  Task-oriented semantic communications have achieved significant performance
gains. However, the employed deep neural networks in semantic communications
have to be updated when the task is changed or multiple models need to be
stored for performing different tasks. To address this issue, we develop a
unified deep learning-enabled semantic communication system (U-DeepSC), where a
unified end-to-end framework can serve many different tasks with multiple
modalities of data. As the number of required features varies from task to
task, we propose a vector-wise dynamic scheme that can adjust the number of
transmitted symbols for different tasks. Moreover, our dynamic scheme can also
adaptively adjust the number of transmitted features under different channel
conditions to optimize the transmission efficiency. Particularly, we devise a
lightweight feature selection module (FSM) to evaluate the importance of
feature vectors, which can hierarchically drop redundant feature vectors and
significantly accelerate the inference. To reduce the transmission overhead, we
then design a unified codebook for feature representation to serve multiple
tasks, where only the indices of these task-specific features in the codebook
are transmitted. According to the simulation results, the proposed U-DeepSC
achieves comparable performance to the task-oriented semantic communication
system designed for a specific task but with significant reduction in both
transmission overhead and model size.
