Transformer-Aided Semantic Communications

TL;DR

This paper introduces a transformer-based semantic communication framework that uses attention mechanisms to prioritize and encode critical image regions, significantly improving transmission efficiency and semantic preservation in bandwidth-limited systems.

Contribution

The work applies vision transformers for semantic image compression, creating attention masks that enhance semantic preservation and bandwidth efficiency in communication systems.

Findings

Improved image reconstruction quality with limited data transmission.

Effective preservation of semantic information during compression.

Enhanced bandwidth efficiency through attention-guided encoding.

Abstract

The transformer structure employed in large language models (LLMs), as a specialized category of deep neural networks (DNNs) featuring attention mechanisms, stands out for their ability to identify and highlight the most relevant aspects of input data. Such a capability is particularly beneficial in addressing a variety of communication challenges, notably in the realm of semantic communication where proper encoding of the relevant data is critical especially in systems with limited bandwidth. In this work, we employ vision transformers specifically for the purpose of compression and compact representation of the input image, with the goal of preserving semantic information throughout the transmission process. Through the use of the attention mechanism inherent in transformers, we create an attention mask. This mask effectively prioritizes critical segments of images for transmission, ensuring that the reconstruction phase focuses on key objects highlighted by the mask. Our methodology significantly improves the quality of semantic communication and optimizes bandwidth usage by encoding different parts of the data in accordance with their semantic information content, thus enhancing overall efficiency. We evaluate the effectiveness of our proposed framework using the TinyImageNet dataset, focusing on both reconstruction quality and accuracy. Our evaluation results demonstrate that our framework successfully preserves semantic information, even when only a fraction of the encoded data is transmitted, according to the intended compression rates.