Semantic Communication for 6G Networks: A Trade-off between Distortion Criticality and Information Representability

TL;DR

This paper introduces a self-attention based conditional GAN framework for 6G semantic communication, balancing distortion criticality and information representability to improve semantic robustness and adaptive transmission.

Contribution

It proposes a novel SA-cGAN model that jointly considers semantic importance, distortion, and channel quality, integrating a knowledge graph for enhanced semantic robustness in 6G networks.

Findings

Outperforms conventional schemes in semantic metrics at various SNR levels.

Achieves semantic similarity, accuracy, and completeness above 0.90 with increasing SNR.

Demonstrates adaptive compression by reducing redundancy while preserving critical semantics.

Abstract

In this work, a self-attention based conditional generative adversarial network (SA-cGAN) framework for the sixth generation (6G) semantic communication system is proposed, explicitly designed to balance the trade-off between distortion criticality and information representability under varying channel conditions. The proposed SA-cGAN model continuously learns compact semantic representations by jointly considering semantic importance, reconstruction distortion, and channel quality, enabling adaptive selection of semantic tokens for transmission. A knowledge graph is integrated to preserve contextual relationships and enhance semantic robustness, particularly in low signal-to-noise ratio (SNR) regimes. The resulting optimization framework incorporates continuous relaxation, submodular semantic selection, and principled constraint handling, allowing efficient semantic resource allocation under bandwidth and multi-constraint conditions. Simulation results show that, although SA-cGAN achieves modest syntactic bilingual evaluation understudy scores at low SNR to approximately 0.72 at 20 dB, it significantly outperforms conventional and JSCC-based schemes in semantic metrics, with semantic similarity, semantic accuracy, and semantic completeness consistently improving above 0.90 with SNR. Additionally, the model exhibits adaptive compression behavior, aggressively reducing redundant content while preserving critical semantic information to maintain fidelity. The convergence of training loss further validates stable and efficient learning of semantic representations. Overall, the results confirm that the proposed SA-cGAN model effectively captures distortion-invariant semantic representations and dynamically adapts transmitted content based on distortion criticality and information representability for meaning-centric communication in future 6G networks.