ChannelComp: A General Method for Computation by Communications
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Abstract (arXiv)
Over-the-air computation (AirComp) is a well-known technique by which several wireless devices transmit by analog amplitude modulation to achieve a sum of their transmit signals at a common receiver. The underlying physical principle is the superposition property of the radio waves. Since such superposition is analog and in amplitude, it is natural that AirComp uses analog amplitude modulations. Unfortunately, this is impractical because most wireless devices today use digital modulations. It would be highly desirable to use digital communications because of their numerous benefits, such as error correction, synchronization, acquisition of channel state information, and widespread use. However, when we use digital modulations for AirComp, a general belief is that the superposition property of the radio waves returns a meaningless overlapping of the digital signals. In this paper, we break through such beliefs and propose an entirely new digital channel computing method named ChannelComp, which can use digital as well as analog modulations. We propose a feasibility optimization problem that ascertains the optimal modulation for computing arbitrary functions over-the-air. Additionally, we propose pre-coders to adapt existing digital modulation schemes for computing the function over the multiple access channel. The simulation results verify the superior performance of ChannelComp compared to AirComp, particularly for the product functions, with more than 10 dB improvement of the computation error.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Oct 2023.
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Citing papers per year: 2023: 1; 2024: 4; 2025: 3; 2026: 2.
10 papers.
- 2023Blind Federated Learning via Over-the-Air q-QAMcited by 8
- 2024Private Collaborative Edge Inference via Over-the-Air Computationcited by 5
- 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
- 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
- 2024Scalable and Reliable Over-the-Air Federated Edge Learningcited by 1
- 2025VecComp: Vector Computing via MIMO Digital Over-the-Air Computationcited by 1
- 2024Waveform Design for Over-the-Air Computingcited by 0
- 2025Learned Digital Codes for Over-the-Air Computation in Federated Edge Learningcited by 0
- 2026MD-AirComp+: Adaptive Quantization for Blind Massive Digital Over-the-Air Computationcited by 0
- 2026Out-of-Air Computation: Enabling Structured Function Extraction from Wireless Superpositioncited by 0
- 2026Out-of-Air Computation: Enabling Structured Function Extraction from Wireless Superposition5 Apr 2026
- 2026MD-AirComp+: Adaptive Quantization for Blind Massive Digital Over-the-Air Computation20 Feb 2026
- 2025Learned Digital Codes for Over-the-Air Computation in Federated Edge Learning22 Dec 2025
- 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing3 Dec 2025
- 2025VecComp: Vector Computing via MIMO Digital Over-the-Air Computation4 Nov 2025
- 2024Private Collaborative Edge Inference via Over-the-Air Computation30 Jul 2024
- 2024Scalable and Reliable Over-the-Air Federated Edge Learning16 Jul 2024
- 2024Waveform Design for Over-the-Air Computing31 May 2024
- 2024Multiple Access in the Era of Distributed Computing and Edge Intelligence26 Feb 2024
- 2023Blind Federated Learning via Over-the-Air q-QAM7 Nov 2023