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

• URL: http://arxiv.org/abs/2411.01544v1
• Date: Sun, 03 Nov 2024 12:29:23 GMT
• Title: Building the Self-Improvement Loop: Error Detection and Correction in Goal-Oriented Semantic Communications
• Authors: Peizheng Li, Xinyi Lin, Adnan Aijaz,
• Abstract summary: semantic communication (SemCom) 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 proposes a comprehensive framework for detecting and correcting semantic errors in SemCom systems.
• Score: 2.677520298504178
• License:
• 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.

Related papers

• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck [28.661084093544684]
  We propose a novel approach based on the information bottleneck (IB) principle and invariant risk minimization (IRM) framework. The proposed method aims to extract compact and informative features that possess high capability for effective domain-shift generalization. We show that the proposed scheme outperforms state-of-the-art approaches and achieves a better rate-distortion tradeoff.
  arXiv  Detail & Related papers  (2024-05-15T17:07:55Z)
• Pragmatic Goal-Oriented Communications under Semantic-Effectiveness Channel Errors [3.266331042379877]
  In forthcoming AI-assisted 6G networks, integrating semantic, pragmatic, and goal-oriented communication strategies becomes imperative. This paper proposes and details a novel mathematical modeling of errors stemming from language mismatches at both semantic and effectiveness levels. 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.
  arXiv  Detail & Related papers  (2024-01-19T16:43:47Z)
• Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts [89.04751776308656]
  This paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding. In response to security concerns, we introduce the application of covert communications aided by a friendly jammer.
  arXiv  Detail & Related papers  (2023-09-05T23:24:56Z)
• Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework [124.6509194665514]
  A novel implicit semantic-aware communication (iSAC) architecture is proposed for representing, communicating, and interpreting the implicit semantic meaning between source and destination users. A projection-based semantic encoder is proposed to convert the high-dimensional graphical representation of explicit semantics into a low-dimensional semantic constellation space for efficient physical channel transmission. A generative adversarial imitation learning-based solution, called G-RML, is proposed to enable the destination user to learn and imitate the implicit semantic reasoning process of source user.
  arXiv  Detail & Related papers  (2023-06-20T01:32:27Z)
• Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation [74.38561925376996]
  Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios. An effective semantic correction algorithm is proposed by mining the inference rule from the knowledge graph. For the multi-user cognitive semantic communication system, a message recovery algorithm is proposed to distinguish messages of different users.
  arXiv  Detail & Related papers  (2023-03-15T12:01:43Z)
• Cognitive Semantic Communication Systems Driven by Knowledge Graph [33.29303908864777]
  A cognitive semantic communication framework is proposed by exploiting knowledge graph. A simple, general and interpretable solution for semantic information detection is developed. Our proposed system is superior to other benchmark systems in terms of the data compression rate and the reliability of communication.
  arXiv  Detail & Related papers  (2022-02-24T08:26:18Z)
• Semi-supervised Domain Adaptive Structure Learning [72.01544419893628]
  Semi-supervised domain adaptation (SSDA) is a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. We introduce an adaptive structure learning method to regularize the cooperation of SSL and DA.
  arXiv  Detail & Related papers  (2021-12-12T06:11:16Z)
• Reinforcement Learning-powered Semantic Communication via Semantic Similarity [13.569045590522316]
  We introduce a new semantic communication mechanism, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. We show that the commonly used bit-level metrics are vulnerable of catching important semantic meaning and structures. We put forward a reinforcement learning (RL)-based solution which allows us to simultaneously optimize any user-defined semantic measurement.
  arXiv  Detail & Related papers  (2021-08-27T05:21:05Z)
• Learning to Communicate and Correct Pose Errors [75.03747122616605]
  We study the setting proposed in V2VNet, where nearby self-driving vehicles jointly perform object detection and motion forecasting in a cooperative manner. We propose a novel neural reasoning framework that learns to communicate, to estimate potential errors, and to reach a consensus about those errors.
  arXiv  Detail & Related papers  (2020-11-10T18:19:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.