Over-the-Air Computing for Wireless Data Aggregation in Massive IoT

Abstract

Wireless data aggregation (WDA), referring to aggregating data distributed at devices (e.g., sensors and smartphone), is a common operation in 5G-and-beyond machine-type communications to support Internet-of-Things (IoT), which lays the foundation for diversified applications such as distributed sensing, learning, and control. Conventional WDA techniques that are designed based on a separated-communication-and-computation principle encounter difficulty in accommodating the massive access under the limited radio resource and stringent latency constraints imposed by emerging applications (e.g, auto-driving). To address this issue, over-the-air computation (AirComp) is being developed as a new WDA solution by seamlessly integrating computation and communication. By exploiting the waveform superposition property of a multiple-access channel, AirComp turns the air into a computer for computing and communicating functions of distributed data at many devices, thereby allowing low-latency WDA over massive devices. In view of growing interests on AirComp, this article provides a timely overview of the technology by introducing basic principles, discussing advanced techniques and applications, and identifying promising research opportunities.

An Introductory paper to over-the-air computing

References in corpus (5)

• Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
• Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things
• Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
• On Analog Gradient Descent Learning over Multiple Access Fading Channels
• Massive Access for 5G and Beyond

Cited by in corpus (11)

• Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
• Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation
• A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
• Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers
• Deploying Federated Learning in Large-Scale Cellular Networks: Spatial Convergence Analysis
• Bayesian Over-The-Air Computation
• Over-the-Air Decentralized Federated Learning
• Over-the-Air Computation via Cloud Radio Access Networks
• Optimal Receive Beamforming for Over-the-Air Computation
• UAV Aided Over-the-Air Computation
• Integrated user scheduling and beam steering in over-the-air federated learning for mobile IoT