Digital twinInternational
Latency optimization for 6G V2I digital twins
A framework builds a wireless V2I digital twin for 6G, exchanging model parameters via compressed personalized federated learning and steering post-reconstruction traffic with LLM-guided multi-agent reinforcement learning. The twin jointly predicts path loss and quality-of-experience KPIs, targeting end-to-end latency reduction in the closed control loop.
Why it matters
Demonstrates how coupling model learning with traffic control keeps latency bounded in a networked digital twin's closed loop.
Relevant to DT products
- Original headline
- LLM-Guided Dual-Stage Latency Optimization for Closed-Loop 6G V2I Digital Twins
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