AI Agent-Driven Maintenance: Case Study of Outage Probability for 5G Wireless System with L-Branch SC Receiver Influenced by Rician Fading and Rician Co-Channel Interference

This paper analyzes the performance of a 5 G wireless communication system employing selection combining (SC) at the receiver side with $L$ branches. The transmitted signal is subject to small-scale Rician fading, along with Rician co-channel interference (CCI). By implementing an SC scheme with multiple receiving branches, the detrimental effects of multipath propagation are mitigated, resulting in improved system performance parameters such as outage probability ($P_{\text {out }}$) and bit error probability (BEP). A mathematical expression for the outage probability will be derived, based on the signal-to-interference ratio (SIR) at the output of the SC receiver. Based on the obtained results and graphical illustrations, the influence of Rician fading parameters, interference characteristics, and the number of receiver branches on the outage probability will be examined. Additionally, we also consider LLM-driven AI agent-based approach to maintenance of mobile network leveraging the previously calculated outage probability relying on Open AI's o1mini model and provide proof-of-concept implementation.