  We investigate joint source channel coding (JSCC) for wireless image
transmission over multipath fading channels. Inspired by recent works on deep
learning based JSCC and model-based learning methods, we combine an autoencoder
with orthogonal frequency division multiplexing (OFDM) to cope with multipath
fading. The proposed encoder and decoder use convolutional neural networks
(CNNs) and directly map the source images to complex-valued baseband samples
for OFDM transmission. The multipath channel and OFDM are represented by
non-trainable (deterministic) but differentiable layers so that the system can
be trained end-to-end. Furthermore, our JSCC decoder further incorporates
explicit channel estimation, equalization, and additional subnets to enhance
the performance. The proposed method exhibits 2.5 -- 4 dB SNR gain for the
equivalent image quality compared to conventional schemes that employ
state-of-the-art but separate source and channel coding such as BPG and LDPC.
The performance further improves when the system incorporates the channel state
information (CSI) feedback. The proposed scheme is robust against OFDM signal
clipping and parameter mismatch for the channel model used in training and
evaluation.
