2024/11/27 by Milin Zhang, Mohammad Abdi, Zhang, Milin +5 · 5 citations
Computer Science · Engineering · #Artificial intelligence #Big Data and Digital Economy #Computer science #Data science #Enhanced Data Rates for GSM Evolution #FOS: Computer and information sciences #FOS: Electrical engineering #Information retrieval #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Robotics and Automated Systems #Semantic Web #Semantic computing #Semantic network #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2411.18199
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/11/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.