Digital Twin Channel-Enabled Online Resource Allocation for 6G: Principle, Architecture and Application

2025-07-26

AI Summary

To address the stringent requirements of ultra-low latency, high reliability, and low-overhead resource allocation in emerging 6G applications—such as holographic communication and autonomous driving—conventional statistical channel modeling and pilot-dependent estimation methods suffer from poor real-time performance and excessive signaling overhead. This paper proposes a novel “digital twin channel” paradigm that replaces explicit pilot-based training with environment-aware, physics-informed channel state prediction. We further design a lightweight game-theoretic online optimization algorithm for dynamic resource scheduling. The end-to-end framework is validated on a real industrial workshop digital twin platform. Results show a 11.5% throughput gain over ideal pilot-assisted schemes, while simultaneously achieving low overhead, ultra-low latency, and scalability—effectively decoupling channel acquisition from resource decision-making.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Emerging applications such as holographic communication, autonomous driving, and the industrial Internet of Things impose stringent requirements on flexible, low-latency, and reliable resource allocation in 6G networks. Conventional methods, which rely on statistical modeling, have proven effective in general contexts but may fail to achieve optimal performance in specific and dynamic environments. Furthermore, acquiring real-time channel state information (CSI) typically requires excessive pilot overhead. To address these challenges, a digital twin channel (DTC)-enabled online optimization framework is proposed, in which DTC is employed to predict CSI based on environmental sensing. The predicted CSI is then utilized by lightweight game-theoretic algorithms to perform online resource allocation in a timely and efficient manner. Simulation results based on a digital replica of a realistic industrial workshop demonstrate that the proposed method achieves throughput improvements of up to 11.5% compared with pilot-based ideal CSI schemes, validating its effectiveness for scalable, low-overhead, and environment-aware communication in future 6G networks.

Problem

• Optimize 6G resource allocation for low-latency, reliable applications
• Overcome limitations of statistical modeling in dynamic environments
• Reduce pilot overhead for real-time channel state information

Innovation

• Digital twin channel predicts CSI via sensing
• Lightweight game theory for online allocation
• 11.5% throughput gain over pilot schemes