Building the Self-Improvement Loop: Error Detection and Correction in Goal-Oriented Semantic Communications

2024-11-03

IEEE Conference on Standards for Communications and Networking

AI Summary

Semantic communication (SemCom) improves transmission efficiency but lacks mechanisms to detect and correct semantic errors—i.e., discrepancies between sender and receiver semantic representations—severely compromising system reliability. This paper formally defines semantic error for the first time and proposes an end-to-end semantic reliability assurance framework for goal-oriented SemCom. Our approach comprises: (1) online semantic error monitoring via Gaussian process (GP)-based latent space modeling; (2) a human-in-the-loop reinforcement learning (HITL-RL) correction mechanism enabling adaptive, closed-loop semantic-level optimization; and (3) integration of semantic similarity metrics, dynamic channel modeling, and user feedback. Experiments demonstrate that the framework significantly reduces semantic error rates and enhances task completion rates and system robustness under diverse perturbations—including adversarial attacks, channel distortions, feature drift, and shifts in user preferences.

Technology Category

• Natural Language Processing: Safety and Robustness
• Multiagent Systems: Agent Communication
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Error detection and correction are essential for ensuring robust and reliable operation in modern communication systems, particularly in complex transmission environments. However, discussions on these topics have largely been overlooked in semantic communication (SemCom), which focuses on transmitting meaning rather than symbols, leading to significant improvements in communication efficiency. Despite these advantages, semantic errors-stemming from discrepancies between transmitted and received meanings-present a major challenge to system reliability. This paper addresses this gap by proposing a comprehensive framework for detecting and correcting semantic errors in SemCom systems. We formally define semantic error, detection, and correction mechanisms, and identify key sources of semantic errors. To address these challenges, we develop a Gaussian process (GP)-based method for latent space monitoring to detect errors, alongside a human-in-the-loop reinforcement learning (HITL-RL) approach to optimize semantic model configurations using user feedback. Experimental results validate the effectiveness of the proposed methods in mitigating semantic errors under various conditions, including adversarial attacks, input feature changes, physical channel variations, and user preference shifts. This work lays the foundation for more reliable and adaptive SemCom systems with robust semantic error management techniques.

Problem

• Detecting and correcting semantic errors in communication systems
• Addressing reliability challenges in semantic communication environments
• Developing robust error management for adaptive semantic transmissions

Innovation

• Gaussian process latent space monitoring
• Human-in-the-loop reinforcement learning optimization
• Comprehensive semantic error detection and correction framework