Waveforms for Computing Over the Air: A groundbreaking approach that redefines data aggregation

Over-the-air computation (AirComp) leverages the signal superposition characteristic of wireless multiple-access channels (MACs) to perform mathematical computations. Initially introduced to enhance communication reliability in interference channels and wireless sensor networks (WSNs), AirComp has more recently found applications in task-oriented communications like wireless distributed learning and in wireless control systems. Its adoption aims to address latency challenges arising from an increased number of edge devices or Internet of Things (IoT) devices accessing the constrained wireless spectrum. This article is the first one to focus on the physical layer (PHY) of these systems. We present a unified framework, specifically on the waveform and the signal processing aspects at the transmitter and receiver, to meet the challenges that AirComp presents within the different contexts and use cases.

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Publication Details

Journal
IEEE Signal Processing Magazine
Published
2025-03-01
DOI
https://doi.org/10.1109/msp.2024.3500775
Citations
6
Primary Topic
Distributed Sensor Networks and Detection Algorithms
Type
article
Field-Weighted Citation Impact
8.92
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article

Waveforms for Computing Over the Air: A groundbreaking approach that redefines data aggregation

Alphan Şahin, Saeed Razavikia, Ana I. Pèrez-Neira, Marc Martinez-Gost et al.
6 citations
IEEE Signal Processing Magazine
Distributed Sensor Networks and Detection Algorithms
8.92
article

Waveforms for Computing Over the Air: A groundbreaking approach that redefines data aggregation

Alphan Şahin, Saeed Razavikia, Ana I. Pèrez-Neira, Marc Martinez-Gost, Carlo Fischione, Kaibin Huang
article en
6 citations

Abstract

Over-the-air computation (AirComp) leverages the signal superposition characteristic of wireless multiple-access channels (MACs) to perform mathematical computations. Initially introduced to enhance communication reliability in interference channels and wireless sensor networks (WSNs), AirComp has more recently found applications in task-oriented communications like wireless distributed learning and in wireless control systems. Its adoption aims to address latency challenges arising from an increased number of edge devices or Internet of Things (IoT) devices accessing the constrained wireless spectrum. This article is the first one to focus on the physical layer (PHY) of these systems. We present a unified framework, specifically on the waveform and the signal processing aspects at the transmitter and receiver, to meet the challenges that AirComp presents within the different contexts and use cases.

IEEE Signal Processing MagazineVol. 42(2)
University of South Carolina (US), KTH Royal Institute of Technology (SE), University of Hong Kong (HK), Universitat Politècnica de Catalunya (ES)
Climate action
Openalex Percentile: Top 2%
Distributed Sensor Networks and Detection Algorithms
8.92
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Waveforms for Computing Over the Air: A groundbreaking approach that redefines data aggregation — Alphan Şahin, Saeed Razavikia, et al. · IEEE Signal Processing Magazine (2025) | TGRS Research Map | TGRS