  Deep learning-based joint source-channel coding (JSCC) is emerging as a
potential technology to meet the demand for effective data transmission,
particularly for image transmission. Nevertheless, most existing advancements
only consider analog transmission, where the channel symbols are continuous,
making them incompatible with practical digital communication systems. In this
work, we address this by involving the modulation process and consider mapping
the continuous channel symbols into discrete space. Recognizing the non-uniform
distribution of the output channel symbols in existing methods, we propose two
effective methods to improve the performance. Firstly, we introduce a uniform
modulation scheme, where the distance between two constellations is adjustable
to match the non-uniform nature of the distribution. In addition, we further
design a non-uniform modulation scheme according to the output distribution. To
this end, we first generate the constellations by performing feature clustering
on an analog image transmission system, then the generated constellations are
employed to modulate the continuous channel symbols. For both schemes, we
fine-tune the digital system to alleviate the performance loss caused by
modulation. Here, the straight-through estimator (STE) is considered to
overcome the non-differentiable nature. Our experimental results demonstrate
that the proposed schemes significantly outperform existing digital image
transmission systems.
