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

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

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.