New O-RAN framework uses AI to combat jamming for low-latency networks

This paper introduces a new framework for managing radio resource allocation in Open RAN environments, specifically addressing the challenge of adversarial jamming that can disrupt latency-critical network slices. The proposed solution utilizes a near-real-time RIC xApp to proactively manage backlog and reactively allocate reserved physical resource blocks (PRBs) during jamming events. A masked Deep Q-Network was developed to learn optimal control policies, demonstrating significant reductions in latency violations and improved reserve efficiency compared to existing methods. AI

IMPACT Introduces an AI-driven approach to enhance the resilience of 5G network slicing against jamming, potentially improving reliability for industrial applications.

RANK_REASON Academic paper detailing a novel technical approach. [lever_c_demoted from research: ic=1 ai=0.7]

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• Deep Q-Network
• near-RT RIC

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