基于RBF的增量跨层干扰缓解,用于5G通信系统中资源受限的密集物联网网络
Omar Alruwaili1, Jaganathan Logeshwaran2, Yuvaraj Natarajan3
1Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia.
Heliyon
|July 8, 2024
概括
本研究介绍了5G物联网 (IoT) 网络的自适应干扰缓解技术. 新型增量辐射基函数 (RBF) 方法通过在密集的物联网部署中动态管理干扰来提高网络性能.
科学领域:
- 无线通信无线通信
- 网络工程 网络工程
- 信号处理 信号处理
背景情况:
- 在5G网络中,资源有限的物联网 (IoT) 设备的密集部署导致了显著的跨层干扰.
- 干扰会降低网络性能,影响数据传输的可靠性和效率.
- 现有的缓解技术通常将干扰视为静态,无法适应动态的物联网环境.
研究的目的:
- 提出一种新的方法,用于在密集,资源有限的物联网网络的5G通信系统中缓解跨层次干扰.
- 开发使用增量辐射基函数 (RBF) 的动态干扰建模和优化技术.
- 通过适应性资源分配来提高网络容量,可靠性和整体系统性能.
主要方法:
- 使用增量辐射基函数 (RBF) 技术来建模和优化干扰模式.
- 动态更新RBF模型以适应物联网网络不断变化的性质.
- 根据物联网设备的空间分布,位置和密度,智能地分配资源并优化缓解参数.
主要成果:
- 在减轻跨层次干扰方面,增量 RBF 方法的证明有效性.
- 通过广泛的实验和模拟,在通信性能方面取得了实质性的改进.
- 在5G物联网网络中观察到吞吐量增加,数据包丢失减少和延迟减少.
结论:
- 拟议的增量RBF方法为在密集的5G物联网网络中缓解干扰提供了一个动态和适应性的解决方案.
- 这种方法有效地解决了资源有限的物联网设备及其空间分布所带来的挑战.
- 调查结果强调了关键绩效指标的显著改进,为更强大的5G物联网通信铺平了道路.
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