Joint Semantic-Native Communication and Inference via Minimal Simplicial Structures

TL;DR

This paper introduces a novel semantic communication framework using minimal simplicial structures for efficient data inference, significantly reducing payload size while maintaining high accuracy, and enhancing inference performance under various channel conditions.

Contribution

The work proposes a method to identify minimal semantic simplicial structures for communication, combining topological data analysis with neural autoencoders to improve inference efficiency and accuracy.

Findings

85% reduction in payload size without accuracy loss

25% improvement in query accuracy over local methods

Enhanced inference accuracy at low SNR with channel semantics

Abstract

In this work, we study the problem of semantic communication and inference, in which a student agent (i.e. mobile device) queries a teacher agent (i.e. cloud sever) to generate higher-order data semantics living in a simplicial complex. Specifically, the teacher first maps its data into a k-order simplicial complex and learns its high-order correlations. For effective communication and inference, the teacher seeks minimally sufficient and invariant semantic structures prior to conveying information. These minimal simplicial structures are found via judiciously removing simplices selected by the Hodge Laplacians without compromising the inference query accuracy. Subsequently, the student locally runs its own set of queries based on a masked simplicial convolutional autoencoder (SCAE) leveraging both local and remote teacher's knowledge. Numerical results corroborate the effectiveness of the proposed approach in terms of improving inference query accuracy under different channel conditions and simplicial structures. Experiments on a coauthorship dataset show that removing simplices by ranking the Laplacian values yields a 85% reduction in payload size without sacrificing accuracy. Joint semantic communication and inference by masked SCAE improves query accuracy by 25% compared to local student based query and 15% compared to remote teacher based query. Finally, incorporating channel semantics is shown to effectively improve inference accuracy, notably at low SNR values.