Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native
  Communication
Hyowoon Seo Yoonseong Kang Mehdi Bennis Wan Choi
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.