  We present a deep learning based joint source channel coding (JSCC) scheme
for wireless image transmission over multipath fading channels with non-linear
signal clipping. The proposed encoder and decoder use convolutional neural
networks (CNN) and directly map the source images to complex-valued baseband
samples for orthogonal frequency division multiplexing (OFDM) transmission. The
proposed model-driven machine learning approach eliminates the need for
separate source and channel coding while integrating an OFDM datapath to cope
with multipath fading channels. The end-to-end JSCC communication system
combines trainable CNN layers with non-trainable but differentiable layers
representing the multipath channel model and OFDM signal processing blocks. Our
results show that injecting domain expert knowledge by incorporating OFDM
baseband processing blocks into the machine learning framework significantly
enhances the overall performance compared to an unstructured CNN. Our method
outperforms conventional schemes that employ state-of-the-art but separate
source and channel coding such as BPG and LDPC with OFDM. Moreover, our method
is shown to be robust against non-linear signal clipping in OFDM for various
channel conditions that do not match the model parameter used during the
training.
