# Two Birds with One Stone: Multi-Task Semantic Communications Systems over Relay Channel

arXiv: https://arxiv.org/abs/2410.12302 · HTML: https://mlsanity.com/paper/2410.12302/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications](https://mlsanity.com/paper/2309.08895/) (2023)
- [SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2308.09361/) (2023)
- [Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data](https://mlsanity.com/paper/2205.00271/) (2022)
- [Semantic-Forward Relaying: A Novel Framework Towards 6G Cooperative Communications](https://mlsanity.com/paper/2310.07987/) (2023)
- [A Unified Multi-Task Semantic Communication System with Domain Adaptation](https://mlsanity.com/paper/2206.00254/) (2022)
- [Scalable Multi-task Semantic Communication System with Feature Importance Ranking](https://mlsanity.com/paper/2304.05882/) (2023)
- [MambaJSCC: Adaptive Deep Joint Source-Channel Coding with Generalized State Space Model](https://mlsanity.com/paper/2409.16592/) (2024)
- [Semantic Forwarding for Next Generation Relay Networks](https://mlsanity.com/paper/2401.17247/) (2024)
- [Multi-User Semantic Fusion for Semantic Communications over Degraded Broadcast Channels](https://mlsanity.com/paper/2406.10556/) (2024)
- [A Relay System for Semantic Image Transmission based on Shared Feature Extraction and Hyperprior Entropy Compression](https://mlsanity.com/paper/2311.10492/) (2023)

## Abstract (arXiv)

In this paper, we propose a novel multi-task, multi-link relay semantic communications (MTML-RSC) scheme that enables the destination node to simultaneously perform image reconstruction and classification with one transmission from the source node. In the MTML-RSC scheme, the source node broadcasts a signal using semantic communications, and the relay node forwards the signal to the destination. We analyze the coupling relationship between the two tasks and the two links (source-to-relay and source-to-destination) and design a semantic-focused forward method for the relay node, where it selectively forwards only the semantics of the relevant class while ignoring others. At the destination, the node combines signals from both the source node and the relay node to perform classification, and then uses the classification result to assist in decoding the signal from the relay node for image reconstructing. Experimental results demonstrate that the proposed MTML-RSC scheme achieves significant performance gains, e.g., 1.73 dB improvement in peak-signal-to-noise ratio (PSNR) for image reconstruction and increasing the accuracy from 64.89% to 70.31% for classification.