Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data

Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter. The actual observable data at the transmitter can also have non-identical distribution with the empirical data in the shared background knowledge library. To address these practical issues, this paper proposes a new neural network-based semantic communication system for image transmission, where the task is unaware at the transmitter and the data environment is dynamic. The system consists of two main parts, namely the semantic extraction (SE) network and the data adaptation (DA) network. The SE network learns how to extract the semantic information using a receiver-leading training process. By using domain adaptation technique from transfer learning, the DA network learns how to convert the data observed into a similar form of the empirical data that the SE network can process without re-training. Numerical experiments show that the proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution. The codes are available on https://github.com/SJTU-mxtao/Semantic-Communication-Systems.