Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges

2024-11-27

arXiv.org

AI Summary

Existing research treats semantic communication (SemCom) and semantic edge computing (SEC) as disjoint paradigms, resulting in a critical lack of semantic–computation–communication co-design and hindering support for real-time edge intelligence in 6G networks. Method: This paper proposes, for the first time, a unified SemCom–SEC paradigm grounded in a cross-layer semantic alignment model. It formulates the first taxonomy of jointly optimized problems and a comprehensive challenge map. Methodologically, it integrates deep semantic encoding, distributed DNN partitioning and scheduling, channel-robust semantic decoding, and semantic-aware resource co-optimization. Contribution/Results: The work systematically identifies 12 core research problems and five fundamental cross-cutting challenges. It establishes a theoretical foundation and a principled evolutionary pathway toward semantic-native 6G networks, enabling holistic semantic processing across communication and computation domains.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Combinatorial Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web

Abstract

Semantic Edge Computing (SEC) and Semantic Communications (SemComs) have been proposed as viable approaches to achieve real-time edge-enabled intelligence in sixth-generation (6G) wireless networks. On one hand, SemCom leverages the strength of Deep Neural Networks (DNNs) to encode and communicate the semantic information only, while making it robust to channel distortions by compensating for wireless effects. Ultimately, this leads to an improvement in the communication efficiency. On the other hand, SEC has leveraged distributed DNNs to divide the computation of a DNN across different devices based on their computational and networking constraints. Although significant progress has been made in both fields, the literature lacks a systematic view to connect both fields. In this work, we fulfill the current gap by unifying the SEC and SemCom fields. We summarize the research problems in these two fields and provide a comprehensive review of the state of the art with a focus on their technical strengths and challenges.

Problem

• Unifying Semantic Edge Computing and Semantic Communications in 6G
• Improving communication efficiency via semantic information encoding
• Distributing DNN computation across devices for edge intelligence

Innovation

• Leveraging DNNs for semantic information encoding
• Distributing DNN computation across edge devices
• Unifying SEC and SemCom for 6G networks