# Waveform Design for Over-the-Air Computing

arXiv: https://arxiv.org/abs/2405.20877 · HTML: https://mlsanity.com/paper/2405.20877/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 17 of 17)

- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis](https://mlsanity.com/paper/2001.05713/) (2020)
- [Optimized Power Control Design for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2106.09316/) (2021)
- [Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation](https://mlsanity.com/paper/2102.02946/) (2021)
- [Over-the-Air Computation Systems: Optimization, Analysis and Scaling Laws](https://mlsanity.com/paper/1909.00329/) (2019)
- [Joint Optimization of Communications and Federated Learning Over the Air](https://mlsanity.com/paper/2104.03490/) (2021)
- [Hybrid Beamforming for Massive MIMO Over-the-Air Computation](https://mlsanity.com/paper/2006.04560/) (2020)
- [Over-the-Air Computation via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2105.05113/) (2021)
- [Reduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networks](https://mlsanity.com/paper/1812.02373/) (2018)
- [Integrated Sensing, Communication, and Computation Over-the-Air: MIMO Beamforming Design](https://mlsanity.com/paper/2201.12581/) (2022)
- [ChannelComp: A General Method for Computation by Communications](https://mlsanity.com/paper/2310.06532/) (2023)
- [Over-the-Air Computation via Intelligent Reflecting Surfaces](https://mlsanity.com/paper/1904.12475/) (2019)
- [UAV Aided Over-the-Air Computation](https://mlsanity.com/paper/2106.00254/) (2021)
- [Accelerating Distributed Optimization via Over-the-Air Computing](https://mlsanity.com/paper/2212.14003/) (2022)
- [Optimal Receive Filter Design for Misaligned Over-the-Air Computation](https://mlsanity.com/paper/2309.16033/) (2023)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Exploiting the Superposition Property of Wireless Communication For Average Consensus Problems in Multi-Agent Systems](https://mlsanity.com/paper/1804.10418/) (2018)

## Abstract (arXiv)

In response to the increasing number of devices expected in next-generation networks, a shift to over-the-air (OTA) computing has been proposed. By leveraging the superposition of multiple access channels, OTA computing enables efficient resource management by supporting simultaneous uncoded transmission in the time and frequency domains. To advance the integration of OTA computing, our study presents a theoretical analysis that addresses practical issues encountered in current digital communication transceivers, such as transmitter synchronization (sync) errors and intersymbol interference (ISI). To this end, we investigate the theoretical mean squared error (MSE) for OTA transmission under sync errors and ISI, while also exploring methods for minimizing the MSE in OTA transmission. Using alternating optimization, we also derive optimal power policies for both the devices and the base station. In addition, we propose a novel deep neural network (DNN)-based approach to design waveforms that improve OTA transmission performance under sync errors and ISI. To ensure a fair comparison with existing waveforms such as raised cosine (RC) and better-than-raised-cosine (BTRC), we incorporate a custom loss function that integrates energy and bandwidth constraints along with practical design considerations such as waveform symmetry. Simulation results validate our theoretical analysis and demonstrate performance gains of the designed pulse over RC and BTRC waveforms. To facilitate testing of our results without the need to rebuild the DNN structure, we also provide curve-fitting parameters for the selected DNN-based waveforms.