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

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