Over-the-Air Computation for 6G: Foundations, Technologies, and Applications
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Abstract (arXiv)
The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation. However, the scarce radio resources and stringent latency requirement make it challenging to meet these demands. To tackle these challenges, over-the-air computation (AirComp) emerges as a potential technology. Specifically, AirComp seamlessly integrates the communication and computation procedures through the superposition property of multiple-access channels, which yields a revolutionary multiple-access paradigm shift from "compute-after-communicate" to "compute-when-communicate". By this means, AirComp enables spectral-efficient and low-latency wireless data aggregation by allowing multiple devices to occupy the same channel for transmission. In this paper, we aim to present the recent advancement of AirComp in terms of foundations, technologies, and applications. The mathematical form and communication design are introduced as the foundations of AirComp, and the critical issues of AirComp over different network architectures are then discussed along with the review of existing literature. The technologies employed for the analysis and optimization on AirComp are reviewed from the information theory and signal processing perspectives. Moreover, we present the existing studies that tackle the practical implementation issues in AirComp systems, and elaborate the applications of AirComp in Internet of Things and edge intelligent networks. Finally, potential research directions are highlighted to motivate the future development of AirComp.
Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). First posted on arXiv 19 Oct 2022.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 4; 2026: 3.
12 papers.
- 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
- 2023Over-the-Air Federated Learning and Optimizationcited by 10
- 2025Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networkscited by 7
- 2024Private Collaborative Edge Inference via Over-the-Air Computationcited by 5
- 2025Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologiescited by 4
- 2026Metasurfaces-Integrated Wireless Neural Networks for Lightweight Over-The-Air Edge Inferencecited by 2
- 2023Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioningcited by 1
- 2025RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimizationcited by 0
- 2025Over-the-Air Goal-Oriented Communicationscited by 0
- 2026Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmapcited by 0
- 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learningcited by 0
- 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning8 May 2026
- 2026Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap13 Mar 2026
- 2026Metasurfaces-Integrated Wireless Neural Networks for Lightweight Over-The-Air Edge Inference22 Feb 2026
- 2025Over-the-Air Goal-Oriented Communications23 Dec 2025
- 2025RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization21 Dec 2025
- 2025Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies2 Apr 2025
- 2025Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networks31 Mar 2025
- 2024Private Collaborative Edge Inference via Over-the-Air Computation30 Jul 2024
- 2023Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioning27 Oct 2023
- 2023Over-the-Air Federated Learning and Optimization16 Oct 2023
- 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networks3 Jan 2023
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directions24 Oct 2022