Domain Knowledge aided Neural Network for Wireless Channel Estimation

Channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) transmission is well investigated with model based approaches. Recent effort also explores the data driven approaches to exploit the capabilities of Neural Networks (NNs) to estimate the channel. These models are mostly being developed as black box without any anchor to the theory of wireless signal propagation. We propose a NN model, where the structure and parameters are derived from the domain knowledge of wireless signal and channel characteristics. Our model is developed in two stages: the first stage handles the noise reduction, while the second stage extracts the channel characteristics to reduce error caused by multipath. We have used the knowledge of signal to noise ratio and subcarrier correlation due to channel delay spread to empower the two stages of the proposed model. Our results also show that induction of domain knowledge results in reduction of data dependency by 60%. Our model outperforms the practical model based methods as well as blind data driven approaches. It achieves ∼10 dB improvement over Least Square channel estimation.