# Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks

arXiv: https://arxiv.org/abs/2403.10613 · HTML: https://mlsanity.com/paper/2403.10613/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2024.

## Built on by (most cited first, 9 of 9)

- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in Wireless Image Transmission](https://mlsanity.com/paper/2501.01138/) (2025)
- [Adaptive Semantic Token Communication for Transformer-based Edge Inference](https://mlsanity.com/paper/2505.17604/) (2025)
- [Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding](https://mlsanity.com/paper/2504.03690/) (2025)
- [SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks](https://mlsanity.com/paper/2511.01466/) (2025)
- [In-Context Source and Channel Coding](https://mlsanity.com/paper/2601.10267/) (2026)
- [TONIC: Token-Centric Semantic Communication for Task-Oriented Wireless Systems](https://mlsanity.com/paper/2605.21553/) (2026)
- [Joint AoI and Handover Optimization in Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2509.12716/) (2025)
- [Semantic-Aware Generative Image Transmission for Resource-Constrained Visual IoT Systems](https://mlsanity.com/paper/2606.28398/) (2026)

## Abstract (arXiv)

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 each block, together with an adaptive transmission module, which allows the model to seamlessly adapt to different channel qualities and the transmission powers}. Simulation results demonstrate the superior performance of DeepJSCC-PF compared to the state-of-the-art BPG image compression algorithm operating at the maximum achievable rate of conventional decode-and-forward and compress-and-forward protocols, in both half- and full-duplex relay scenarios over AWGN and Rayleigh fading channels.