标题： 盲训练用于信道自适应数字语义通信 显示英文标题

标题： Blind Training for Channel-Adaptive Digital Semantic Communications

摘要： 语义编码器和解码器在数字语义通信（SC）中常常难以适应不可预测的信道环境和多样的系统设计。 为解决这些挑战，本文提出了一种新的框架，用于训练语义编码器和解码器，以实现信道自适应的数字SC。 核心思想是使用二进制对称信道（BSC）作为通用数字通信的统一表示，从而无需指定信道环境或系统设计。 基于这一思想，我们的框架采用并行BSC来等效建模编码器输出与解码器输入之间的关系。 这些BSC的比特翻转概率在端到端训练过程中被视为可训练参数，并根据实际系统的不同需求应用不同程度的正则化。 通过为推理阶段开发一种训练感知的通信策略，验证了我们框架的优势。 该策略通过根据实际需求和信道条件自适应地选择功率和调制等级，使通信中的比特错误与预训练的比特翻转概率相匹配。 仿真结果表明，所提出的框架在任务性能和功耗方面均优于现有的训练方法。 显示英文摘要

摘要： 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.