# DeepJSCC-l++: Robust and Bandwidth-Adaptive Wireless Image Transmission

arXiv: https://arxiv.org/abs/2305.13161 · HTML: https://mlsanity.com/paper/2305.13161/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2023.

## Built on by (most cited first, 8 of 8)

- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [Multi-hop Deep Joint Source-Channel Coding with Deep Hash Distillation for Semantically Aligned Image Recovery](https://mlsanity.com/paper/2510.06868/) (2025)
- [SNR-EQ-JSCC: Joint Source-Channel Coding with SNR-Based Embedding and Query](https://mlsanity.com/paper/2501.04732/) (2025)
- [SAFE: Semantic Adaptive Feature Extraction with Rate Control for 6G Wireless Communications](https://mlsanity.com/paper/2410.01597/) (2024)
- [Variational Source-Channel Coding for Semantic Communication](https://mlsanity.com/paper/2410.08222/) (2024)
- [SafeStep: An Interactive Demonstration of Semantic Communication for Pedestrian Safety Monitoring](https://mlsanity.com/paper/2608.27688/) (2026)
- [Beyond Token Accuracy: Prioritizing What Matters for Visual Reconstruction](https://mlsanity.com/paper/2610.03822/) (2026)

## Abstract (arXiv)

This paper presents a novel vision transformer (ViT) based deep joint source channel coding (DeepJSCC) scheme, dubbed DeepJSCC-l++, which can be adaptive to multiple target bandwidth ratios as well as different channel signal-to-noise ratios (SNRs) using a single model. To achieve this, we train the proposed DeepJSCC-l++ model with different bandwidth ratios and SNRs, which are fed to the model as side information. The reconstruction losses corresponding to different bandwidth ratios are calculated, and a new training methodology is proposed, which dynamically assigns different weights to the losses of different bandwidth ratios according to their individual reconstruction qualities. Shifted window (Swin) transformer, is adopted as the backbone for our DeepJSCC-l++ model. Through extensive simulations it is shown that the proposed DeepJSCC-l++ and successive refinement models can adapt to different bandwidth ratios and channel SNRs with marginal performance loss compared to the separately trained models. We also observe the proposed schemes can outperform the digital baseline, which concatenates the BPG compression with capacity-achieving channel code.