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.

翻译：语义通信有望成为第六代（6G）无线网络的核心新范式。现有研究大多隐式利用信道信息进行编解码器训练，这导致信道类型或统计特征发生变化时通信性能恶化。针对不同信道带来的这一挑战，本文提出了一种新颖的信道可迁移语义通信（CT-SemCom）框架，该框架可使在一种信道类型上训练的编解码器适应其他信道类型。进一步，将所提框架与集成非正交多址接入技术的正交频分复用系统（即OFDM-NOMA系统）相结合，提出了一个功率分配问题，以实现从加性高斯白噪声（AWGN）信道到多子载波瑞利衰落信道的迁移。随后我们设计了语义相似对偶变换（SSDT）算法，以低复杂度推导出解析解。仿真结果表明，所提出的基于SSDT算法的CT-SemCom框架在信道可迁移性方面显著优于现有工作，例如，在瑞利衰落信道不同方差条件下，图像传输的峰值信噪比（PSNR）提升了4.2-7.3 dB。