Enhanced deep learning-based optimization model for the optimal energy efficiency-oriented Cognitive Radio Networks
Year: 2024
Type: article
Abstract: Cognitive Radio Networks (CRNs) offer a promising solution to spectrum scarcity by enabling secondary users (SUs) to utilize unused spectrum allocated to primary users (PUs). However, optimizing energy efficiency (EE) while protecting PUs from interference remains a significant challenge. This paper presents a novel approach using an Enhanced Long Short-Term Memory (ELSTM) model, fine-tuned by the Red Panda Optimization (RPO) algorithm, to optimize CRN parameters such as transmission time, transmission power, and sensing time. The motivation behind this work is to enhance EE in CRNs without compromising PU protection, driven by the increasing demand for efficient spectrum utilization in wireless communications. The key contributions of this study include the introduction of the ELSTM-RPO model, which is the first of its kind in CRNs, providing systematic optimization of crucial parameters, and outperforming state-of-the-art methods in terms of EE and spectrum utilization. This work sets a new benchmark for energy-efficient CRNs, offering superior performance and robustness across various network scenarios.
Source: Ain Shams Engineering Journal
Authors: S. Saranya, P. Jayarajan
Institution: Dr. N.G.P. Institute of Technology
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $860
OpenAlex ID: W4402401200