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We present a deep learning based joint source channel coding (JSCC) schemefor wireless image transmission over multipath fading channels with non-linearsignal clipping. The proposed encoder and decoder use convolutional neuralnetworks (CNN) and directly map the source images to complex-valued basebandsamples for orthogonal frequency division multiplexing (OFDM) transmission. Theproposed model-driven machine learning approach eliminates the need forseparate source and channel coding while integrating an OFDM datapath to copewith multipath fading channels. The end-to-end JSCC communication systemcombines trainable CNN layers with non-trainable but differentiable layersrepresenting the multipath channel model and OFDM signal processing blocks. Ourresults show that injecting domain expert knowledge by incorporating OFDMbaseband processing blocks into the machine learning framework significantlyenhances the overall performance compared to an unstructured CNN. Our methodoutperforms conventional schemes that employ state-of-the-art but separatesource and channel coding such as BPG and LDPC with OFDM. Moreover, our methodis shown to be robust against non-linear signal clipping in OFDM for variouschannel conditions that do not match the model parameter used during thetraining.

Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM