# Waveforms for Computing Over the Air

arXiv: https://arxiv.org/abs/2405.17007 · HTML: https://mlsanity.com/paper/2405.17007/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2024.

## Built on by (most cited first, 6 of 6)

- [Private Collaborative Edge Inference via Over-the-Air Computation](https://mlsanity.com/paper/2407.21151/) (2024)
- [Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing](https://mlsanity.com/paper/2512.03719/) (2025)
- [VecComp: Vector Computing via MIMO Digital Over-the-Air Computation](https://mlsanity.com/paper/2511.02765/) (2025)
- [Robust Over-the-Air Computation with Type-Based Multiple Access](https://mlsanity.com/paper/2502.19014/) (2025)
- [Out-of-Air Computation: Enabling Structured Function Extraction from Wireless Superposition](https://mlsanity.com/paper/2604.04312/) (2026)
- [Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning](https://mlsanity.com/paper/2605.07263/) (2026)

## Abstract (arXiv)

Over-the-air computation (AirComp) leverages the signal-superposition characteristic of wireless multiple access channels to perform mathematical computations. Initially introduced to enhance communication reliability in interference channels and wireless sensor networks, 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 paper is the first one to focus on the physical layer 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.