可学习的无线数字孪生：基于神经表示的电磁场重建
Learnable Wireless Digital Twins: Reconstructing Electromagnetic Field with Neural Representations
AI总结：
针对MIMO信道获取开销大及数字孪生建模难的问题，提出基于神经辐射场的端到端深度学习框架，利用众包无线样本重建3D电磁场，能隐式学习电磁特性并准确预测信道。
摘要：
要充分获取多输入多输出（MIMO）的增益，需要准确的信道信息。然而，传统的信道获取方法主要依赖导频训练信号，导致显著的训练开销（时间、能量、频谱）。文献[1]提出了数字孪生辅助通信，通过用数字副本逼近真实世界来减少或消除这一开销。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3D环境及其相关的电磁（EM）特性进行建模，以及如何以连贯的方式更新环境动态。为了应对这些挑战，受计算机视觉、3D重建和神经辐射场最新进展的启发，我们为下一代无线系统提出了一种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众包世界锁定无线样本，重建无线接入点覆盖的3D EM场。这一愿景框架以经典EM理论为基础，采用深度学习模型来学习环境中物体的EM特性和交互行为。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EM特性，准确预测无线信道，并对环境变化具有泛化能力，凸显了这一面向下一代无线平台的新方向的前景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
