Modern communication systems rely on accurate channel estimation to achieve efficient and reliable transmission of information. As the communication channel response is highly related to the user's location, one can use a neural network to map the user's spatial coordinates to the channel coefficients. However, these latter are rapidly varying as a function of the location, on the order of the wavelength. Classical neural architectures being biased towards learning low frequency functions (spectral bias), such mapping is therefore notably difficult to learn. In order to overcome this limitation, this paper presents a frugal, model-based network that separates the low frequency from the high frequency components of the target mapping function. This yields an hypernetwork architecture where the neural network only learns low frequency sparse coefficients in a dictionary of high frequency components. Simulation results show that the proposed neural network outperforms standard approaches on realistic synthetic data.

翻译：现代通信系统依赖精确的信道估计以实现高效可靠的信息传输。由于通信信道响应与用户位置高度相关，可利用神经网络将用户空间坐标映射至信道系数。然而，信道系数随位置变化剧烈（变化尺度可达波长量级）。经典神经网络结构存在偏向学习低频函数的特性（频谱偏差），导致此类映射学习极为困难。为突破这一限制，本文提出一种节俭的模型驱动网络，将目标映射函数的低频与高频分量分离。该超网络架构中，神经网络仅需学习高频分量字典中的低频稀疏系数。仿真结果表明，在真实合成数据上，所提网络性能显著优于传统方法。