Semantic HARQ for Intelligent Transportation Systems: Joint Source-Channel Coding-Powered Reliable Retransmissions

2025-04-20

AI Summary

To address unreliable semantic transmission in intelligent transportation systems (ITS) caused by high vehicle mobility, time-varying channels, and dense vehicular networks, this paper proposes a robust semantic communication framework integrating hybrid automatic repeat request (HARQ) with joint source-channel coding (JSCC). We innovatively design three semantic HARQ mechanisms—Type-I, weighted fusion, and synonym fusion—and introduce a generative signal reconstructor augmented with a local knowledge base to enable channel error detection and semantic-level error correction. Furthermore, the framework incorporates conditional generative networks, discriminative channel-aware modules, and semantic feature/decision-level fusion techniques. Experimental results under representative ITS scenarios demonstrate that the proposed method reduces semantic frame error rate by over 40% compared to conventional HARQ schemes and achieves a 2.3× improvement in spectral efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication
• Intelligent Robots: State Estimation

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: Assisted, interactive, and conversational search
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

The surge of data traffic in Intelligent Transportation Systems (ITS) places a significant challenge on limited wireless resources. Semantic communication, which transmits essential semantics of the raw data, offers a promising solution by reducing redundancy and improving spectrum efficiency. However, high vehicle mobility, dynamic channel conditions, and dense vehicular networks severely impact transmission reliability in ITS. To address these limitations, we integrate Hybrid Automatic Repeat reQuest (HARQ) with Joint Source-Channel Coding (JSCC) to provide reliable semantic communications for ITS. To counteract the adverse effects of time-varying fading channels and noise, we propose a generative signal reconstructor module supported by a local knowledge base, which employs a discriminator for channel error detection and a conditional generative network for error correction. We propose three innovative semantic HARQ (sem-HARQ) schemes, Type I sem-HARQ (sem-HARQ-I), sem-HARQ with weighted combining (sem-HARQ-WC), and sem-HARQ with synonymous combining (sem-HARQ-SC) to enable reliable JSCC-based semantic communications. At the transmitter, both sem-HARQ-I and sem-HARQ-WC retransmit the same semantic signals, while sem-HARQ-SC introduces redundant semantics across different HARQ rounds through synonymous mapping. At the receiver, sem-HARQ-I performs semantic decoding based solely on the currently received signal. In contrast, sem-HARQ-WC enhances reliability by fusing the current received semantic signal with prior erroneous signals at the feature or decision level, thereby exploiting semantic information from failed HARQ rounds. Similarly, sem-HARQ-SC employs feature-level combining, leveraging incremental semantic redundancy to merge semantic features from retransmissions.

Problem

• Enhancing ITS reliability via semantic HARQ schemes
• Combating fading channels with generative error correction
• Optimizing retransmissions using joint source-channel coding

Innovation

• Integrates HARQ with JSCC for reliable semantic communications
• Uses generative signal reconstructor with local knowledge base
• Proposes three semantic HARQ schemes for enhanced reliability