OFDM-Guided Deep Joint Source Channel Coding for Wireless Multipath Fading Channels

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 multi...

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Bibliographic Details
Published in:IEEE transactions on cognitive communications and networking 2022-06, Vol.8 (2), p.584-599
Main Authors: Yang, Mingyu, Bian, Chenghong, Kim, Hun-Seok
Format: Article
Language:English
Subjects:
Artificial neural networks
Channel coding
Channel estimation
Channels
Coders
Codes
Deep learning
deep neural networks
Equalization
Error correcting codes
Fading
Fading channels
Image coding
Image quality
Image transmission
Joint source channel coding
model-driven machine learning
OFDM
Orthogonal Frequency Division Multiplexing
Training
Wireless communication
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Summary: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 Better Portable Graphics (BPG) and Low-Density Parity-Check (LDPC) schemes. 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.
ISSN:2332-7731
2332-7731
DOI:10.1109/TCCN.2022.3151935