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

TL;DR

DeepJSCC-l++ is a transformer-based wireless image transmission scheme that adaptively handles multiple bandwidth ratios and SNRs with a single model, outperforming traditional digital methods.

Contribution

The paper introduces DeepJSCC-l++, a novel ViT-based model that adaptively manages various bandwidths and SNRs using a new training methodology with side information.

Findings

Achieves adaptive performance across multiple bandwidth ratios and SNRs.

Outperforms digital BPG + channel coding baseline in simulations.

Uses shifted window transformer as backbone for improved robustness.

Abstract

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.