Jamming-Resilient PRB Reservation for Latency-Critical O-RAN Network Slicing

2026-05-28

AI Summary

This work addresses the severe degradation of physical resource block (PRB) capacity in O-RAN network slicing under adversarial interference, which often leads to ultra-reliable low-latency communication (URLLC) latency violations. To mitigate this issue, the authors propose an elasticity-aware resource allocation framework based on reserved PRBs. The framework deploys an xApp on the near-real-time RAN Intelligent Controller (RIC), integrating a hybrid mechanism that proactively clears queues before interference onset to reserve latency margin and dynamically allocates reserved resources during interference events. Innovatively, it combines reservation-based strategies with reinforcement learning by introducing a Masked Deep Q-Network (Masked DQN) to intelligently and adaptively optimize PRB reservation decisions in non-stationary interference environments. Simulation results demonstrate that the proposed approach significantly reduces URLLC latency violation rates and improves the utilization efficiency of reserved resources compared to passive baseline methods.

Abstract

Open radio access network (O-RAN) architectures enable near real-time, software-driven control of network slicing through programmable xApps deployed on the near-real-time RAN Intelligent Controller (near-RT RIC). In industrial 5G downlink systems, adversarial jamming can abruptly reduce the effective physical resource block (PRB) capacity, triggering queue buildup and persistent latency violations, particularly in the presence of low spectral efficiency cell edge user equipments. This paper proposes a reserve-based resilience framework for PRB allocation in sliced O-RAN deployments. A finite pool of reserved PRBs is controlled by a near-RT RIC xApp that provides hybrid mitigation by proactively clearing backlog to build latency margin and reactively allocating reserve capacity during jammer active intervals. We formulate reserve activation as a constrained sequential decision problem and design a masked Deep Q-Network to learn effective control policies under non-stationary jamming. Simulation results show substantial reductions in URLLC latency violations and improved reserve efficiency compared to reactive baselines.

Problem

• O-RAN
• jamming resilience
• PRB allocation
• latency violation
• network slicing

Innovation

• O-RAN
• PRB reservation
• jamming resilience
• Deep Q-Network
• network slicing