  Semantic communications are expected to become the core new paradigms of the
sixth generation (6G) wireless networks. Most existing works implicitly utilize
channel information for codecs training, which leads to poor communications
when channel type or statistical characteristics change. To tackle this issue
posed by various channels, a novel channel-transferable semantic communications
(CT-SemCom) framework is proposed, which adapts the codecs learned on one type
of channel to other types of channels. Furthermore, integrating the proposed
framework and the orthogonal frequency division multiplexing systems
integrating non-orthogonal multiple access technologies, i.e., OFDM-NOMA
systems, a power allocation problem to realize the transfer from additive white
Gaussian noise (AWGN) channels to multi-subcarrier Rayleigh fading channels is
formulated. We then design a semantics-similar dual transformation (SSDT)
algorithm to derive analytical solutions with low complexity. Simulation
results show that the proposed CT-SemCom framework with SSDT algorithm
significantly outperforms the existing work w.r.t. channel transferability,
e.g., the peak signal-to-noise ratio (PSNR) of image transmission improves by
4.2-7.3 dB under different variances of Rayleigh fading channels.
