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，例如增强现实、自动驾驶汽车和远程医疗保健，这些应用需要具有迄今为止无法保证的延迟、吞吐量和可靠性的无线电接入。同时，无线环境在不同的频段、用户移动性和可变的流量模式上变得越来越动态。复杂的跨层交互意味着难以处理的模型不可用，而实现最佳资源利用的机器学习方法至关重要。 该项目首先开发一个开放、简单且功能强大的平台，名为 EdgeRIC，支持跨蜂窝网络堆栈的多个时间尺度的细粒度决策，其次，在该平台上开发基于结构化机器学习的方法，该方法可以最佳地利用所有系统资源最大限度地提高多样化的应用程序性能。 该项目通过一项以机器学习和无线网络为重点的教育计划得到加强，并为研究界和行业专业人士协调研讨会和远程研讨会以传播他们的想法。同时，针对关注机器学习的高中生以夏令营和研讨会的形式进行推广，增强了该项目在 STEM 领域的影响力。该项目旨在实现蜂窝网络中实时（1ms）的智能决策和控制，同时支持近实时（10ms - 1s）和非实时（1s）的训练和适应。 它汇集了数学方法来开发和分析强化学习 (RL) 算法和系统开发，以将它们集成到蜂窝堆栈中。 该项目通过三个主题解决了这样做的关键挑战。 第一个重点关注实时强化学习算法，该算法根据应用程序的相对优先级来调度资源，使用最优策略的结构来促进快速和可扩展的学习。 第二个主题侧重于这些策略的稳健和快速适应，这些策略必须在动态环境和应用程序需求下运行。 第三个主题涉及可扩展的学习，以确定跨网络层和站点运行的分层策略。 这些主题全部集中在一个名为 EdgeRIC 的平台上，用于使用标准化的 OpenAIGym 工具包实施多模式学习算法。 该项目的直接影响是为下一代蜂窝网络创建多时间尺度的学习和控制。 该项目还推进了元和联邦强化学习的基础理论。该项目支持举办外展研讨会和夏令营、开发专注于无线通信机器学习的新课程，以及为研究界和行业专业人士协调研讨会和远程研讨会以传播研究思想。该奖项反映了 NSF 的法定使命，并具有通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。