AI-Driven Approach for QoS Estimation Using LCR in 5G Network with Selection Combining in α-η-µ Fading and Co-Channel Interference Environment

In the following work, an analysis of level crossing rate (LCR) for 5G network distracted by α-η-µ fading and co-channel interference (CCI) with α-η-µ distribution is done. An expression for the LCR at the receiver output is derived. The receiver uses selection combining to alleviate the impact of fading and CCI. Based on presented graphs, we analyzed how quantities of fading parameters and CCI influence the calculated system performance. The second part of the paper explores the potential of using Large Language Model (LLM)-based trending ChatGPT service for purpose of Quality of Service (QoS) prediction, considering LCR among the input variables. Finally, we compare the presented approach to traditional machine learning techniques relying on Weka library in Java.