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[2602.13207] A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy
Summary
This article presents a safety-constrained reinforcement learning framework aimed at enhancing the reliability of wireless autonomy, particularly in mission-critical applications.
Why It Matters
As AI and reinforcement learning become integral to wireless systems, ensuring safety in their deployment is crucial. This framework addresses the risks of unsafe behaviors in applications like UAVs and vehicular networks, proposing a proactive approach to safety that could significantly improve reliability in future 6G networks.
Key Takeaways
- Proposes a proactive safety-constrained RL framework integrating proof-carrying control.
- Demonstrates elimination of unsafe transmissions while maintaining system throughput.
- Achieves provable safety guarantees with minimal performance degradation.
- Highlights the importance of safety in mission-critical wireless applications.
- Sets the stage for trustworthy wireless autonomy in future 6G networks.
Computer Science > Networking and Internet Architecture arXiv:2602.13207 (cs) [Submitted on 12 Jan 2026] Title:A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy Authors:Abdikarim Mohamed Ibrahim, Rosdiadee Nordin View a PDF of the paper titled A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy, by Abdikarim Mohamed Ibrahim and Rosdiadee Nordin View PDF HTML (experimental) Abstract:Artificial intelligence (AI) and reinforcement learning (RL) have shown significant promise in wireless systems, enabling dynamic spectrum allocation, traffic management, and large-scale Internet of Things (IoT) coordination. However, their deployment in mission-critical applications introduces the risk of unsafe emergent behaviors, such as UAV collisions, denial-of-service events, or instability in vehicular networks. Existing safety mechanisms are predominantly reactive, relying on anomaly detection or fallback controllers that intervene only after unsafe actions occur, which cannot guarantee reliability in ultra-reliable low-latency communication (URLLC) settings. In this work, we propose a proactive safety-constrained RL framework that integrates proof-carrying control (PCC) with empowerment-budgeted (EB) enforcement. Each agent action is verified through lightweight mathematical certificates to ensure compliance with interference constraints, while empowerment budgets regulate the frequency of safety overrides to balance sa...
