## 1 Introduction
In NCSs, data loss occurs when buffer overflows, router congestion, or connection interruptions happen at network nodes. Packet loss is unpredictable, leading to a lack of data integrity and adverse effects on the system. The parameter identification of network control systems relies on the observation of output data, which, being transmitted over a network, inevitably introduces elements of communication uncertainty. In NCSs, packet loss is typically addressed through three main approaches. The first approach employs stochastic systems and statistical principles, where the packet loss is mathematically described either by using the average packet loss probability or by assuming that the packet loss adheres to a specific probability distribution. The second approach involves a design method based on time-lag systems, which is applied under conditions where the total extent of the packet loss between two sampling moments is known. The third approach transforms the periods during which packet loss occurs into interruptions in the corresponding transmission channel connections. This is integrated with the theory of motion switching systems, thereby treating NCSs experiencing packet loss as if they were dynamic systems with switching functionalities [19; 20; 21]. Significant advancements have been made in research focused on control and estimation under conditions of uncertain communication. [PERSON] et al. [22] primarily investigated the fundamental issues of linear quadratic Gaussian control in NCSs with unreliable communication channels, as well as the stability of such systems under conditions of input delays, packet loss, and observation delays. [PERSON] et al. [23] explored the distributed fusion estimation of multi-sensor outputs under uncertain network conditions, introducing a sequence of Bernoulli random variables to describe observations for updating estimates at each sam pling time, and they used predictions to compensate for information loss due to packet loss. [PERSON] et al. [24] emphasized the significance of packet loss information in NCSs for enhancing control performance and identifying communication failures or attacks, proposing two methods: one involving recursive filters based on NCSs input-output models for packet loss estimation and Kalman filtering for state estimation, and the other using state space models of NCS for simultaneous estimation of controlled object states and packet loss scenarios. [PERSON] et al. [25] investigated hierarchical fusion estimation in multi-sensor networks, employing an asynchronous sampling scheme to improve estimation performance under data packet loss and transmission delays. [PERSON] et al. [26] created a new model for describing random transmission delays and packet loss in communication channels, introducing an optimal linear filter for estimation based on this model. Quantization identification, event-triggered mechanisms, and system control and estimation under communication uncertainty have made significant progress. In most existing studies, uncertain channels and event-triggered mechanisms are typically applied in the control domain, yet are rarely discussed in system identification. This paper investigates FIR system identification under the combined impact of these three factors. The advantages of FIR systems in complex systems and intelligent algorithm applications are evident. With appropriate design and application, FIR systems can process and analyze complex data structures and system behaviors [27; 28; 29]. This makes them ideal for many signal-processing and system-identification applications. Packet loss greatly impacts the received data sequence, and the quantization process coupled with event-triggered mechanisms further reduces the amount of information in the data, creating a highly nonlinear interaction that poses great challenges for system parameter identification.