## 4 LLM- and MDE-Enabled Network Planning Workflow
First, the user provides text describing the desired configuration of network planning experiment using natural language text, covering also constraints, such as design limitations and aspects related to performance. Apart from user input, a metamodel is also leveraged in order to construct two prompts targeting ChatGPT:  
**Prompt 1:**_According to {Experiment configuration description text} generate Ecore model instance based on given metamodel {Ecore metamodel}_  
**Prompt 2:**_According to {Experiment limitations text} generate OCL constraints based on given metamodel {Ecore metamodel}_  
Furthermore, the script responsible for LLM prompt construction was implemented using Python programming language and OpenAI API ([[https://platform.openai.com/docs/api-reference](https://platform.openai.com/docs/api-reference)]([https://platform.openai.com/docs/api-reference](https://platform.openai.com/docs/api-reference)), accessed on 8 April 2024) for ChatGPT. The result of the first prompt is model instance, which represents experiment configuration in a form compliant with the given metamodel. However, the outcome of the second prompt is a set of OCL rules which are checked if they hold against the model instance. Once both the rules and model instance are extracted using LLM, a design-time consistency check of the model instance is performed in order to determine if all the given constraints hold. After that, taking into account the specified fading environment configuration, such as outage probability and channel capacity, performance-related elements are assigned to the instance model as well. Additionally, in order to speed up such calculations, a GPU-enabled approach is adopted introducing loop-based computation parallelization, building upon our past works presented in [36; 38]. With the aim to achieve this, the following prompt for parametrizing experiment script leveraging model instance is executed:  
Figure 16: MDE and LLM synergy-based workflow for next-generation network experimentation and planning: 1—Natural language text experiment description and constraints; 2—Taking user-defined input to Prompt construction script; 3—Eclipse Ecore-based metamodel representation; 4—Prompt1 and Prompt 2 executions; 5—Model instance; 6—Experiment template; 7—Model instance as input for code generation; 8—OCL rules for verification of model instance; 9—Verified model instance; 10—Prompt3 execution; 11—Parametrized experiment; 12—Performance estimations, such as Pout, CC, ABEP, LCR, AFD. **Prompt 3:**_Parametrize experiment based on template [experiment template] using model instance [model instance]_  
The elements of the underlying metamodel for experiment representation are depicted in Figure 17. Here, the cardinalities are denoted as: 1-single instance participates in relationship; *-multiple instances of same type within the relationship allowed. The top-level concept within the metamodel is deployment. Deployment consists of elements representing service provider infrastructure like base stations, and, on the other side, considers the end users of these services, while these users can rely on different types of receivers. Regarding the elements of the underlying telco infrastructure, the properties such as power consumption, frequency range, and capacity expressed as number of active users and target network generation (2G-5G) are considered. Moreover, aspects expressing the environmental configuration are also considered as fading and co-channel interference types. In this context, we also consider distinct parameters, specific for the particular type of fading and co-channel interference model. In the end, the proposed metamodel also covers performance-related goals, such as boundary values for channel capacity and outage probability. The estimated performance value is compared to these goals expressed in the form of OCL rules, so the user will be notified whether the experimental deployment is compliant with these requirements. In what follows, Table 13 shows example OCL rules for the described scenario.