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 this https URL

本文提出了一种新的适应性深度联合源通信信道编码方案，支持多速率传输，使用单个深度神经网络模型，并通过政策网络动态控制速率，该方案是首个能够使用单个网络模型自动调整速率的深度联合源通信信道编码方案。