# GenAI-Empowered Network Evolution: Performance Analysis of AF and DF Relaying Systems over Dual-Hop Wireless Networks Under κ-μ Fading Case Study

## TL;DR

This paper analyzes the performance of relay-based wireless networks using AF and DF techniques under κ-μ fading, and introduces a GenAI-assisted approach to optimize network management.

## Contribution

The novel integration of GenAI into the analysis of relay system performance under κ-μ fading for adaptive network management.

## Key findings

- Closed-form expressions for outage and bit error probabilities are derived for AF and DF relaying under κ-μ fading.
- GenAI is shown to aid in interpreting performance metrics for network adaptation and re-configuration.
- The study provides insights for optimizing next-generation relay-based wireless networks.

## Abstract

In this paper, the performance of dual-hop relay transmission in modern wireless communication systems is analyzed by considering two fundamental relaying techniques, namely, Amplify-and-Forward (AF) and Decode-and-Forward (DF). The propagation conditions on the source–relay (S-R) and relay–destination (R-D) links are modeled using the κ-μ statistical distribution, which effectively captures the fading characteristics in both line-of-sight (LoS) and non-line-of-sight (NLoS) environments. The analysis focuses on key performance metrics, including the outage probability (Pout) and average bit error probability (Pe), for Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK) modulation schemes, assuming transmission via a single relay without a direct S–D link. Closed-form expressions for the considered metrics are derived based on the κ-μ model and verified by numerical evaluation. In addition to classical analytical modeling, a Generative Artificial Intelligence (GenAI)-enabled workflow is incorporated as a supportive tool in order to aid in automated analysis, the interpretation of the results in the context of network management under varying channel and system parameters based on the Pout and Pe calculations with the aim to tackle the underlying complexity and cognitive load of infrastructure adaptation and re-configuration operations. The combined analytical and GenAI-assisted approach provides valuable insights for the optimization, design, and continuous evolution of robust relay-based architectures in next-generation wireless networks.