## 5 Conclusions  
The main contribution of our paper is modelling of a wireless channel with a multi-branch SC diversity receiver in the presence of Beaulieu-Xie fading and CCI with \(\kappa\)-\(\mu\) distribution. There is no defined fading distribution in the literature that can adequately model the multiple specular signal components transmitting between the transmitter and the receiver together with the diffused components, which is a typical environment in the case of femtocells and high-speed trains. For the aforementioned reasons, we presented here derivations for different first- and second-order performances for such a model. Another great advantage of introducing the BX fading distribution is that the existing fading distributions (\(\kappa\)-\(\mu\), Nakagami-m, Rayleigh, Rician, etc.) can be obtained as its special cases by adjusting a combination of parameters. Since \(\kappa\)-\(\mu\) is also general fading distribution, it is possible to apply the obtained results to a large number of system configurations in the presence of fading and CCI with different listed distributions. Additionally, the presented approach making use of LLMs and MDE significantly reduces both the effort and time required for wireless network experimentation as it takes natural language text descriptions as input from the end user. This way, the required cognitive load is reduced as mastering additional tools for conceptual modeling directly is not required. In our future work, we will analyze the performance of wireless systems in the presence of fading and CCI with these and other general distributions, although with EGC and MRC diversity receivers. Using EGC and MRC diversity receivers achieves better \begin{table}
\begin{tabular}{l l l} \hline \hline
**Text** & **OCL Rule** \\ \hline \multirow{3}{*}{Deployment should have at least two base stations} & context Deployment \\  & inv deploymentHasAtLeastTwoBaseStations: \\  & self.baseStations\textgreater{}\textgreater{}size() \textgreater{}=2 \\ \hline \multirow{3}{*}{Outage probability of deployment should be less than 0.05} & context Deployment \\  & inv outageProbabilityBelowThreshold: \\  & self.outageProbability \textless{} 0.05 \\ \hline \multirow{3}{*}{Minimal number of service consumers supported should be 150} & context Deployment \\  & inv MinimumServiceConsumers: \\  & self.serviceConsumers.numConsumers \textgreater{}=150 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Examples for generated OCL rules. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Aspect**} & \multirow{2}{*}{**Manual Efforts**} & \multicolumn{2}{c}{**Execution Time**} & \multirow{2}{*}{**Experiment Description**} \\  & & **1 Receiver** & & \\  & & **2 Receivers** & & \\ \hline \multirow{2}{*}{Text to model instance} & \multirow{2}{*}{50 s—sentence typing} & 8.4 & \multirow{2}{*}{} \\  & & 13.2 & \\ \hline \multirow{2}{*}{Model instance to experiment} & \multirow{2}{*}{Automatic} & 4.3 & \multirow{2}{*}{} \\  & & 9.6 & \\ \hline \multirow{2}{*}{Performance estimation} & \multirow{2}{*}{Automatic} & 1.8 & \multirow{2}{*}{} \\  & & 2.9 & \\ \hline \multirow{2}{*}{Constraint definition} & \multirow{2}{*}{30 s—sentence typing} & 7.9 & \multirow{2}{*}{} \\  & & 12.6 & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: LLM-enabled workflow evaluation approach. performance than using SC receivers, which is popular for its simplicity, affordability, and favorable price, but with some loss in performance quality. Conceptualization, N.P., S.S. and D.K. ; methodology, D.K. and S.S.; software, N.P. and S.S.; validation, D.M. and G.D.; formal analysis, N.P. ; investigation, S.S. and D.K. ; resources, D.K. ; data curation, S.S. and N.P. ; writing--original draft preparation, D.K. and N.P. ; writing--review and editing, D.M. and G.D.; visualization, S.S.; supervision, D.M. and D.K. ; project administration, D.K. ; funding acquisition, N.P. and D.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.