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Waveform Design for Over-the-Air Computing

arXiv 2405.20877 ↗First posted 31 May 2024cs.IT cs.DC cs.LGcited by 0

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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.

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.

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17 of 34 references.

References · 17

  1. 2018Federated Learning via Over-the-Air Computationcited by 219
  2. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  3. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 48
  4. 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 29
  5. 2019Over-the-Air Computation Systems: Optimization, Analysis and Scaling Lawscited by 27
  6. 2021Joint Optimization of Communications and Federated Learning Over the Aircited by 14
  7. 2020Hybrid Beamforming for Massive MIMO Over-the-Air Computationcited by 13
  8. 2021Over-the-Air Computation via Reconfigurable Intelligent Surfacecited by 13
  9. 2018Reduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networkscited by 11
  10. 2022Integrated Sensing, Communication, and Computation Over-the-Air: MIMO Beamforming Designcited by 11
  11. 2023ChannelComp: A General Method for Computation by Communicationscited by 10
  12. 2019Over-the-Air Computation via Intelligent Reflecting Surfacescited by 5
5 more
  1. 2021UAV Aided Over-the-Air Computationcited by 5
  2. 2022Accelerating Distributed Optimization via Over-the-Air Computingcited by 4
  3. 2023Optimal Receive Filter Design for Misaligned Over-the-Air Computationcited by 4
  4. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
  5. 2018Exploiting the Superposition Property of Wireless Communication For Average Consensus Problems in Multi-Agent Systemscited by 2

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  1. 2025Channel-Aware Constellation Design for Digital OTA Computation
  2. 2025Learned Digital Over-the-Air Computing for Federated Edge Learning
  3. 2025Learned Digital Codes for Over-the-Air Computation in Federated Edge Learning
  4. 2025On Designing Modulation for Over-the-Air Computation -- Part I: Noise-Aware Design
  5. 2024Biased Over-the-Air Federated Learning under Wireless Heterogeneity
  6. 2025Scalable Transceiver Design for Multi-User Communication in FDD Massive MIMO Systems via Deep Learning
  7. 2022Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control
  8. 2026The Computing Channel: How Modulation Programs the Airwaves ↗