Performance Evaluation of a Wireless Cooperative Network Using Random Forest Technique

Abstract

Cooperative networks implement relay nodes to boost dependable and efficient end-to-end data exchange operations amongst endpoints. As the decision of relays through Multi Branch Multi-Hop (MB-MH) network topologies is made based on conventional SNR-based methods, a high Bit Error Rate (BER) is generated. This research thus evaluates Random Forest (RF) as an Artificial Intelligence (AI) based solution to find its optimal relay nodes. RF Model merges channel conditions with SNR attributes to reduce Network Error as it minimizes the errors thus lowering operational cost. The RF model also did well when used for simulations on Rayleigh fading channels in MATLAB R2021b and reduced BER compared to conventional approaches. The RF approach AI technology can improve the exceptional performance in wireless cooperation networks, contributing to next-generation communication systems.