Semantic encoders and decoders for digital semantic communication (SC) often struggle to adapt to variations in unpredictable channel environments and diverse system designs. To address these challenges, this paper proposes a novel framework for training semantic encoders and decoders to enable channel-adaptive digital SC. The core idea is to use binary symmetric channel (BSC) as a universal representation of generic digital communications, eliminating the need to specify channel environments or system designs. Based on this idea, our framework employs parallel BSCs to equivalently model the relationship between the encoder's output and the decoder's input. The bit-flip probabilities of these BSCs are treated as trainable parameters during end-to-end training, with varying levels of regularization applied to address diverse requirements in practical systems. The advantage of our framework is justified by developing a training-aware communication strategy for the inference stage. This strategy makes communication bit errors align with the pre-trained bit-flip probabilities by adaptively selecting power and modulation levels based on practical requirements and channel conditions. Simulation results demonstrate that the proposed framework outperforms existing training approaches in terms of both task performance and power consumption.

翻译：数字语义通信中的语义编码器与解码器往往难以适应不可预测的信道环境变化和多样化的系统设计。为解决这些挑战，本文提出一种新颖的语义编码器与解码器训练框架，以实现信道自适应的数字语义通信。其核心思想是采用二进制对称信道作为通用数字通信的统一表征，从而无需预先指定具体信道环境或系统设计。基于这一思想，本框架通过并行二进制对称信道等效建模编码器输出与解码器输入之间的关系。在端到端训练过程中，将这些二进制对称信道的比特翻转概率作为可训练参数，并通过施加不同强度的正则化以满足实际系统中的多样化需求。通过为推理阶段设计训练感知的通信策略，本框架的优势得以验证：该策略根据实际需求与信道条件自适应选择功率与调制等级，使通信误码与预训练的比特翻转概率相匹配。仿真结果表明，所提框架在任务性能与功耗方面均优于现有训练方法。