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

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.

[1]  Kaibin Huang,et al.  Cooperative Interference Management for Over-the-Air Computation Networks , 2020, IEEE Transactions on Wireless Communications.

[2]  Mehdi Bennis,et al.  Wireless Network Intelligence at the Edge , 2018, Proceedings of the IEEE.

[3]  Osvaldo Simeone,et al.  Privacy for Free: Wireless Federated Learning via Uncoded Transmission With Adaptive Power Control , 2020, IEEE Journal on Selected Areas in Communications.

[4]  Kaibin Huang,et al.  Optimal Power Control for Over-the-Air Computation , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[5]  Zhi Ding,et al.  Federated Learning via Over-the-Air Computation , 2018, IEEE Transactions on Wireless Communications.

[6]  Wei Yu,et al.  Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things , 2018, IEEE Signal Processing Magazine.

[7]  Jeffrey G. Andrews,et al.  Fundamentals of Lte , 2010 .

[8]  Deniz Gündüz,et al.  One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis , 2020, IEEE Transactions on Wireless Communications.

[9]  Michael Gastpar,et al.  Computation Over Multiple-Access Channels , 2007, IEEE Transactions on Information Theory.

[10]  Kaibin Huang,et al.  MIMO Over-the-Air Computation for High-Mobility Multimodal Sensing , 2018, IEEE Internet of Things Journal.

[11]  Erik G. Larsson,et al.  Massive Access for 5G and Beyond , 2020, IEEE Journal on Selected Areas in Communications.

[12]  Wei Chen,et al.  The Roadmap to 6G: AI Empowered Wireless Networks , 2019, IEEE Communications Magazine.

[13]  R. Buck,et al.  Approximate complexity and functional representation , 1979 .

[14]  Mohammad Mohammadi Amiri,et al.  Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air , 2020 .

[15]  Slawomir Stanczak,et al.  Exploiting the Superposition Property of Wireless Communication For Average Consensus Problems in Multi-Agent Systems , 2018, 2018 European Control Conference (ECC).