Learning-aided joint time-frequency channel estimation for 5G new radio

In this paper, we propose a learning-aided signal processing solution for channel estimation in 5G new radio (NR). Channel estimation is an important algorithm for baseband modem design. In 5G NR, estimating the channel is challenging due to two reasons. First, the pilot signals are transmitted over a small fraction of the available time-frequency resources. Second, the real time nature of physical layer processing introduces a strict limitation on the computational complexity of channel estimation. To this end, we propose a channel estimation technique that integrates a small one hidden layer neural network between two linear minimum mean squared error (LMMSE) interpolation blocks. While the neural network leverages the advantages of offline data-driven learning, the LMMSE blocks exploit the second order online channel statistics along time and frequency dimensions. The training procedure tunes the weights of the neural network by back-propagating through the time domain LMMSE interpolation block. We derive bounds on the training loss with the proposed method and show that our approach can improve the channel estimate.