  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.
