Automating Spectrum Sharing in Emergency Response: A Predictive Modeling Approach

Authors: Vaishali Nagpure

DOI: https://doi.org/10.5281/zenodo.14208892

Short DOI: https://doi.org/g8rrfm

Country: USA

Full-text Research PDF File:   View   |   Download

Abstract: This case study explores the application of statistical models for automating public safety spectrum sharing during emergency situations, focusing on enhancing communication efficiency among first responders. Given the increasing complexity of incidents such as natural disasters, effective spectrum management is crucial for ensuring reliable access to communication resources. We developed a predictive model using time-series analysis and multiple linear regression to assess historical spectrum usage and contextual factors, including incident types and weather conditions. By predicting spectrum demand in real-time, particularly during critical events like hurricanes, the model enables automated adjustments to spectrum allocations. Results from the implementation demonstrated improved communication reliability, increased operational efficiency, and enhanced decision-making capabilities among public safety agencies. The findings highlight the potential of integrating statistical modeling with real-time data to optimize spectrum management, ultimately contributing to more effective emergency response efforts. This study underscores the importance of continuous model refinement through post-event analysis to adapt to evolving needs in public safety communications

Keywords: Dynamic Spectrum Access, Public Safety Communications, Predictive Modeling, Cognitive Radio Networks, Emergency Spectrum Management

Paper Id: 231640

Published On: 2022-04-06

Published In: Volume 10, Issue 2, March-April 2022

Cite This: Automating Spectrum Sharing in Emergency Response: A Predictive Modeling Approach - Vaishali Nagpure - IJIRMPS Volume 10, Issue 2, March-April 2022. DOI 10.5281/zenodo.14208892

Share this