Deep Joint Source Channel Coding for Wireless Image Transmission with OFDM

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 bas...

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Vydané v:IEEE International Conference on Communications (2003) s. 1 - 6
Hlavní autori: Yang, Mingyu, Bian, Chenghong, Kim, Hun-Seok
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2021
Predmet:
Baseband
Deep learning
deep neural net-works
Fading channels
Image communication
Joint source channel coding
model-driven machine learning
OFDM
Training
Wireless communication
ISSN:1938-1883
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Popis
Shrnutí: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.
ISSN:1938-1883
DOI:10.1109/ICC42927.2021.9500996