Pragmatic Goal-Oriented Communications under Semantic-Effectiveness Channel Errors

TL;DR

This work tackles language and knowledge disparities in future AI‑native 6G networks by modeling errors at semantic and effectiveness levels caused by language mismatch between transmitter and receiver. It introduces a formal framework with semantic SM and effectiveness EM metrics and proposes an optimal transport–based semantic equalization method built on a finite transformation codebook to mitigate misinterpretations. The approach leverages a measurable transformation $T$, a codebook $\mathcal{C}_{\mathcal{T}}$, and a probabilistic policy $\pi$ to minimize semantic or effectiveness risk, with definitions of $R_S$ and $R_E$ guiding the optimization. Numerical results on a grid‑world task show emergent language diversity and demonstrate that SM/EM‑oriented equalization can reduce language mismatch effects under noisy channels, while stochastic decoding can further enhance task performance, highlighting the method's potential for robust, goal‑oriented communication among heterogeneous AI agents in next‑generation networks.

Abstract

In forthcoming AI-assisted 6G networks, integrating semantic, pragmatic, and goal-oriented communication strategies becomes imperative. This integration will enable sensing, transmission, and processing of exclusively pertinent task data, ensuring conveyed information possesses understandable, pragmatic semantic significance, aligning with destination needs and goals. Without doubt, no communication is error free. Within this context, besides errors stemming from typical wireless communication dynamics, potential distortions between transmitter-intended and receiver-interpreted meanings can emerge due to limitations in semantic processing capabilities, as well as language and knowledge representation disparities between transmitters and receivers. The main contribution of this paper is two-fold. First, it proposes and details a novel mathematical modeling of errors stemming from language mismatches at both semantic and effectiveness levels. Second, it provides a novel algorithmic solution to counteract these types of errors which leverages optimal transport theory. Our numerical results show the potential of the proposed mechanism to compensate for language mismatches, thereby enhancing the attainability of reliable communication under noisy communication environments.

Figures (5)

• Figure 1: System model.
• Figure 2: Mapping of the learned languages. Both encoder-decoder pairs were trained to solve the same task in identical conditions only differing in stochastic training aspects.
• Figure 3: Average episode length (lower is better) for the different communication strategies with varying snr for a deterministic decoder ($\beta\xrightarrow{}\infty$ in \ref{['eq:decoder_softmax']}).
• Figure 4: Average episode length (lower is better) for the different communication strategies with varying snr for a stochastic decoder with $\beta=5$ in \ref{['eq:decoder_softmax']}.
• Figure 5: Average episode length (lower is better) for the different communication strategies with varying $\beta$ and $\ac{snr}=\infty$.

Theorems & Definitions (3)

• Definition 1: Semantic mismatch
• Definition 2: Effectiveness mismatch
• Definition 3: Semantic language equalization