专业人员的讲习班和电视节目，从而增强了该项目。同时，以夏令营和针对机器学习的高中生的夏季训练营和研讨会的形式进行宣传增强了该项目在STEM领域的影响。该项目旨在实时实时在蜂窝网络（1MS）中进行智能决策和控制，同时支持在接近现实的时间（10ms-1s-1s-1s）和非现实时间（1S）和非现实时间（1s）（1s）（1s）。 它汇集了数学方法来开发和分析加强学习（RL）算法和系统开发，以将它们集成到细胞堆栈中。 该项目通过三个主要主题解决了这样做的主要挑战。 第一个侧重于实时RL算法，该算法使用最佳策略的结构来基于应用程序的相对优先级来安排资源，以促进快速可扩展的学习。 第二个主题侧重于这些策略的强大和快速适应，这些策略必须在动态环境和应用需求上运行。 第三个主题介绍了可扩展的学习，以确定在网络层和站点上运行的层次结构策略。 这些主题都在平台上汇集在一起​​，标题为Edgeric，用于使用标准化的OpenAigym工具包实现多模式学习算法。 该项目的直接影响在于为下一代蜂窝网络创建多时间的学习和控制。 该项目还推进了元和联合RL的基本理论。该项目支持开展活动，开发新课程的研讨会和夏令营，专注于无线沟通的机器学习，并为研究社区和行业专业人员的研讨会和电视节目进行协调，以传播研究思想。该奖项反映了NSF的法规使命，并认为通过基金会的知识优点和广泛的crietia criter scriter scritia criter scritia criter criteria criter criter criteria criteria criter criteria crietia crietia criteria crietia cribitia均值得一提。