  This article presents our initial results in deep learning for channel
estimation and signal detection in orthogonal frequency-division multiplexing
(OFDM). OFDM has been widely adopted in wireless broadband communications to
combat frequency-selective fading in wireless channels. In this article, we
take advantage of deep learning in handling wireless OFDM channels in an
end-to-end approach. Different from existing OFDM receivers that first estimate
CSI explicitly and then detect/recover the transmitted symbols with the
estimated CSI, our deep learning based approach estimates CSI implicitly and
recovers the transmitted symbols directly. To address channel distortion, a
deep learning model is first trained offline using the data generated from the
simulation based on the channel statistics and then used for recovering the
online transmitted data directly. From our simulation results, the deep
learning based approach has the ability to address channel distortions and
detect the transmitted symbols with performance comparable to minimum
mean-square error (MMSE) estimator. Furthermore, the deep learning based
approach is more robust than conventional methods when fewer training pilots
are used, the cyclic prefix (CP) is omitted, and nonlinear clipping noise is
presented. In summary, deep learning is a promising tool for channel estimation
and signal detection in wireless communications with complicated channel
distortions and interferences.
