An Efficient Hybrid Ensemble SVM for Optimal Channel and Power Allocation Using Chaotic Quantum Bat Optimization

Abstract

This paper addresses the challenge of resource allocation in wireless networks, given the increasing usage of mobile devices and sensors. To achieve energy efficiency, we propose a novel method called Hybrid Random Forest Ensemble Support Vector Machine (RFESVM) with Chaotic Cloud Quantum Bat Algorithm (CCQBA). The proposed method identifies the best power allocation for each user in Device-to-Device (D2D) communication, which can significantly improve coverage and lower data rates and latency. To overcome the shortcomings of existing methods, we use the chaotic cloud quantum bat algorithm that combines randomness, ergodicity of chaotic mapping, and stability inclination to enhance the convergence speed. To address the issue of class imbalance in the dataset, we combine SVM with RF and establish an ensemble technique called RFESVM. Our proposed method achieves higher energy efficiency compared to other techniques, demonstrating the effectiveness of optimal power allocation in D2D communication. Overall, the proposed method can significantly improve the systems' energy efficiency while ensuring high network performance in wireless networks.