Papers
Topics
Authors
Recent
2000 character limit reached

Distributed Average Consensus via Noisy and Non-Coherent Over-the-Air Aggregation (2403.06920v1)

Published 11 Mar 2024 in eess.SP, cs.SY, and eess.SY

Abstract: Over-the-air aggregation has attracted widespread attention for its potential advantages in task-oriented applications, such as distributed sensing, learning, and consensus. In this paper, we develop a communication-efficient distributed average consensus protocol by utilizing over-the-air aggregation, which exploits the superposition property of wireless channels rather than combat it. Noisy channels and non-coherent transmission are taken into account, and only half-duplex transceivers are required. We prove that the system can achieve average consensus in mean square and even almost surely under the proposed protocol. Furthermore, we extend the analysis to the scenarios with time-varying topology. Numerical simulation shows the effectiveness of the proposed protocol.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (37)
  1. Wei Ren. Consensus strategies for cooperative control of vehicle formations. IET Control Theory & Applications, 1(2):505–512, 2007.
  2. Reza Olfati-Saber. Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Transactions on automatic control, 51(3):401–420, 2006.
  3. Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning. In Proceedings of the 50th IEEE Annual Allerton Conference on Communication, Control, and Computing, pages 1543–1550, 2012.
  4. Discrete-time average-consensus under switching network topologies. In 2006 American Control Conference, pages 6–pp, 2006.
  5. Consensus conditions of multi-agent systems with time-varying topologies and stochastic communication noises. IEEE Transactions on Automatic Control, 55(9):2043–2057, 2010.
  6. Consensus seeking over directed networks with limited information communication. Automatica, 49(2):610–618, 2013.
  7. Angelia Nedić and Ji Liu. On convergence rate of weighted-averaging dynamics for consensus problems. IEEE Transactions on Automatic Control, 62(2):766–781, 2016.
  8. Resilient consensus for time-varying networks of dynamic agents. In 2017 American control conference, pages 252–258, 2017.
  9. State consensus for discrete-time multiagent systems over time-varying graphs. IEEE Transactions on Automatic Control, 66(1):346–353, 2020.
  10. Event-triggered consensus control for discrete-time stochastic multi-agent systems: The input-to-state stability in probability. Automatica, 62:284–291, 2015.
  11. An overview of recent advances in event-triggered consensus of multiagent systems. IEEE transactions on cybernetics, 48(4):1110–1123, 2017.
  12. Sats: Secure average-consensus-based time synchronization in wireless sensor networks. IEEE Transactions on Signal Processing, 61(24):6387–6400, 2013.
  13. Consensus-based cooperative formation control with collision avoidance for a multi-uav system. In 2014 American Control Conference, pages 2077–2082, 2014.
  14. A comprehensive survey of pilot contamination in massive mimo—5g system. IEEE Communications Surveys & Tutorials, 18(2):905–923, 2015.
  15. Distributed consensus algorithms in sensor networks with imperfect communication: Link failures and channel noise. IEEE Transactions on Signal Processing, 57(1):355–369, 2008.
  16. Coordination and consensus of networked agents with noisy measurements: Stochastic algorithms and asymptotic behavior. SIAM Journal on Control and Optimization, 48(1):134–161, 2009.
  17. Distributed consensus algorithms in sensor networks: Quantized data and random link failures. IEEE Transactions on Signal Processing, 58(3):1383–1400, 2009.
  18. Quantized consensus by means of gossip algorithm. IEEE Transactions on Automatic Control, 57(1):19–32, 2011.
  19. Stochastic consensus over noisy networks with markovian and arbitrary switches. Automatica, 46(10):1571–1583, 2010.
  20. Mean square consensus of multi-agent systems over fading networks with directed graphs. Automatica, 95:503–510, 2018.
  21. Over-the-air computing for wireless data aggregation in massive iot. IEEE Wireless Communications, 28(4):57–65, 2021.
  22. Federated learning via over-the-air computation. IEEE Transactions on Wireless Communications, 19(3):2022–2035, 2020.
  23. Federated learning over wireless fading channels. IEEE Transactions on Wireless Communications, 19(5):3546–3557, 2020.
  24. One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis. IEEE Transactions on Wireless Communications, 20(3):2120–2135, 2020.
  25. Data and channel-adaptive sensor scheduling for federated edge learning via over-the-air gradient aggregation. IEEE Internet of Things Journal, 9(3):1640–1654, 2021.
  26. Learning rate optimization for federated learning exploiting over-the-air computation. IEEE Journal on Selected Areas in Communications, 39(12):3742–3756, 2021.
  27. Exploiting the superposition property of wireless communication for average consensus problems in multi-agent systems. In Proceedings of European Control Conference , pages 1766–1772, 2018.
  28. Max-consensus over fading wireless channels. IEEE Transactions on Control of Network Systems, 8(2):791–802, 2021.
  29. Over-the-air max-consensus in clustered networks adopting half-duplex communication technology. IEEE Transactions on Control of Network Systems, 2022.
  30. Nicolò Michelusi. Decentralized federated learning via non-coherent over-the-air consensus. arXiv preprint arXiv:2210.15806, 2022.
  31. Simon Haykin. Communication systems. John Wiley & Sons, 2008.
  32. Distributed projection subgradient algorithm over time-varying general unbalanced directed graphs. IEEE Transactions on Automatic Control, 64(3):1309–1316, 2018.
  33. Distributed convex optimization with inequality constraints over time-varying unbalanced digraphs. IEEE Transactions on Automatic Control, 63(12):4331–4337, 2018.
  34. Stochastic consensus seeking with noisy and directed inter-agent communication: Fixed and randomly varying topologies. IEEE Transactions on Automatic Control, 55(1):235–241, 2009.
  35. Boris Polyak. Introduction to Optimization. New York: Optimization Software, Inc., 1987.
  36. Rick Durrett. Probability: theory and examples, volume 49. Cambridge university press, 2019.
  37. A convergence theorem for non negative almost supermartingales and some applications. In Optimizing methods in statistics, pages 233–257. Elsevier, 1971.
Citations (1)

Summary

We haven't generated a summary for this paper yet.

Slide Deck Streamline Icon: https://streamlinehq.com

Whiteboard

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.