Leveraging Outage Probability of a Weibull-faded Gamma-shadowed Channel with co-channel Interference for ChatGPT-Driven QoS Prediction

Abstract

In this paper, a model of a wireless receiver based on macro diversity (MD) technique is considered. The system consists of a macro diversity SC receiver (MD SC) that combines the output signals from two micro diversity (mD SC) receivers with L input branches. This approach is used to simultaneously reduce the effects of Weibull short-term fading, Gamma long-term fading, and co-channel interference (CCI) on system performance. In this paper, closed-form expressions for the outage probability (Pout) of the system for the ratio of the signal-to-interference (SIR) at the outputs of the mD SC and the MD SC receiver, were derived. The system Pout of the ratio the signal-to-interference at the output of the MD SC receiver can further be used to calculate the average fading duration (AFD) of the proposed system configuration. Further, we explore the potential of using Large Language Model (LLM)-based trending ChatGPT service to estimate the degree of the Quality of Service (QoS), considering Pout as one of the input variables. Finally, we have compared the proposed method to traditional Weka-based machine learning algorithm in predicting the QoS.

Keywords

• outage probability (pout)
• selection combining (sc)
• weibull short-term fading
• gamma long-term fading
• gamma co-channel interference (cci)
• chatgpt
• weka