Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks
For 6G network operators, it addresses the practical problem of telemetry latency in centralized RAN control loops, enabling stable ISAC operation under realistic delays.
This paper tackles telemetry latency in 6G ISAC networks, proposing a Digital Twin-assisted belief-state RL framework that improves median throughput by 12% and reduces sensing error by 7% at 50 ms latency, while retaining 88% of zero-latency throughput at 100 ms.
Integrated Sensing and Communication (ISAC) enables joint data transmission and environmental perception for sixth-generation (6G) networks, but centralized and virtualized RAN control loops introduce telemetry latency that yields stale observations and unstable control. This paper proposes a Digital Twin-assisted belief-state reinforcement learning framework for latency-robust ISAC. A Digital Twin (DT) reconstructs a synchronized belief state from delayed telemetry using an Extended Kalman Filter, and a Proximal Policy Optimization agent performs joint beamforming and power allocation for communication and sensing. Closed-loop simulations with telemetry delays up to 100 ms demonstrate consistent performance gains over latency-unaware deep reinforcement learning (DRL) and heuristic baselines. At 50 ms latency, the proposed method improves median throughput by 12% and reduces sensing error by 7% relative to a DT-only controller, while achieving an order-of-magnitude reduction in reliability violations. Even at 100 ms latency, the proposed approach retains approximately 88% of its zero-latency throughput. These results show that Digital Twin-assisted belief-state control enables stable and efficient ISAC operation under realistic telemetry delays in 6G networks.