用数字孪生预测信道，实现6G低时延高效资源分配
Digital Twin Channel-Enabled Online Resource Allocation for 6G: Principle, Architecture and Application
- 基于环境感知的数字孪生信道预测信道状态
- 相比理想导频方案，吞吐量提升最高达11.5%
- 适合工业物联网等动态场景的实时资源调度
全息通信、自动驾驶和工业物联网等新兴应用对6G网络的资源分配提出了灵活、低时延和高可靠的要求。传统依赖统计建模的方法在一般场景下有效，但在特定动态环境中难以达到最优性能。此外，获取实时信道状态信息（CSI）通常需要大量导频开销。为此，本文提出一种基于数字孪生信道（DTC）的在线优化框架，利用DTC根据环境感知预测CSI，再由轻量级博弈算法实时高效地完成资源分配。基于真实工业车间数字孪生的仿真结果表明，该方法相比理想导频CSI方案，吞吐量最高提升11.5%，验证了其在可扩展、低开销、环境感知的未来6G通信中的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。