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在下行NOMA 6G网络中优化FBS 3D位置以最大化总比率
Osamah Thamer Hassan Alzubaidi1,2, Hayder Faeq Alhashimi3, Salah Alheejawi4
1Centre of Advanced Communication, Research and Innovation (ACRI), Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya (UM), Kuala Lumpur, 50603, Malaysia. osamah.th@uokerbala.edu.iq.
Scientific reports
|November 6, 2025
概括
使用遗传算法优化飞行基站 (FBS) 3D 位置,增强了万物互联网 (IoE) 设备的无线连接. 这一策略通过最大限度地减少6G网络的干扰来最大限度地提高总和率 (TSR).
科学领域:
- 无线通信无线通信
- 网络工程 网络工程
- 优化算法 优化算法
背景情况:
- 万物互联网 (IoE) 设备的普及给下一代无线网络带来了重大挑战,要求超可靠,高容量的连接.
- 飞行基站 (FBS) 增强了车辆通信,但它们的移动性和 IoE 设备的移动性会导致通信链路中断,降低性能.
- 战略FBS定位至关重要,以减轻这些中断,并确保连接的一致性.
研究的目的:
- 通过优化服务于 IoE 设备的多个 FBS 的三维 (3D) 位置,在下链 6G 网络中最大限度地提高总和率 (TSR).
- 通过智能FBS放置来最大限度地减少集群间/集群内部干扰,确保FBS与动态IoE设备位置相比保持中心位置.
- 在每个FBS引入使用非直角多重接入 (NOMA) 的新型传输结构,以提供高效的IoE设备服务.
主要方法:
- 开发了一种使用多FBS与非直角多通道 (NOMA) 的传输结构,用于下链6G网络.
- 制定了一个非凸的优化问题,以确定FBS的最佳3D位置,以保持对IoE设备的中心性.
- 采用基于遗传算法的进化方法来解决复杂的FBS定位优化问题.
- 实施了连续干扰取消 (SIC) 策略,以在 IoE 设备之间管理 NOMA-SIC.
主要成果:
- 与现有方法相比,基于遗传算法的提议优化显著改善了总和率 (TSR).
- 与最先进的方法相比,达到高达21.03%的TSR,包括化,块坐标下降,修改的灰狼优化和集群中心.
- 证明了优化FBS 3D位置的有效性,以保持连接并提高网络性能.
结论:
- 优化FBS的3D位置是一种高效的策略,可以提高6G网络中的无线通信性能,其中有许多IoE设备.
- 拟议的多FBS NOMA传输结构,加上遗传算法优化和SIC,为增强TSR和连接提供了一个强大的解决方案.
- 这些发现突显了智能基站放置在未来无线系统中克服移动性诱导的挑战的潜力.
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