Fully harvesting the gain of multiple-input and multiple-output (MIMO) requires accurate channel information. However, conventional channel acquisition methods mainly rely on pilot training signals, resulting in significant training overheads (time, energy, spectrum). Digital twin-aided communications have been proposed in [1] to reduce or eliminate this overhead by approximating the real world with a digital replica. However, how to implement a digital twin-aided communication system brings new challenges. In particular, how to model the 3D environment and the associated EM properties, as well as how to update the environment dynamics in a coherent manner. To address these challenges, motivated by the latest advancements in computer vision, 3D reconstruction and neural radiance field, we propose an end-to-end deep learning framework for future generation wireless systems that can reconstruct the 3D EM field covered by a wireless access point, based on widely available crowd-sourced world-locked wireless samples between the access point and the devices. This visionary framework is grounded in classical EM theory and employs deep learning models to learn the EM properties and interaction behaviors of the objects in the environment. Simulation results demonstrate that the proposed learnable digital twin can implicitly learn the EM properties of the objects, accurately predict wireless channels, and generalize to changes in the environment, highlighting the prospect of this novel direction for future generation wireless platforms.

翻译：充分挖掘多输入多输出（MIMO）系统的性能增益需要精确的信道信息。然而，传统的信道获取方法主要依赖导频训练信号，导致巨大的训练开销（时间、能量、频谱）。文献[1]提出了数字孪生辅助通信，通过数字副本近似真实世界来减少或消除这种开销。然而，如何实现数字孪生辅助通信系统带来了新的挑战。具体而言，如何对三维环境及其相关电磁特性进行建模，以及如何以连贯的方式更新环境动态。为应对这些挑战，受计算机视觉、三维重建和神经辐射场最新进展的启发，我们提出了一种面向未来无线系统的端到端深度学习框架。该框架能够基于接入点与设备之间广泛可用的、世界锁定的众包无线样本，重构无线接入点覆盖范围内的三维电磁场。这一前瞻性框架以经典电磁理论为基础，采用深度学习模型学习环境中物体的电磁特性与交互行为。仿真结果表明，所提出的可学习数字孪生能够隐式学习物体的电磁特性，准确预测无线信道，并能泛化至环境变化，凸显了该新颖方向在未来无线平台中的应用前景。