Implicit Semantic Communication Based on Bayesian Reconstruction Framework

TL;DR

This paper introduces a Bayesian hypergraph inference framework for semantic communication that effectively captures high-order semantic relations, significantly improving the recovery of implicit meanings from explicit pairwise data.

Contribution

It presents a novel Bayesian hypergraph inference method that models high-order semantic relations, enhancing the ability to recover implicit meanings in semantic communication.

Findings

Achieves up to 90% accuracy in recovering high-order hyperedges

Effectively captures high-order interactions beyond pairwise relations

Demonstrates superior performance on real-world datasets

Abstract

Semantic communication is a novel communication paradigm that focuses on the transportation and delivery of the \emph{meaning} of messages. Recent results have verified that a graphical structure provides the most expressive and structurally faithful formalism for representing the relational semantics in most information sources. However, most existing works represent the semantics based on pairwise relation-based graphs, which cannot capture the higher-order interactions that are essential for some semantic sources. This paper proposes a novel Bayesian hypergraph inference-based semantic communication framework that can directly recover implicit semantic information involving high-order hyperedges at the receiver based on the pairwise relation-based explicit semantics sent by the transmitter. Experimental results based on real-world datasets demonstrated that the proposed SBRF achieves up to 90\% recovery accuracy of the high-order hyperedges based on the pairwise relation-based explicit semantics.