  As one of the key techniques to realize semantic communications, end-to-end
optimized neural joint source-channel coding (JSCC) has made great progress
over the past few years. A general trend in many recent works pushing the model
adaptability or the application diversity of neural JSCC is based on the
convolutional neural network (CNN) backbone, whose model capacity is yet
limited, inherently leading to inferior system coding gain against traditional
coded transmission systems. In this paper, we establish a new neural JSCC
backbone that can also adapt flexibly to diverse channel conditions and
transmission rates within a single model, our open-source project aims to
promote the research in this field. Specifically, we show that with elaborate
design, neural JSCC codec built on the emerging Swin Transformer backbone
achieves superior performance than conventional neural JSCC codecs built upon
CNN, while also requiring lower end-to-end processing latency. Paired with two
spatial modulation modules that scale latent representations based on the
channel state information and target transmission rate, our baseline SwinJSCC
can further upgrade to a versatile version, which increases its capability to
adapt to diverse channel conditions and rate configurations. Extensive
experimental results show that our SwinJSCC achieves better or comparable
performance versus the state-of-the-art engineered BPG + 5G LDPC coded
transmission system with much faster end-to-end coding speed, especially for
high-resolution images, in which case traditional CNN-based JSCC yet falls
behind due to its limited model capacity.
