a stochastic process, channel characteristics are typically described using probability distribution models that can adapt to measured data in real-world environments. To model the channel, the usual approach is to estimate specific model parameters from the received measurement data and then evaluate the fit between the data and the model to determine the channel characteristics. Furthermore, the time-varying nature of channels in mobile communication necessitates that the channel estimation process controls computational delay to effectively reduce the real-time impact of measurement cycles on channel evaluation. Therefore, determining the probability distribution model depends not only on the sample size but also on the parameters to be estimated, the selected distribution, and the parameter estimation method. However, in indoor communication, the highly dynamic and complex nature of channels makes it difficult to accurately analyze channel characteristics. This is mainly due to the rich transmission multipath and random shadowing effects caused by complex environments such as indoor and cabin spaces, which result in a large number of multipath propagation components in wireless signals between communication transceivers, and the LOS path cannot be guaranteed to exist. As a result, users have difficulty determining the typical channel types of signal fading and distribution changes in different positions while moving. This leads to a decrease in the performance of channel estimators and the problem of mismatch between the estimated and measured values of the channel estimation curve, which can seriously affect system performance prediction, adaptive modulation strategy generation, and transmission capacity optimization. Therefore, a more generalized representation of channel models is needed to address these challenges. As research has progressed, new channel models have been proposed, including the \(k\)-\(\mu\) distribution and \(k\)-\(\mu\) fading distribution models, which are more generalized and versatile models for channel fading. The \(k\)-\(\mu\) distribution model, in particular, has garnered attention due to its good agreement with measured channel data and its ability to degrade to different typical channel fading models (e.g., Rayleigh, Rice, and Nakagami-m channels) under certain parameter constraints [4], the development of typical fading channel models can be seen in Table 1. Since the estimation of channel models plays a crucial role in predicting the performance of 5G communication systems, it is important to study the \(k\)-\(\mu\) fading channel model and develop reliable and effective algorithms for estimating its parameters, which can provide a new approach for modeling channels. This is essential to analyzing the theoretical performance limits of communication systems operating in \(k\)-\(\mu\) fading channels and providing a solid theoretical basis for system design. In recent years, there have been many studies on channel performance analysis and system performance optimization of typical fading channel models both domestically and internationally, as reported in [1; 11; 12; 13]. However, there have been relatively few studies on parameter estimation of the \(k\)-\(\mu\) distribution and \(k\)-\(\mu\) shadowing distribution models [14; 15; 16; 17]. In [14], the authors analyzed the error vector magnitude (EVM) performance of the \(k\)-\(\mu\) fading channel, where the EVM parameter was used to evaluate the variation of channel quality, and the results could be used to predict the lower bound of channel quality based