对绿色下一代通信系统的基于人工智能优化的异质方法.
Haitham Khaled1, Emad Alkhazraji1
1Department of Electro-Mechanical Systems Engineering Technology, Abu Dhabi Polytechnic, Abu Dhabi P.O. Box 111499, United Arab Emirates.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
本研究介绍了一种基于软件定义无线电 (SDR) 的长期进化许可辅助访问 (LTE-LAA) 架构,用于适应性异质网络 (HetNets). 拟议的设计大大提高了吞吐量,降低了电力消耗,为下一代网络铺平了道路.
科学领域:
- 无线通信网络 无线通信网络
- 网络架构和设计 网络架构和设计
- 软件定义网络 软件定义网络
背景情况:
- 传统的异质网络 (HetNets) 由于硬件限制,在适应动态网络条件时面临限制.
- 软件定义无线电 (SDR) 技术为提高网络适应性提供了一个有希望的解决方案.
研究的目的:
- 为下一代通信网络引入一种基于软件定义无线电 (SDR) 的新型长期进化许可辅助访问 (LTE-LAA) 架构.
- 通过正确的设计和调整,证明在HetNets中实现高适应性,增加吞吐量和降低功耗的可行性.
主要方法:
- 制定吞吐量最大化和功耗最小化作为一个受约束的优化问题.
- 将拟议的解决方案 (优化和启发式) 与现有方法进行比较.
- 采用人工智能 (AI) 技术,包括随机森林回归,粒子群优化和遗传算法,用于多目标优化.
主要成果:
- 拟议的基于SDR的LTE-LAA架构在功率效率和系统吞吐量方面明显优于现有的方法.
- 由人工智能驱动的多目标优化有效地平衡了吞吐量,功率效率和能源消耗之间的权衡.
- 该架构在动态网络环境中表现出高水平的适应性.
结论:
- 开发的基于SDR的LTE-LAA架构是下一代HetNets的可行解决方案,满足对功率,吞吐量和绿色可扩展性的关键要求.
- 该研究强调了SDR技术和AI在优化未来无线通信系统方面的潜力.
- 这项研究为更具适应性和能源效率的无线网络设计提供了基础.
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