Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native Communication

TL;DR

This paper introduces Bayesian inverse reasoning methods to improve heterogeneous semantic-native communication by inferring agents' contexts, demonstrating that the linearized approach offers better accuracy and efficiency.

Contribution

It proposes Bayesian inverse CR and Bayesian inverse linearized CR methods for context inference in SNC, with the latter being more computationally efficient and accurate.

Findings

Bayesian iLCR outperforms Bayesian iCR in accuracy.

Bayesian iLCR requires less computation.

Heterogeneous SNC with Bayesian iLCR improves communication effectiveness.

Abstract

This work deals with the heterogeneous semantic-native communication (SNC) problem. When agents do not share the same communication context, the effectiveness of contextual reasoning (CR) is compromised calling for agents to infer other agents' context. This article proposes a novel framework for solving the inverse problem of CR in SNC using two Bayesian inference methods, namely: Bayesian inverse CR (iCR) and Bayesian inverse linearized CR (iLCR). The first proposed Bayesian iCR method utilizes Markov Chain Monte Carlo (MCMC) sampling to infer the agent's context while being computationally expensive. To address this issue, a Bayesian iLCR method is leveraged which obtains a linearized CR (LCR) model by training a linear neural network. Experimental results show that the Bayesian iLCR method requires less computation and achieves higher inference accuracy compared to Bayesian iCR. Additionally, heterogeneous SNC based on the context obtained through the Bayesian iLCR method shows better communication effectiveness than that of Bayesian iCR. Overall, this work provides valuable insights and methods to improve the effectiveness of SNC in situations where agents have different contexts.