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

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.

Accepted for publication in Elsevier Computer Networks

References in corpus (18)

• Distilling the Knowledge in a Neural Network
• Measuring and testing dependence by correlation of distances
• Scaling Laws for Neural Language Models
• Bayesian Online Changepoint Detection
• Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
• Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ
• Rethinking Modern Communication from Semantic Coding to Semantic Communication
• A Survey on Collaborative DNN Inference for Edge Intelligence
• Supervised Compression for Resource-Constrained Edge Computing Systems
• Boundary-Aware Segmentation Network for Mobile and Web Applications
• Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AI
• Reinforcement Learning-powered Semantic Communication via Semantic Similarity
• Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence
• Constellation Design for Deep Joint Source-Channel Coding
• SC2 Benchmark: Supervised Compression for Split Computing
• LimitNet: Progressive, Content-Aware Image Offloading for Extremely Weak Devices & Networks
• BeamSense: Rethinking Wireless Sensing with MU-MIMO Wi-Fi Beamforming Feedback
• Adversarial Attacks to Latent Representations of Distributed Neural Networks in Split Computing