AI 中文总结
本文提出在3D数字孪生框架中结合Transformer与信道孪生泛化，实现低开销、高精度CSI推断，并通过迁移学习增强空间可扩展性，显著优于基线方法。
AI 中文摘要
准确的信道状态信息（CSI）对于先进的多天线无线网络至关重要。虽然配备高保真且特定于站点的射线追踪（RT）的无线数字孪生可以克服CSI获取的开销，然而，对于每个正交频分复用（OFDM）符号，从无线数字孪生计算确定性的、经过校准的基于RT的CSI违反了5G NR参数集所要求的严格微秒级延迟预算。为了克服这一计算瓶颈，本文在经校准的3D数字孪生框架内研究了三种方法，即（i）信道孪生的泛化，（ii）使用神经接收机的性能增强，以及（iii）用于可扩展性的数据驱动插值框架。我们对稀疏的时间锚点子集计算高精度CSI，并采用基于注意力的Transformer来预测剩余符号。与多项式样条或序列长短期记忆（LSTM）网络不同，Transformer利用全局感受野来捕获非线性多径动态，同时实现可并行化的实时推断。为了实现空间可扩展性，我们引入了信道孪生泛化。通过将3D RT模型与Transformer一起微调，该框架利用一个位置的CSI数据集来推断未见环境的信道行为。迁移学习进一步仅使用少量本地收集的数据将模型适应到新环境。仿真结果表明，所提出的架构显著优于基线插值器，实现了稳健的空间可迁移性，并通过统一的神经接收机降低了误码率。这些结果为下一代蜂窝网络中分布式信道孪生建立了可扩展、环境无关的基础，以实现高质量、低开销的CSI获取。
英文摘要
Accurate channel state information (CSI) is critical for advanced multi-antenna wireless networks. While high-fidelity and site-specific ray-tracing (RT) equipped wireless digital twins can overcome overhead for CSI acquisition. However, computing deterministic, calibrated RT based CSI from a wireless digital twin for every orthogonal frequency-division multiplexing (OFDM) symbol violates the strict microsecond latency budgets of the 5G NR numerology. To overcome this computational bottleneck, this paper investigates three approaches within a calibrated 3D digital twin framework, namely (i) the generalization of the channel twin, (ii) the performance enhancement using a neural receiver, and (iii) a data-driven interpolation framework for scalability. We compute high-precision CSI for a sparse subset of temporal anchors and employ an attention-based Transformer to predict the remaining symbols. Unlike polynomial splines or sequential long short-term memory (LSTM) networks, the Transformer exploits a global receptive field to capture the non-linear multipath dynamics while enabling parallelizable, real-time inference. To achieve spatial scalability, we introduce channel twin generalization. By fine-tuning the 3D RT models alongside the Transformer, the framework leverages the CSI dataset of one location to infer the channel behavior of an unseen environment. Transfer learning further adapts the model to a new environment using only a small fraction of locally collected data. Simulation results demonstrate that the proposed architecture substantially outperforms the baseline interpolators, achieves robust spatial transferability, and lowers the bit error rate through the unified neural receiver. These results establish a scalable, environment-agnostic foundation of distributed channel twins for high-quality, low-overhead CSI acquisition in next-generation cellular networks.
