Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks
Chenghong Bian, Yulin Shao, Haotian Wu, Emre Ozfatura, Deniz Gunduz

TL;DR
This paper presents deep joint source-channel coding schemes for image transmission over cooperative relay networks, utilizing neural networks and transformer models to improve performance over traditional methods in various relay configurations.
Contribution
Introduction of DeepJSCC-PF with transformer-based relay models and adaptive transmission strategies for enhanced image transmission in cooperative relay networks.
Findings
DeepJSCC-PF outperforms BPG in image quality at maximum rate.
Transformer-based relay models learn to generate correlated signals for beamforming.
Proposed methods work effectively over AWGN and Rayleigh fading channels.
Abstract
We introduce deep joint source-channel coding (DeepJSCC) schemes for image transmission over cooperative relay channels. The relay either amplifies-and-forwards its received signal, called DeepJSCC-AF, or leverages neural networks to extract relevant features from its received signal, called DeepJSCC-PF (Process-and-Forward). We consider both half- and full-duplex relays, and propose a novel transformer-based model at the relay. For a half-duplex relay, it is shown that the proposed scheme learns to generate correlated signals at the relay and source to obtain beamforming gains. In the full-duplex case, we introduce a novel block-based transmission strategy, in which the source transmits in blocks, and the relay updates its knowledge about the input signal after each block and generates its own signal. To enhance practicality, a single transformer-based model is used at the relay at…
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Taxonomy
TopicsCooperative Communication and Network Coding · Wireless Communication Security Techniques · Advanced Wireless Communication Technologies
MethodsAttention Is All You Need · Linear Layer · Softmax · Layer Normalization · Multi-Head Attention · Residual Connection · Dense Connections · Vision Transformer
