The proliferation of Ultra Wide-Band (UWB) systems has introduced new challenges in predicting optimal resource allocation, necessitating advanced methodologies to enhance efficiency. Current resource prediction models for UWB systems often struggle to accurately forecast optimal resource allocation due to the dynamic and complex nature of the communication environment. This study aims to overcome these limitations by introducing a novel framework that integrates machine learning ensembles and latent graph predictor FM to achieve more accurate and reliable resource predictions. While various resource prediction models exist, a noticeable gap remains in achieving optimal predictions for UWB systems in dynamic scenarios. Existing models lack the adaptability and precision required for efficient resource allocation. This research bridges this gap by introducing a comprehensive approach that leverages ensembles of classifiers and latent graph predictor FM to enhance prediction accuracy. This study addresses the existing gaps in resource prediction by proposing an innovative approach that combines ensembles of classifiers with a Latent Graph Predictor FM. Our methodology involves the development of an integrated model that combines the strengths of machine learning ensembles and latent graph predictor FM. The ensemble of classifiers captures diverse patterns and features, while the latent graph predictor FM refines predictions based on latent relationships within the communication network. This dual-layered approach ensures robust and accurate resource prediction in UWB systems. The experimental results demonstrate a significant improvement in resource prediction accuracy compared to existing models. The proposed framework effectively adapts to dynamic UWB environments, providing optimal resource allocation in real-time scenarios. The study showcases the potential of ensembles of classifiers and latent graph predictor FM in addressing the challenges of resource prediction in UWB systems.
B. Ebenezer Abishek1, A. Vijayalakshmi2, Blessy Sharon Gem3, P. Sathish Kumar4
Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, India1,3, Vels Institute of Science, Technology and Advanced Studies, India2, Rajalakshmi Institute of Technology, India4
Ultra Wide-Band Systems, Resource Prediction, Ensembles of Classifiers, Latent Graph Predictor FM, Optimal Resource Allocation
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 14 , Issue: 4 , Pages: 3043 - 3049 )
Date of Publication :
December 2023
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