\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Number** & **Model Type** &
\begin{tabular}{c} **Degradation to** \\ **Other Models** \\ \end{tabular} & **Reference** \\ \hline
1 & One-sided Gaussian & & [5] \\ \hline
2 & Rayleigh distribution & & [6] \\ \hline
3 & Rician distribution & 2 & [7] \\ \hline
4 & Nakagami-\(m\) distribution & 2 & [8] \\ \hline
5 & Hoyt (Nakagami-q) distribution & & [9] \\ \hline
6 & \(k\)-\(\mu\) distribution & \(1,2,3,4\) & [10] \\ \hline
7 & \(k\)-\(\mu\) shadowed fading & \(1,2,3,4,5,6,7\) & [4] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The development process of the fading channel model. on \(k\)-\(\mu\) fading channels. In [15], the authors assumed QAM modulation channels passing through \(k\)-\(\mu\) shadow fading channels and proposed the average symbol error rate (ASER) expressions for orthogonal amplitude modulation (RQAM) and cross-orthogonal amplitude modulation (XQAM) in single-input single-output (SISO) and multiple-input multiple-output (MIMO) channel systems. In [16], the transmission characteristics of different receivers in \(k\)-\(\mu\) shadowing fading channels were studied. The average bit error rate, outage probability, and channel capacity under three different receivers, namely, N-branch selection combining, dual-branch selection combining, and dual-branch maximal ratio combining, were derived. In [17], the authors used the \(k\)-\(\mu\) fading channel model to describe complex data transmission scenarios in a bidirectional relay network, derived the outage probability under \(k\)-\(\mu\) fading channels, and also derived the outage probability under various mixed fading channels. In addition, the \(k\)-\(\mu\) distribution and \(k\)-\(\mu\) shadowing distribution have been widely used in the performance analysis of wireless communication and network systems. Through detailed analysis of the higher-order statistical characteristics of the frequency-agile system in the \(k\)-\(\mu\) and \(k\)-\(\mu\) shadow fading channels, reference [18] derived novel and accurate expressions for the high-order cumulants and moments of channel capacity under the generalized fading model, as well as simplified high-order statistical expressions for channel capacity in extremely low and high signal-to-noise ratio conditions. Reference [19] considered a master-slave and cognitive relay spectrum sharing network in which the channel follows a \(k\)-\(\mu\) shadow fading distribution, derived the expression for the interruption probability of the cognitive relay network under the peak interference power constraint of the master user network, and analyzed the system performance of the cognitive relay network under different fading and shadowing parameters and power constraint conditions. Reference [20] studied the performance of energy-based spectrum sensing in \(k\)-\(\mu\) and \(k\)-\(\mu\) extreme fading channels under the Bayesian framework, and derived accurate expressions for the channel model error probability (as a convex combination of false alarm probability and signal detection probability). Reference [21] derived a novel closed-form analytical solution for the average quality metric (average receiver operating characteristic curve and area under the curve) of a generic \(k\)-\(\mu\) shadow fading channel model by analyzing the average quality metric describing the energy detection process; moreover, to investigate the impact of human body occlusion on the channel in cellular device-to-device communication, reference [22] derived the probability density function and moment function of the channel model and applied it to link communication between devices in an outdoor urban environment at 868 MHz, and found that the shadow effect of the deterministic component varies significantly depending on the relative position of the user device to the body and link geometry.