A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy

TL;DR

This paper introduces a proactive safety-constrained reinforcement learning framework for wireless systems, combining proof-carrying control and empowerment budgets to ensure safety without sacrificing performance in mission-critical applications.

Contribution

It proposes a novel safety-constrained RL framework that integrates mathematical safety certificates and empowerment regulation, providing provable safety guarantees in wireless autonomy.

Findings

Eliminates unsafe transmissions in simulations.

Maintains system throughput comparable to unconstrained methods.

Provides provable safety guarantees with minimal performance loss.

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 safety and autonomy. We implement this framework on a wireless uplink scheduling task using Proximal Policy Optimization (PPO). Simulation results demonstrate that the proposed PCC+EB controller eliminates unsafe transmissions while preserving system throughput and predictable autonomy. Compared with unconstrained and reactive baselines, our method achieves provable safety guarantees with minimal performance degradation. These results highlight the potential of proactive safety constrained RL to enable trustworthy wireless autonomy in future 6G networks.