Transformers, known for their attention mechanisms, have proven highly effective in focusing on critical elements within complex data. This feature can effectively be used to address the time-varying channels in wireless communication systems. In this work, we introduce a channel-aware adaptive framework for semantic communication, where different regions of the image are encoded and compressed based on their semantic content. By employing vision transformers, we interpret the attention mask as a measure of the semantic contents of the patches and dynamically categorize the patches to be compressed at various rates as a function of the instantaneous channel bandwidth. Our method enhances communication efficiency by adapting the encoding resolution to the content's relevance, ensuring that even in highly constrained environments, critical information is preserved. We evaluate the proposed adaptive transmission framework using the TinyImageNet dataset, measuring both reconstruction quality and accuracy. The results demonstrate that our approach maintains high semantic fidelity while optimizing bandwidth, providing an effective solution for transmitting multi-resolution data in limited bandwidth conditions.

翻译：Transformer以其注意力机制而闻名，在聚焦复杂数据中的关键元素方面已被证明极为有效。这一特性可有效用于应对无线通信系统中的时变信道。在本工作中，我们提出了一种信道感知的自适应语义通信框架，其中图像的不同区域根据其语义内容进行编码和压缩。通过采用视觉Transformer，我们将注意力掩码解释为图像块语义内容的度量，并依据瞬时信道带宽将图像块动态分类为以不同速率压缩的对象。我们的方法通过使编码分辨率适应内容的相关性来提升通信效率，确保即使在高度受限的环境中，关键信息也能得以保留。我们使用TinyImageNet数据集评估所提出的自适应传输框架，同时测量重建质量和精度。结果表明，我们的方法在优化带宽的同时保持了高语义保真度，为在有限带宽条件下传输多分辨率数据提供了有效的解决方案。