ecappro - sem:无线网络频谱效率最大化的增强型卷尾猴路由优化器

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Ahmed M. Khedr;Dilna Vijayan;Mohamed Saad
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引用次数: 0

摘要

无线通信自干扰(SI)撤销和抑制技术的进展显著提高了采用全双工(FD)传输能力的可行性。与半双工(HD)传输相比,这些功能显著提高了端到端频谱效率(SE),从而为无线通信场景中的多跳路由带来了显著优势。最近,人们对在多跳无线通信场景中发现具有更高SE的路径越来越感兴趣。已经证明,在使用FD干扰约束系统的网络中,求解多跳无线通信场景中的高SE路径不能多项式地求解。本文介绍了一种新颖的路由优化算法ecappro - sem,即增强型卷尾猴路由优化器(enhanced capuchin route optimizer for SE maximization, ecappro - sem),用于无线网络通信。其目标是改进无线通信网络的端到端SE。该算法首先制定一个优化问题,在有效考虑干扰的情况下,最大化FD跨指定路径转发的端到端SE。随后,该算法采用增强的自然启发的元启发式优化技术来精确识别产生最大端到端SE的路由路径。以卷尾猴搜索算法(CSA)为例的自然启发优化方法已经成为逼近最优解的有效元启发式工具。我们通过将其与遗传算法(GA)相结合来增强CSA。新的ecappro - sem算法在实现无线网络路由的峰值端到端SE方面具有优势,在广泛的模拟中优于现有的方法。仿真结果表明,在不同信噪比(SNRs)下,ECapRO分别比Mod-Dijkstras、Pruned-HC和CapRO分别高出33.53%、5.17%和3.40%。此外,在各种SI抵消(SIC)因子下,ECapRO分别优于Mod-Dijkstras、Pruned-HC和CapRO 210.33%、5.25%和2.14%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ECapRO-SEM: Enhanced Capuchin Route Optimizer for Spectral Efficiency Maximization in Wireless Networks
The progress made in self-interference (SI) revocation and suppression techniques for wireless communication has significantly enhanced the viability of employing full-duplex (FD) transmission abilities. Compared to half-duplex (HD) transmission, these capabilities notably enhance the end-to-end spectral efficiency (SE), leading to significant advantages for multihop routing in wireless communication scenarios. There has recently been an increase in interest in discovering paths in multihop wireless communication scenarios that exhibit higher SE. It has been proved that solving for higher SE paths within multihop wireless communication scenarios cannot be solved polynomially in networks employing FD interference-constrained systems. This study introduces ECapRO-SEM, an innovative route optimization algorithm called the enhanced capuchin route optimizer for SE maximization (ECapRO-SEM) for wireless network communication. Its goal is to improve the end-to-end SE of wireless communication networks. The algorithm begins by formulating an optimization problem to maximize end-to-end SE in FD forwarding across specified paths while effectively accounting for interference. Subsequently, the algorithm employs an enhanced nature-inspired metaheuristic optimization technique to precisely identify the routing path that yields the utmost end-to-end SE. Nature-inspired optimization methods, exemplified by the capuchin search algorithm (CSA), have emerged as potent metaheuristic tools for approximating optimal solutions. We enhance the CSA by integrating it with the genetic algorithm (GA). The novel ECapRO-SEM algorithm demonstrates its superiority in achieving peak end-to-end SE for wireless network routing, outperforming existing methodologies in extensive simulations. Simulation results show that ECapRO outperforms Mod-Dijkstras, Pruned-HC, and CapRO by 33.53%, 5.17%, and 3.40%, respectively, at different signal-to-noise ratios (SNRs). Furthermore, with various SI cancellation (SIC) factors, ECapRO outperforms Mod-Dijkstras, Pruned-HC, and CapRO by 210.33%, 5.25%, and 2.14%, respectively.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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