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Deep Residual Learning Meets OFDM Channel Estimation

In this letter we apply deep learning tools to conduct channel estimation for an orthogonal frequency division multiplexing (OFDM) system based on downlink pilots. To be specific, a residual learning based deep neural network specifically designed for channel estimation is introduced. Due to the compact network size as well as the underlying network architecture, the computation cost can be greatly reduced. Furthermore, this residual network architecture is compatible with any downlink pilot patterns making it compatible for modern wireless systems. The estimation error of the introduced residual learning approach is evaluated under 3rd Generation Partnership Project (3GPP) channel models. It outperforms other deep learning based estimation method with comparable to minimum mean square error (MMSE) estimation performance.