Enhancing Cognitive Radio Network Design with New Energy Detection versus Pilot and Radio-Based Techniques

Rizwana A., Nagaraju V.

2023

Abstract

This study aimed to enhance the energy efficiency (EE) and accuracy of the Cognitive Radio Network (CRN) system design by using a unique energy detection approach, contrasting it with the conventional Pilot and Radio Based Detection Technique. A model was developed and processed in Python, using a network dataset for initial exploration, sourced from the UCI Machine Learning Repository. Statistically, with a confidence interval of 95% and sample size of 140, the energy detection’s precision was assessed. In evaluating spectrum allocation, the conventional technique had a slightly higher accuracy. However, our proposed energy detection method achieved an impressive 95.2713% accuracy. Surprisingly, it processed in just 4 seconds, half the time taken by the conventional method. The results confirm the new method’s superiority in energy efficiency.

Paper Citation

in Harvard Style

A. R. and V. N. (2023). Enhancing Cognitive Radio Network Design with New Energy Detection versus Pilot and Radio-Based Techniques. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 520-526. DOI: 10.5220/0012602900003739

in Bibtex Style

@conference{ai4iot23,
author={Rizwana A. and Nagaraju V.},
title={Enhancing Cognitive Radio Network Design with New Energy Detection versus Pilot and Radio-Based Techniques},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={520-526},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012602900003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Enhancing Cognitive Radio Network Design with New Energy Detection versus Pilot and Radio-Based Techniques
SN - 978-989-758-661-3
AU - A. R.
AU - V. N.
PY - 2023
SP - 520
EP - 526
DO - 10.5220/0012602900003739
PB - SciTePress