The CCI can be caused by many other factors, like: poor radiation from antenna side lobs, faulty filtering, bad weather conditions, insufficient cross-polarization isolation, nonlinearity of power amplifier, and so on, but it may be mitigated, like fading, by using diversity combining schemes. As far as we know, the CCI, which also deadens wireless systems beside the BX fading, was not taken into account in the available literature until our group of authors. We introduced here the influence of CCI with \(\kappa-\mu\) distribution. In the twentieth century, Nakagami-\(m\) was a very popular distribution because of its ease of manipulation and wide range of applicability [3]. However, it was found that in some environments, Rician and even Weibull distributions give better results. By measurements, it was discovered that the tails of the Nakagami-\(m\) distribution do not fit well to experimental data but only around the mean or median. At the beginning of this millennium, a new fading distribution was proposed--the \(\kappa\)-\(\mu\) distribution, more flexible than distributions known till that time. Additionally, the \(\kappa\)-\(\mu\) distribution is a general fading distribution that includes almost all previously known distributions as special cases. According to that, we included the CCI with a \(\kappa\)-\(\mu\) distribution that runs in addition to BX fading and derived different performances for these disturbances when attenuated by a multi-branch SC receiver. The SC combiner was chosen for its simplicity, satisfactory performance, and affordability, although EGC and MRC combiners give slightly better performance. For such a scenario, we derived here the expressions for the PDF and CDF for the output SIR. After, the other performance elements are obtained, as follows: outage probability, level crossing rate, average fade duration, channel capacity, moment-generating function, and ABEP. Numerical results are presented in more graphs versus the SIR for different values of fading and CCI parameters. Then, the impact of parameters on all performance is checked. Finally, the expression is derived. In the second part, we present a workflow whose goal is to make network planning more convenient and faster, making use of model-driven engineering (MDE)--for network model representation and Large Language Models (LLMs)--automated experiment code generation based on textual description. In this context, the expression derived in the first part of the paper is used for evaluation of the presented approach. The main contributions of this paper are as follows: (1) derivation of the expressions for performance for the \(L\)-branch SC receiver in the presence of BX fading and \(\kappa\)-\(\mu\) CCI; (2) graphical presentation of obtained performance in order to examine the impact of fading and CCI parameters to concerned quantities; and (3) presentation of a tool chain for automated network planning experiment generation starting from free-form text, relying on MDE and LLM. The paper is structured as follows: following the introduction in Section 1 of a description of the papers from the area, in Section 2, the model of an SC receiver is introduced and SIR-based performances of the first order are derived. The graphical presentation and analysis of these performances are also given. In Section 3, the second-order performances are shown. In Section 4, the proposed network experimentation workflow leveraging MDE and LLMs is described, focusing on experiment generation and verification based on textual descriptions. Finally, Section 5 concludes the paper by giving the main points, highlights advantages and disadvantages, and also mentions future research.