## 1 Introduction
On the other hand, SC combining has a lower cost and still good enough features for mitigating fading and CCI. Therefore, in this paper, we analyze a multi-branch SC receiver used to enhance wireless systems in the presence of \(\eta\)-\(\mu\) fading and CCI. Since this scenario has an important vehicular application, we derive its CC expression. On another front, artificial intelligence (AI) has opened new horizons for wireless network planning, especially in proactive performance-related infrastructure reconfiguration and tuning [16]. Realistic environments also encompass negative effects on propagated waves, which must be considered in these predictions. However, the rise of AI-based tools and rapid advancements in this area have made simulation workflows increasingly complex, demanding a higher cognitive load to master the related concepts and techniques. As a downside, because adopting novel techniques requires additional effort, the time required for insightful network planning has increased, consequently slowing down prototyping and research. Hence, in this paper, we focus on providing an automated network simulation workflow that integrates AI tools to reduce the time needed for simulation. The presented case study considers environments influenced by \(\eta\)-\(\mu\) multipath fading and \(\eta\)-\(\mu\) co-channel interference. To the best of our knowledge, there is no other publicly available scientific work that adopts Chat Generative Pre-trained Transformer (ChatGPT) in combination with a model-driven approach for the purpose of graph database-related code generation in the field of telecommunications. The main contributions of this paper are as follows:  
1. We derive the expression for CC for the \(L\)-branch SC receiver in the case of \(\eta\)-\(\mu\) multipath fading and \(\eta\)-\(\mu\) CCI;2. We present a QoS estimation model based on classifications within the Neo4j graph database, leveraging the previously derived CC as one of the inputs;
3. We propose a ChatGPT-based approach to automated Neo4j query generation, covering data import and classification using a meta-model. This paper is organized into four main sections and includes two appendices. Following Section 1, we derive the CC expression, and analyze the influence of the parameters based on the results in plotted graphs. Afterwards, we propose a classification-based method for quality of service (QoS) prediction, utilizing the previously derived CC expression within the network planning environment to calculate one of the input variables. In this section, we also provide an overview of the underlying framework for machine learning capabilities--the Neo4j graph database and its graph data science (GDS) library. Additionally, we give background information about large language models (LLMs) and model-driven engineering, as they represent the underlying concepts behind ChatGPT and Ecore. The synergy of these two is leveraged with the aim of automatizing a machine learning-aided simulation workflow for mobile network planning. While Ecore-based models were used for prompt construction, we relied on ChatGPT for Neo4j query generation based on these prompts. At the end of the third section, we evaluate the benefits of the proposed approach and provide a comparison to the manual procedure. Finally, the achieved QoS estimation performance is compared to other similar work. The fourth section summarizes the results and discusses possible future research directions. The appendices offer additional details on the derivation of the CC expression and the intermediate steps.