  Adaptive rate control for deep joint source and channel coding (JSCC) is
considered as an effective approach to transmit sufficient information in
scenarios with limited communication resources. We propose a deep JSCC scheme
for wireless image transmission with entropy-aware adaptive rate control, using
a single deep neural network to support multiple rates and automatically adjust
the rate based on the feature maps of the input image and their entropy, as
well as the channel conditions. In particular, we maximize the entropy of the
feature maps to increase the average information carried by each transmitted
symbol during the training. We further decide which feature maps should be
activated based on their entropy, which improves the efficiency of the
transmitted symbols. We also propose a pruning module to remove less important
pixels in the activated feature maps in order to further improve transmission
efficiency. The experimental results demonstrate that our proposed scheme
learns an effective rate control strategy that reduces the required channel
bandwidth while preserving the quality of the reconstructed images.
