  In this paper, we propose a novel deep learning based approach for joint
channel estimation and signal detection in orthogonal frequency division
multiplexing (OFDM) systems by exploring the time and frequency correlation of
wireless fading channels. Specifically, a Channel Estimation Network (CENet) is
designed to replace the conventional interpolation procedure in pilot-aided
estimation scheme. Then, based on the outcome of the CENet, a Channel
Conditioned Recovery Network (CCRNet) is designed to recover the transmit
signal. Experimental results demonstrate that CENet and CCRNet achieve superior
performance compared with conventional estimation and detection methods. In
addition, both networks are shown to be robust to the variation of parameter
chances, which makes them appealing for practical implementation.
