Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction

TL;DR

This paper introduces a graph attention-based module for multi-task semantic communication that captures feature correlations, improving transmission efficiency and task performance over existing models.

Contribution

The paper proposes a novel graph attention inter-block module that enhances feature correlation extraction in multi-task semantic communication systems.

Findings

Outperforms existing models by 11.4% on CityScapes 2Task dataset.

Achieves 3.97% higher accuracy on NYU V2 3Task dataset.

Effective under low bandwidth conditions with high compression ratios.

Abstract

Multi-task semantic communication can serve multiple learning tasks using a shared encoder model. Existing models have overlooked the intricate relationships between features extracted during an encoding process of tasks. This paper presents a new graph attention inter-block (GAI) module to the encoder/transmitter of a multi-task semantic communication system, which enriches the features for multiple tasks by embedding the intermediate outputs of encoding in the features, compared to the existing techniques. The key idea is that we interpret the outputs of the intermediate feature extraction blocks of the encoder as the nodes of a graph to capture the correlations of the intermediate features. Another important aspect is that we refine the node representation using a graph attention mechanism to extract the correlations and a multi-layer perceptron network to associate the node representations with different tasks. Consequently, the intermediate features are weighted and embedded into the features transmitted for executing multiple tasks at the receiver. Experiments demonstrate that the proposed model surpasses the most competitive and publicly available models by 11.4% on the CityScapes 2Task dataset and outperforms the established state-of-the-art by 3.97% on the NYU V2 3Task dataset, respectively, when the bandwidth ratio of the communication channel (i.e., compression level for transmission over the channel) is as constrained as 1 12 .