Quantized Distributed Estimation with Event-triggered Communication and Packet Loss
Abstract
This paper focuses on the problem of quantized distributed estimation with event-triggered communication and packet loss, aiming to reduce the number of transmitted bits. The main challenge lies in the inability to differentiate between an untriggered event and a packet loss occurrence. This paper proposes an event-triggered distributed estimation algorithm with quantized communication and quantized measurement, in which it introduces a one-bit information reconstruction method to deal with packet loss. The almost sure convergence and convergence rate of the proposed algorithm are established. Besides, it is demonstrated that the global average communication bit-rate decreases to zero over time. Moreover, the trade-off between communication rate and convergence rate is revealed, providing guidance for designing the communication rate required to achieve the algorithm's convergence rate. A numerical example is supplied to validate the findings.
Cite
@article{arxiv.2511.21906,
title = {Quantized Distributed Estimation with Event-triggered Communication and Packet Loss},
author = {Ying Wang and Yanlong Zhao and Ji-Feng Zhang and Karl Henrik Johansson},
journal= {arXiv preprint arXiv:2511.21906},
year = {2025}
}
Comments
6 page,4 figures, accepted by 64rd IEEE Conference on Decision and Control