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Waveforms for Computing Over the Air (2405.17007v1)

Published 27 May 2024 in eess.SP

Abstract: 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, namely, for wireless distributed learning and in wireless control systems. Its adoption aims to address latency challenges arising from an increased number of edge devices or IoT devices accessing the constrained wireless spectrum. This paper focuses on the physical layer of these systems, 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.

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References (48)
  1. M. Goldenbaum, H. Boche, and S. Stanczak, “Harnessing interference for analog function computation in wireless sensor networks,” IEEE Transactions on Signal Processing, vol. 61, no. 20, pp. 4893–4906, 2013.
  2. B. Nazer and M. Gastpar, “Computation over multiple-access channels,” IEEE Transactions on Information Theory, vol. 53, no. 10, pp. 3498–3516, 2007.
  3. M. Gastpar, M. Vetterli, and P. Dragotti, “Sensing reality and communicating bits: a dangerous liaison,” IEEE Signal Processing Magazine, vol. 23, no. 4, pp. 70–83, 2006.
  4. D. Gunduz, Z. Qin, I. Estella, H. S. Dhillon, Z. Yang, A. Yener, K. K. Wong, and C.-B. Chae, “Beyond transmitting bits: Context, semantics, and task-oriented communications,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 5–41, 2023.
  5. A. Şahin and R. Yang, “A survey on over-the-air computation,” IEEE Communications Surveys & Tutorials, vol. 25, no. 3, pp. 1877–1908, 2023.
  6. Z. Wang, Y. Zhao, Y. Zhou, Y. Shi, C. Jiang, and K. B. Letaief, “Over-the-air computation: Foundations, technologies, and applications,” arXiv preprint arXiv:2210.10524, 2022.
  7. M. Dohler, R. Heath, A. Lozano, C. Papadias, and R. Valenzuela, “Is the phy layer dead?” IEEE Communications Magazine, vol. 49, no. 4, pp. 159–165, 2011.
  8. C.-T. Chu, S. Kim, Y.-A. Lin, Y. Yu, G. Bradski, K. Olukotun, and A. Ng, “Map-reduce for machine learning on multicore,” Advances in neural information processing systems, vol. 19, pp. 281–288, 2006.
  9. X. Wu, S. Zhang, and A. Ozgur, “Stac: Simultaneous transmitting and air computing in wireless data center networks,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 12, pp. 4024–4034, 2016.
  10. B. Song, C. Ding, A. T. Kamal, J. A. Farrell, and A. K. Roy-chowdhury, “Distributed camera networks,” IEEE Signal Processing Magazine, vol. 28, no. 3, pp. 20–31, 2011.
  11. Z. Liu, Q. Lan, A. E. Kalør, P. Popovski, and K. Huang, “Over-the-air view-pooling for low-latency distributed sensing,” in 2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2023, pp. 71–75.
  12. J. Lee, Y. Jang, H. Kim, S.-L. Kim, and S.-W. Ko, “Over-the-air consensus for distributed vehicle platooning control,” in ICC 2023 - IEEE International Conference on Communications, 2023, pp. 5965–5971.
  13. A. Şahin, “Wireless federated k𝑘kitalic_k-means clustering with non-coherent over-the-air computation,” in Proc. IEEE Military Communications Conference (MILCOM), 2023, pp. 339–344.
  14. R. Guirado, A. Rahimi, G. Karunaratne, E. Alarcon, A. Sebastian, and S. Abadal, “Whype: A scale-out architecture with wireless over-the-air majority for scalable in-memory hyperdimensional computing,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 13, no. 1, pp. 137–149, 2023.
  15. J.-J. Xiao, S. Cui, Z.-Q. Luo, and A. J. Goldsmith, “Linear coherent decentralized estimation,” IEEE Transactions on Signal Processing, vol. 56, no. 2, pp. 757–770, 2008.
  16. M. Goldenbaum, H. Boche, and S. Stanczak, “Nomographic functions: Efficient computation in clustered gaussian sensor networks,” IEEE Transactions on Wireless Communications, vol. 14, no. 4, pp. 2093–2105, 2015.
  17. M. Martinez-Gost, A. Pérez-Neira, and M. Á. Lagunas, “ENN: A neural network with DCT-adaptive activation functions,” IEEE Journal of Selected Topics in Signal Processing, 2023.
  18. G. Zhu, Y. Wang, and K. Huang, “Broadband analog aggregation for low-latency federated edge learning,” IEEE Transactions on Wireless Communications, vol. 19, no. 1, pp. 491–506, 2020.
  19. M. Goldenbaum and S. Stanczak, “Robust analog function computation via wireless multiple-access channels,” IEEE Transactions on Communications, vol. 61, no. 9, pp. 3863–3877, 2013.
  20. X. Cao, G. Zhu, J. Xu, and K. Huang, “Optimized power control for over-the-air computation in fading channels,” IEEE Transactions on Wireless Communications, vol. 19, no. 11, pp. 7498–7513, 2020.
  21. U. Premaratne, S. Warnakulasooriya, and R. Nandana, “Characterization of event-based sampling encoders for industrial internet of things using input–output mutual information,” IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5495–5505, 2021.
  22. X. Xie, C. Hua, J. Hong, and Y. Wei, “Joint design of coding and modulation for digital over-the-air computation,” arXiv preprint arXiv:2311.06829, 2023.
