Quantized Distributed Estimation with Event-triggered Communication and Packet Loss

TL;DR

This paper presents a novel quantized distributed estimation algorithm that effectively handles event-triggered communication and packet loss, reducing communication bits while ensuring convergence and revealing trade-offs between communication rate and convergence speed.

Contribution

It introduces a one-bit information reconstruction method for packet loss, establishes convergence properties, and analyzes the trade-off between communication rate and convergence.

Findings

Global average communication bit-rate decreases to zero over time

Convergence and convergence rate are theoretically established

Numerical example validates the proposed algorithm's effectiveness

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.