Over-the-Air Computation with DFT-spread OFDM for Federated Edge Learning
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Abstract (arXiv)
In this study, we propose an over-the-air computation (AirComp) scheme for federated edge learning (FEEL) without channel state information (CSI) at the edge devices (EDs) or the edge server (ES). The proposed scheme relies on non-coherent communication techniques for achieving distributed training by majority vote (MV). In this work, the votes, i.e., the signs of the local gradients, from the EDs are represented with the pulse-position modulation (PPM) symbols constructed with discrete Fourier transform (DFT)-spread orthogonal frequency division multiplexing (OFDM) (DFT-s-OFDM). By taking the delay spread and time-synchronization errors into account, the MV at the ES is obtained with an energy detector. Hence, the proposed scheme does not require CSI at the EDs and ES. We also prove the convergence of the distributed training when the MV is obtained with the proposed scheme under fading channel. Through simulations, we show that the proposed scheme provides a high test accuracy in fading channels while resulting in lower peak-to-mean envelope power ratio (PMEPR) symbols.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Dec 2021.
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Citing papers per year: 2022: 4; 2023: 2; 2024: 0; 2025: 2; 2026: 1.
9 papers.
- 2022A Survey on Over-the-Air Computationcited by 19
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
- 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learningcited by 9
- 2022Over-the-Air Computation over Balanced Numeralscited by 4
- 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
- 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systemscited by 1
- 2023Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computationcited by 0
- 2025OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centerscited by 0
- 2026SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillationcited by 0
- 2026SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillation17 Feb 2026
- 2025OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centers30 Dec 2025
- 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing3 Dec 2025
- 2023Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation5 Aug 2023
- 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systems4 Feb 2023
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directions24 Oct 2022
- 2022A Survey on Over-the-Air Computation20 Oct 2022
- 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning13 Oct 2022
- 2022Over-the-Air Computation over Balanced Numerals22 Sep 2022