Model-based learning for location-to-channel mapping
This work addresses a domain-specific problem in communication systems for improving channel estimation, but it is incremental as it builds on existing neural network methods with a novel architectural tweak.
The paper tackled the challenge of learning rapidly varying channel coefficients from user locations in communication systems by proposing a frugal, model-based hypernetwork that separates low and high frequency components. Simulation results showed it outperforms standard approaches on realistic synthetic data.
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.