EdgeRIC: Delivering Real-time Intelligence to Radio Access Networks

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  • EdgeRIC is a platform for real-time AI-in-the-loop for decision and control in cellular networks. It is designed to access network and application-level information to execute AI-optimized and other policies in real-time (sub-millisecond) .

Our Projects

Our Demos

BeamArmor

Anti-jamming: Controlling MIMO weights in realtime to steer the beam null toward the jammer

AI Scheduling

RL-based scheduling policy trained to maximize overall system throughput

Multi-site Management

Interference-aware resource distribution across sites with Near-RT RIC

Our Repositories

5G Testbed

5G Testbed

Meet the Team

Dinesh Bharadia

Professor, UCSD

Srinivas Shakkottai

Professor, TAMU

Ish Jain

Professor, RPI

Ushasi Ghosh

PhD Student, UCSD

Sushila Seshasayee

PhD Student, UCSD

Ali Mamaghani

PhD Student, UCSD

Qingyuan Zhang

MS Student, UCSD

Woo Hyun Ko

Senior Research Engineer, TAMU

Funding

This work was funded primarily by NSF Grants CNS 2312978, CNS 2312979 and in part by CNS 1955696, ECCS 2030245, ARO grant W911NF- 19-1-0367.

Dataset