Digital Twin Channel-Enabled Online Resource Allocation for 6G: Principle, Architecture and Application
This addresses resource allocation for emerging applications like holographic communication and autonomous driving in 6G networks, representing an incremental advance with a novel method for a known bottleneck.
The paper tackles the challenge of flexible, low-latency resource allocation in 6G networks by proposing a digital twin channel-enabled online optimization framework, achieving up to 11.5% throughput improvement compared to pilot-based ideal CSI schemes.
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.