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Related Experiment Video

Updated: Feb 28, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

GenAI-Empowered Network Evolution: Performance Analysis of AF and DF Relaying Systems over Dual-Hop Wireless Networks

Nenad Petrovic¹, Vuk Vujovic², Suad Suljovic²

¹Faculty of Electronic Engineering, University of Nis, 18104 Nis, Serbia.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

This study analyzes dual-hop relay performance using Amplify-and-Forward (AF) and Decode-and-Forward (DF) techniques. A Generative AI approach aids in optimizing wireless networks by analyzing outage and bit error probabilities.

Keywords:

Binary Phase Shift Keying Generative Artificial Intelligence (GenAI)Quadrature Phase Shift Keying average bit error rate outage probability κ-µ fading

Related Experiment Videos

Last Updated: Feb 28, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Wireless Communication Systems
Signal Processing
Artificial Intelligence

Background:

Dual-hop relay transmission is crucial for modern wireless systems.
Performance analysis requires understanding fading characteristics in various environments.
Relaying techniques like Amplify-and-Forward (AF) and Decode-and-Forward (DF) are fundamental.

Purpose of the Study:

To analyze the performance of AF and DF relaying techniques in dual-hop systems.
To model wireless propagation using the versatile κ-μ distribution for diverse environments (LoS and NLoS).
To derive and verify closed-form expressions for outage probability (Pout) and average bit error probability (Pe) for BPSK and QPSK modulation.

Main Methods:

Modeled source-relay (S-R) and relay-destination (R-D) links using the κ-μ statistical distribution.
Derived closed-form expressions for Pout and Pe under BPSK and QPSK modulation.
Incorporated a Generative Artificial Intelligence (GenAI)-enabled workflow for automated analysis and interpretation.

Main Results:

Closed-form expressions for Pout and Pe were derived based on the κ-μ model.
Numerical evaluations validated the analytical results.
The GenAI workflow demonstrated utility in interpreting results for network management.

Conclusions:

The combined analytical and GenAI-assisted approach offers valuable insights for optimizing relay-based wireless architectures.
This methodology aids in tackling complexity and cognitive load in infrastructure adaptation.
The findings support the continuous evolution of robust next-generation wireless networks.