Semantic Communications for Vehicle-Based Mission-Critical Services: Challenges and Solutions

TL;DR

This paper proposes a semantic communication framework tailored for vehicle-based mission-critical services, addressing reliability and latency challenges in 6G networks, and validates it through a UAV congestion relief case study using explainable AI.

Contribution

It introduces a systematic SemCom framework specifically designed for vehicle-based MC services, filling a gap in existing research and addressing unique service challenges.

Findings

The proposed SemCom framework effectively enhances communication reliability for vehicle MC services.

Case study demonstrates improved congestion relief using the framework with XAI validation.

Addresses key challenges of latency and reliability in 6G vehicle communications.

Abstract

As mission-critical (MC) services such as Unmanned Aerial Vehicles (UAVs) based emergency communication and Internet of Vehicles (IoVs) enabled autonomous driving emerge, the traditional communication framework can not meet the growing demands for higher reliability and lower latency and the increasing transmission loads. Semantic Communication (SemCom), an emerging communication paradigm that shifts the focus from bit-level data to its context and intended task at the receiver (i.e., semantic level), is envisioned to be a key revolution in Sixth Generation (6G) networks. However, an explicit and systematic SemCom framework specifically tailored for Vehicle-based MC (VbMC) services has yet to be proposed, primarily due to the complexity and lack of analysis on their MC characteristics. In this article, we first present the key information-critical and infrastructure-critical vehicle-based services within the SemCom framework. We then analyze the unique characteristics of MC services and the corresponding challenges they present for SemCom. Building on this, we propose a novel SemCom framework designed to address the specific needs of MC services in vehicle systems, offering potential solutions to existing challenges. Finally, we present a case study on UAV-based rapid congestion relief, utilizing eXplainable AI (XAI) to validate the effectiveness of the proposed SemCom framework.