  23. G. Mergen and L. Tong, “Type based estimation over multiaccess channels,” IEEE Transactions on Signal Processing, vol. 54, no. 2, pp. 613–626, 2006.
  24. M. Martinez-Gost, A. Pérez-Neira, and M. Á. Lagunas, “LoRa-based over-the-air computing for Sat-IoT,” in 2023 31st European Signal Processing Conference (EUSIPCO), 2023, pp. 1514–1518.
  25. A. Şahin, “Over-the-air computation based on balanced number systems for federated edge learning,” IEEE Transactions on Wireless Communications, vol. 23, no. 5, pp. 4564–4579, 2024.
  26. M. Martinez-Gost, A. Pérez-Neira, and M. Á. Lagunas, “Log-FSK: A frequency modulation for over-the-air computing,” EUSIPCO, 2024.
  27. M. M. Gost, A. Pérez-Neira, and M. Á. Lagunas, “DCT-based air interface design for function computation,” IEEE Open Journal of Signal Processing, vol. 4, pp. 44–51, 2023.
  28. R. Guirado, A. Rahimi, G. Karunaratne, E. Alarcón, A. Sebastian, and S. Abadal, “Whype: A scale-out architecture with wireless over-the-air majority for scalable in-memory hyperdimensional computing,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 13, no. 1, pp. 137–149, 2023.
  29. S. Razavikia, J. M. B. Da Silva, and C. Fischione, “Channelcomp: A general method for computation by communications,” IEEE Transactions on Communications, pp. 1–1, 2023.
  30. N. Sidiropoulos, T. Davidson, and Z.-Q. Luo, “Transmit beamforming for physical-layer multicasting,” IEEE Transaction on Signal Processing, vol. 54, no. 6, pp. 2239–2251, 2006.
  31. L. Vandenberghe and S. Boyd, “Semidefinite programming,” SIAM review, vol. 38, no. 1, pp. 49–95, 1996.
  32. Z.-Q. Luo, W.-K. Ma, A. M.-C. So, Y. Ye, and S. Zhang, “Semidefinite relaxation of quadratic optimization problems,” IEEE Signal Processing Magazine, vol. 27, no. 3, pp. 20–34, 2010.
  33. S. Razavikia, J. M. B. D. S. Júnior, and C. Fischione, “Sumcomp: Coding for digital over-the-air computation via the ring of integers,” 2023.
  34. A. Şahin, “Distributed learning over a wireless network with non-coherent majority vote computation,” IEEE Transactions on Wireless Communications, vol. 22, no. 11, pp. 8020–8034, 2023.
  35. M. Martinez-Gost, A. Perez-Neira, and M.  . Lagunas, “Frequency modulation aggregation for federated learning,” in GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023, pp. 1878–1883.
  36. G. Zhu, Y. Du, D. Gunduz, and K. Huang, “One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis,” IEEE Transactions on Wireless Communications, vol. 20, no. 3, pp. 2120–2135, 2021.
  37. G. Zhu and K. Huang, “Mimo over-the-air computation for high-mobility multimodal sensing,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6089–6103, 2019.
  38. X. Fan, Y. Wang, Y. Huo, and Z. Tian, “BEV-SGD: Best effort voting SGD against byzantine attacks for analog aggregation based federated learning over the air,” IEEE Internet of Things Journal, pp. 1–14, 2022.
  39. S. Huang, Y. Zhou, T. Wang, and Y. Shi, “Byzantine-resilient federated machine learning via over-the-air computation,” in Proc. IEEE International Conference on Communications Workshops (ICC Workshops), 2021, pp. 1–6.
  40. S. Park and W. Choi, “Byzantine fault tolerant distributed stochastic gradient descent based on over-the-air computation,” IEEE Trans. Commun., pp. 1–15, 2022.
  41. M. Seif, R. Tandon, and M. Li, “Wireless federated learning with local differential privacy,” in Proc. IEEE International Symposium on Information Theory (ISIT), 2020, pp. 2604–2609.
  42. D. Liu and O. Simeone, “Privacy for free: Wireless federated learning via uncoded transmission with adaptive power control,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 1, pp. 170–185, 2021.
  43. H. Jeon, D. Hwang, J. Choi, H. Lee, and J. Ha, “Secure type-based multiple access,” IEEE Transactions on Information Forensics and Security, vol. 6, no. 3, pp. 763–774, 2011.
  44. M. Frey, I. Bjelakovic, and S. Stanczak, “Towards secure over-the-air computation,” in Proc. IEEE International Symposium on Information Theory (ISIT), 2021, pp. 700–705.
  45. R.-A. Stoica, O. Taghizadeh, and S. B. Mary Baskaran, “Secret computing over multiple access channels,” in 2022 56th Asilomar Conference on Signals, Systems, and Computers, 2022, pp. 559–563.
  46. Y. Blau and T. Michaeli, “The perception-distortion tradeoff,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 6228–6237.
  47. S. Li and S. Avestimehr, “Coded computing: Mitigating fundamental bottlenecks in large-scale distributed computing and machine learning,” Foundations and Trends in Communications and Information Theory, vol. 17, no. 1, pp. 1–148, 2020.
  48. M. Soleymani, “Analog coding: Theory and applications,” PhD thesis, University of Michigan, Michigan, MI, 2022.
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