Energy Efficiency Enhancement Model (EEEM) in Cognitive Radio Networks Using Underlay Techniques with Energy Harvesting
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International Journal of Recent Engineering Science (IJRES) |  |
| © 2024 by IJRES Journal | ||
| Volume-11 Issue-3 |
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| Year of Publication : 2024 | ||
| Authors : Umeudu Francis .T, Omijeh Bourdillon .O |
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| DOI : 10.14445/23497157/IJRES-V11I3P110 |
How to Cite?
Umeudu Francis .T, Omijeh Bourdillon .O, "Energy Efficiency Enhancement Model (EEEM) in Cognitive Radio Networks Using Underlay Techniques with Energy Harvesting," International Journal of Recent Engineering Science, vol. 11, no. 3, pp. 94-102, 2024. Crossref, https://doi.org/10.14445/23497157/IJRES-V11I3P110
Abstract
This research presents the development and evaluation of an Energy Efficiency Enhancement Model (EEEM) tailored for cognitive radio networks, specifically integrating underlay techniques with energy harvesting capabilities. The study meticulously addresses key objectives, beginning with the creation of security-enhanced spectrum sensing algorithms featuring an Energy detector, ensuring robust and reliable spectrum utilization for enhanced network security. Moreover, an optimized dynamic power allocation algorithm was developed, facilitating real-time adjustments to transmit power based on channel conditions and RF energy harvesting levels, thereby optimizing power consumption and maximizing data transmission rates. Through extensive performance evaluation, the proposed scheme showcases notable enhancements across various metrics. Notably, the EEEM achieves an average throughput improvement of approximately 39.91% over the existing model, demonstrating its capacity to efficiently utilize resources for higher data transmission rates across diverse Signal-to-Noise Ratio (SNR) levels. Spectral efficiency witnesses an average improvement of about 22.8%, showcasing the model's effectiveness in optimizing data transmission per unit bandwidth. The enhanced model also shows approximately a 37.5% improvement in power consumption compared to the existing model based on the given final power consumption values. These findings highlight the significant enhancements achieved by the EEEM in improving throughput, spectral efficiency, and spectrum utilization efficiency, positioning it as a promising approach for enhancing performance in underlay cognitive radio networks with RF energy harvesting capabilities. Additionally, the comparative validation against existing energy efficiency schemes further validates the superiority and practical applicability of the EEEM, consolidating its potential for advancing energy-efficient communication networks.
Keywords
Cognitive radio, Underlay, Overlay, Energy harvesting, Algorithm, Wireless network.
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