  Semantic communication (SemCom) demonstrates strong superiority over
conventional bit-level accurate transmission, by only attempting to recover the
essential semantic information of data. In this paper, in order to tackle the
non-differentiability of channels, we propose an alternate learning based
SemCom system for visual transmission, named SparseSBC. Specially, SparseSBC
leverages two separate Deep Neural Network (DNN)-based models at the
transmitter and receiver, respectively, and learns the encoding and decoding in
an alternate manner, rather than the joint optimization in existing literature,
so as to solving the non-differentiability in the channel. In particular, a
``self-critic" training scheme is leveraged for stable training. Moreover, the
DNN-based transmitter generates a sparse set of bits in deduced ``semantic
bases", by further incorporating a binary quantization module on the basis of
minimal detrimental effect to the semantic accuracy. Extensive simulation
results validate that SparseSBC shows efficient and effective transmission
performance under various channel conditions, and outperforms typical SemCom
solutions.
