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，如增强现实、自动驾驶汽车和远程医疗保健，这些应用要求无线电接入具有迄今为止无法提供的延迟、吞吐量和可靠性保证。同时，无线环境在不同的频谱带、用户移动性和可变的业务模式上变得越来越动态。复杂的跨层交互意味着无法使用易于处理的模型，因此机器学习方法来优化资源利用至关重要。 该项目首先开发了一个开放，简单和功能强大的平台，名为EdgeRIC，支持在蜂窝网络堆栈中的多个时间尺度上进行细粒度决策，其次，在这个平台上开发了一种基于结构化机器学习的方法，最佳地利用所有系统资源，以最大限度地提高各种应用程序的性能。 该项目通过一项教育计划得到加强，该计划侧重于机器学习和无线网络，并协调研究界和行业专业人员的讲习班和远程研讨会，以传播他们的想法。与此同时，以夏令营和研讨会的形式面向高中生开展的以机器学习为重点的推广活动，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（1 ms）的智能决策和控制，同时支持近实时（10 ms-1 s）和非实时（1 s）的培训和适应。 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，以将它们集成到细胞堆栈中。 该项目通过三个主题解决这样做的关键挑战。 第一个重点是实时RL算法，根据应用程序的相对优先级调度资源，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健壮和快速适应，这些策略必须在动态环境和应用程序需求中运行。 第三个主题涉及可扩展学习，以确定跨网络层和站点运行的分层策略。 这些主题都集中在一个名为EdgeRIC的平台上，用于使用标准化的OpenAIGym工具包实现多模态学习算法。 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Meta和联邦强化学习的基础理论。该项目支持研讨会和夏令营的推广，开发以无线通信机器学习为重点的新课程，并为研究界和行业专业人士协调研讨会和远程研讨会，以传播研究理念。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。