  We present a novel adaptive deep joint source-channel coding (JSCC) scheme
for wireless image transmission. The proposed scheme supports multiple rates
using a single deep neural network (DNN) model and learns to dynamically
control the rate based on the channel condition and image contents.
Specifically, a policy network is introduced to exploit the tradeoff space
between the rate and signal quality. To train the policy network, the
Gumbel-Softmax trick is adopted to make the policy network differentiable and
hence the whole JSCC scheme can be trained end-to-end. To the best of our
knowledge, this is the first deep JSCC scheme that can automatically adjust its
rate using a single network model. Experiments show that our scheme
successfully learns a reasonable policy that decreases channel bandwidth
utilization for high SNR scenarios or simple image contents. For an arbitrary
target rate, our rate-adaptive scheme using a single model achieves similar
performance compared to an optimized model specifically trained for that fixed
target rate. To reproduce our results, we make the source code publicly
available at https://github.com/mingyuyng/Dynamic_JSCC.
