## 4 LLM- and MDE-Enabled Network Planning Workflow  
The emerging Large Language Model (LLM)-based ChatGPT ([[https://chat.openai.com/](https://chat.openai.com/)]([https://chat.openai.com/](https://chat.openai.com/)), accessed on 8 April 2024) human-like conversational service has drawn significant attention in both industry and academia, which resulted in many novel adoptions and use cases in various areas, ranging from creative content writing to programming [35]. Taking into account the experiments carried out by curious researchers and enthusiasts around the world, it can be summarized that LLMs are able to cover various relevant aspects within the generation of computer applications and the software development process itself, as well. Among these adoptions, some of them, besides LLMs, rely on their synergy with model-driven engineering (MDE) [36], making many innovative usage scenarios possible [36; 37; 38]: (1) domain conceptualization--metamodel construction based on free-form textual information; (2) instance creation--metamodel and natural language text are used as inputs, while the target output is instance of a model with respect to that metamodel; (3) modeling constraint extraction--identification of rules that must hold within model instances, where inputs are these constraints in textual form, along with the given metamodel, while the outputs are formal logic rules, such as Object Constraints Language (OCL); (4) generation of code--code templates together with model instances are taken as inputs and used for the purpose of generating executable program code, targeting some specific platform or programming language. Considering the previously mentioned LLM and MDE synergy use cases, in this paper, we adopt these techniques with the goal of reducing the overall cognitive load and effort needed for wireless network planning and experimentation. Due to the increasing complexity of infrastructure, besides the growing number of the involved devices and their heterogeneous nature, the process of next-generation network-related prototyping and experimentation is highly challenging task [36; 38]. For that reason, in this paper, we used an approach leveraging MDE tools (Eclipse Ecore ([[https://eclipse.dev/modeling/emf/](https://eclipse.dev/modeling/emf/)]([https://eclipse.dev/modeling/emf/](https://eclipse.dev/modeling/emf/)), accessed on 8 April 2024) and Object Constraints Language--OCL ([[https://www.omg.org/spec/OCL/2.4/About-OCL](https://www.omg.org/spec/OCL/2.4/About-OCL)]([https://www.omg.org/spec/OCL/2.4/About-OCL](https://www.omg.org/spec/OCL/2.4/About-OCL)), accessed on 8 April 2024)) for representation of domain concepts and their relationships, together with constraints and, on the other hand, trending ChatGPT as LLM representative in order to enable automated creation of model \begin{table}
\begin{tabular}{c c c c} \hline
**AFD** & \(z=-10\)**dB** & \(z=0\)**dB** & \(z=10\)**dB** \\ \hline \(\kappa_{y}=1\), \(\mu=1\), \(L=2\) & 7 & 8 & 7 \\ \hline \(\kappa_{y}=2\), \(\mu=1\), \(L=2\) & 9 & 10 & 11 \\ \hline \(\kappa_{y}=3\), \(\mu=1\), \(L=2\) & 10 & 13 & 14 \\ \hline \(\kappa_{y}=4\), \(\mu=1\), \(L=2\) & 12 & 15 & 17 \\ \hline \(\kappa_{y}=1\), \(\mu=2\), \(L=2\) & 9 & 11 & 12 \\ \hline \(\kappa_{y}=1\), \(\mu=3\), \(L=2\) & 10 & 13 & 14 \\ \hline \(\kappa_{y}=1\), \(\mu=4\), \(L=2\) & 12 & 14 & 16 \\ \hline \(\kappa_{y}=1\), \(\mu=1\), \(L=3\) & 6 & 8 & 7 \\ \hline \(\kappa_{y}=1\), \(\mu=1\), \(L=4\) & 6 & 8 & 7 \\ \hline \(\kappa_{y}=1\), \(\mu=1\), \(L=5\) & 5 & 7 & 7 \\ \hline \end{tabular}
\end{table} 
Table 12: The number of additions in sums in (42) for reaching precision at the 5 th significant digit for the AFD; the CCI parameters \(\kappa_{y}\) and \(\mu\), and number of branches \(L\), are variable; permanent fading parameters are: \(\kappa_{x}=1\) and \(m=1\), and powers: \(\Omega=1\) and s = 1.  
instances based on input text, extract domain constraints from text and, finally, generate the experiment code based on model instances. In Figure 16, we depict the proposed workflow.