  With the recent advancements in edge artificial intelligence (AI), future
sixth-generation (6G) networks need to support new AI tasks such as
classification and clustering apart from data recovery. Motivated by the
success of deep learning, the semantic-aware and task-oriented communications
with deep joint source and channel coding (JSCC) have emerged as new paradigm
shifts in 6G from the conventional data-oriented communications with separate
source and channel coding (SSCC). However, most existing works focused on the
deep JSCC designs for one task of data recovery or AI task execution
independently, which cannot be transferred to other unintended tasks.
Differently, this paper investigates the JSCC semantic communications to
support multi-task services, by performing the image data recovery and
classification task execution simultaneously. First, we propose a new
end-to-end deep JSCC framework by unifying the coding rate reduction
maximization and the mean square error (MSE) minimization in the loss function.
Here, the coding rate reduction maximization facilitates the learning of
discriminative features for enabling to perform classification tasks directly
in the feature space, and the MSE minimization helps the learning of
informative features for high-quality image data recovery. Next, to further
improve the robustness against variational wireless channels, we propose a new
gated deep JSCC design, in which a gated net is incorporated for adaptively
pruning the output features to adjust their dimensions based on channel
conditions. Finally, we present extensive numerical experiments to validate the
performance of our proposed deep JSCC designs as compared to various benchmark
schemes.
