  Semantic communication has witnessed a great progress with the development of
natural language processing (NLP) and deep learning (DL). Although existing
semantic communication technologies can effectively reduce errors in semantic
interpretation, most of these solutions adopt a fixed bit length structure,
along with a rigid transmission scheme, which is inefficient and lacks
scalability when faced with different meanings and signal-to-noise ratio (SNR)
conditions. In this paper, we explore the impact of adaptive bit lengths on
semantic coding (SC) under various channel conditions. First, we propose
progressive semantic hybrid automatic repeat request (HARQ) schemes that
utilize incremental knowledge (IK) to simultaneously reduce the communication
cost and semantic error. On top of this, we design a novel semantic encoding
solution with multi-bit length selection. In this fashion, the transmitter
employs a policy network to decide the appropriate coding rate, so as to secure
the correct information delivery at the cost of minimal bits. Moreover, a
specific denoiser is further introduced to reduce the semantic errors
encountered in the transmission process according to the semantic
characteristics of context. Extensive simulation results have been conducted to
verify the effectiveness of the proposed solution.
