Years of study of the propagation channel showed a close relation between a location and the associated communication channel response. The use of a neural network to learn the location-to-channel mapping can therefore be envisioned. The Implicit Neural Representation (INR) literature showed that classical neural architecture are biased towards learning low-frequency content, making the location-to-channel mapping learning a non-trivial problem. Indeed, it is well known that this mapping is a function rapidly varying with the location, on the order of the wavelength. This paper leverages the model-based machine learning paradigm to derive a problem-specific neural architecture from a propagation channel model. The resulting architecture efficiently overcomes the spectral-bias issue. It only learns low-frequency sparse correction terms activating a dictionary of high-frequency components. The proposed architecture is evaluated against classical INR architectures on realistic synthetic data, showing much better accuracy. Its mapping learning performance is explained based on the approximated channel model, highlighting the explainability of the model-based machine learning paradigm.

翻译：多年来的传播信道研究表明，位置与相应的通信信道响应之间存在密切关联。因此，可以设想使用神经网络来学习位置到信道的映射关系。隐式神经表示（INR）相关文献指出，经典神经架构在学习低频内容时存在固有偏好，这使得位置-信道映射学习成为一个非平凡问题。事实上，众所周知，该映射是随位置快速变化的函数，其变化尺度在波长量级。本文基于模型驱动的机器学习范式，从传播信道模型出发推导出一种针对特定问题的神经架构。所提出的架构有效克服了频谱偏差问题，仅需学习低频稀疏校正项，从而激活一个由高频分量构成的字典。通过在现实合成数据上与经典INR架构进行对比评估，该架构展现出显著更优的精度。基于近似信道模型对映射学习性能进行了解释，凸显了模型驱动机器学习范式的可解释性优势。