Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911630107869184 |
|---|---|
| author | Tiwari, Himanshu Kar, Binayak Tiwari, Priyanshu |
| author_facet | Tiwari, Himanshu Kar, Binayak Tiwari, Priyanshu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25967 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Tiwari, Himanshu Kar, Binayak Tiwari, Priyanshu Networking and Internet Architecture 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. |
| title | Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2604.25967 |