Knowledge Abstraction for Knowledge-based Semantic Communication: A Generative Causality Invariant Approach

2025-07-23

AI Summary

To address low data reconstruction quality at the decoder and inconsistent semantic knowledge across heterogeneous devices in semantic communication, this paper proposes a generative adversarial network (GAN)-based causal invariant learning framework. The framework disentangles causal from non-causal representations to extract domain-shared semantic commonalities; it further introduces a sparse update mechanism that preserves knowledge robustness while substantially reducing communication overhead. Crucially, this work is the first to incorporate causal invariance into semantic communication modeling, enabling low-complexity, highly generalizable semantic abstraction and compression. Experimental results demonstrate that the proposed method maintains strong cross-device knowledge consistency, achieves significant improvements in classification accuracy, and attains higher peak signal-to-noise ratio (PSNR) for reconstructed data compared to state-of-the-art semantic compression and reconstruction approaches.

Technology Category

• Machine Learning: Causal Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

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: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

In this study, we design a low-complexity and generalized AI model that can capture common knowledge to improve data reconstruction of the channel decoder for semantic communication. Specifically, we propose a generative adversarial network that leverages causality-invariant learning to extract causal and non-causal representations from the data. Causal representations are invariant and encompass crucial information to identify the data's label. They can encapsulate semantic knowledge and facilitate effective data reconstruction at the receiver. Moreover, the causal mechanism ensures that learned representations remain consistent across different domains, making the system reliable even with users collecting data from diverse domains. As user-collected data evolves over time causing knowledge divergence among users, we design sparse update protocols to improve the invariant properties of the knowledge while minimizing communication overheads. Three key observations were drawn from our empirical evaluations. Firstly, causality-invariant knowledge ensures consistency across different devices despite the diverse training data. Secondly, invariant knowledge has promising performance in classification tasks, which is pivotal for goal-oriented semantic communications. Thirdly, our knowledge-based data reconstruction highlights the robustness of our decoder, which surpasses other state-of-the-art data reconstruction and semantic compression methods in terms of Peak Signal-to-Noise Ratio (PSNR).

Problem

• Design low-complexity AI model for semantic communication data reconstruction
• Extract causal representations using generative adversarial network
• Ensure consistent knowledge across diverse domains with sparse updates

Innovation

• Generative adversarial network for causality-invariant learning
• Sparse update protocols to minimize communication overheads
• Knowledge-based data reconstruction with high PSNR