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Graph Based Semantic Encoder Decoder Framework for Task Oriented Communications in Connected Autonomous Vehicles

Soheyb Ribouh, Phil Polo Ditsia Di Ngoma

TL;DR

This work proposes a Graph-Based Semantic Encoder-Decoder (GBSED) architecture tailored for task-oriented communications in CAV networks, which enables a significant reduction in communication overhead while maintaining high semantic fidelity, exceeding 0.9 at SNR levels above 10dB, for downstream vehicular tasks.

Abstract

Connected autonomous vehicles (CAVs) require reliable and efficient communication frameworks to support safety critical and task-oriented applications such as collision avoidance, cooperative perception, and traffic risk assessment. Traditional communication paradigms, which focus on transmitting raw bits, often incur excessive bandwidth consumption and fail to preserve the semantic relevance of transmitted information. To bridge this gap, we propose a Graph-Based Semantic Encoder-Decoder (GBSED) architecture tailored for task-oriented communications in CAV networks. The encoder leverages scene graphs to capture spatial and semantic relationships among road entities, combined with a semantic compression algorithm that reduces the size of the extracted graph based representations by up to 99% compared to raw images, while the decoder reconstructs task relevant representations rather than raw data. This design enables a significant reduction in communication overhead while maintaining high semantic fidelity, exceeding 0.9 at SNR levels above 10dB, for downstream vehicular tasks. We evaluate the proposed framework through simulations in autonomous driving scenarios, where the semantic encoder and decoder are integrated into a MIMO OFDM physical layer system. The results demonstrate high prediction success rates for risk assessment, improved robustness under the 3GPP CDL channel, and significant compression gains, confirming that the proposed semantic communication framework is a promising solution for future 6G systems.

Graph Based Semantic Encoder Decoder Framework for Task Oriented Communications in Connected Autonomous Vehicles

TL;DR

This work proposes a Graph-Based Semantic Encoder-Decoder (GBSED) architecture tailored for task-oriented communications in CAV networks, which enables a significant reduction in communication overhead while maintaining high semantic fidelity, exceeding 0.9 at SNR levels above 10dB, for downstream vehicular tasks.

Abstract

Connected autonomous vehicles (CAVs) require reliable and efficient communication frameworks to support safety critical and task-oriented applications such as collision avoidance, cooperative perception, and traffic risk assessment. Traditional communication paradigms, which focus on transmitting raw bits, often incur excessive bandwidth consumption and fail to preserve the semantic relevance of transmitted information. To bridge this gap, we propose a Graph-Based Semantic Encoder-Decoder (GBSED) architecture tailored for task-oriented communications in CAV networks. The encoder leverages scene graphs to capture spatial and semantic relationships among road entities, combined with a semantic compression algorithm that reduces the size of the extracted graph based representations by up to 99% compared to raw images, while the decoder reconstructs task relevant representations rather than raw data. This design enables a significant reduction in communication overhead while maintaining high semantic fidelity, exceeding 0.9 at SNR levels above 10dB, for downstream vehicular tasks. We evaluate the proposed framework through simulations in autonomous driving scenarios, where the semantic encoder and decoder are integrated into a MIMO OFDM physical layer system. The results demonstrate high prediction success rates for risk assessment, improved robustness under the 3GPP CDL channel, and significant compression gains, confirming that the proposed semantic communication framework is a promising solution for future 6G systems.
Paper Structure (19 sections, 4 equations, 4 figures, 1 table, 3 algorithms)

This paper contains 19 sections, 4 equations, 4 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. System model
  3. Semantic encoder
  4. Scene graph extraction
  5. Scene graph encoding
  6. semantic compression
  7. Semantic decoder
  8. Semantic Decompression
  9. Adjacency Tensor to Scene Graph
  10. Task predilection
  11. Experimental Setup
  12. Dataset
  13. Training Configuration
  14. Communication scenario
  15. Results and Discussion
  16. ...and 4 more sections

Figures (4)

  • Figure 1: The proposed Graph-Based Semantic Encoder–Decoder (GBSED) architecture.
  • Figure 2: Semantic fidelity as a function of the Signal-to-Noise Ratio (SNR).
  • Figure 3: Risk Assessment Model Performance (1043-syn Dataset Yu)
  • Figure 4: Performance metrics of the proposed model as a function of the Signal-to-Noise Ratio (SNR). The plots show the model's accuracy, precision, recall, F1-score, Area Under the Curve (AUC), and Matthews Correlation Coefficient (MCC).