Nonlinear Transform Source-Channel Coding for Semantic Communications

In this paper, we propose a class of high-efficiency deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform, it can be collected under the name nonlinear transform source-channel coding (NTSCC). In the considered model, the transmitter first learns a nonlinear analysis transform to map the source data into latent space, then transmits the latent representation to the receiver via deep joint source-channel coding. Our model incorporates the nonlinear transform as a strong prior to effectively extract the source semantic features and provide side information for source-channel coding. Unlike existing conventional deep joint source-channel coding…

• NN
  National Natural Science Foundation of China

  Awards: 62001049, 61971062, 92067202, 62071058
• NS
  Natural Science Foundation of Beijing Municipality

  Award: 4222012