在5G网络中,小型电池的混合计算和基于真实数据的定位
Flávio Henry Ferreira1, Fabrício José Brito Barros1, Miércio Cardoso de Alcântara Neto1
1Post Graduate Program in Electrical Engineering, Institute of Technology of Federal University of Pará, Federal University of Pará, Belém, PA, Brasil.
PeerJ. Computer science
|July 6, 2023
概括
本研究介绍了一种智能小电池定位方法,使用混合集群算法,对智能城市的5G网络进行元启发式优化. 它旨在最大限度地降低信号衰减,并确保跨各种5G频谱的服务连续性.
科学领域:
- 电信工程 电信工程 电信工程
- 计算机科学 计算机科学
- 城市规划 城市规划
背景情况:
- 下一代网络,特别是5G,对于智能城市至关重要,在人口密地区实现大规模连接.
- 小电池发射器是满足高需求并提供众多连接的关键5G技术.
- 有效的小电池部署对于可靠的智能城市基础设施至关重要.
研究的目的:
- 在智慧城市的背景下,为5G网络提出一个智能小电池定位策略.
- 优化小电池的位置,以最大限度地减少基站和用户之间的信号衰减.
- 评估生物灵感优化算法的性能,以应对这一部署挑战.
主要方法:
- 开发一个混合集群算法与元启发优化集成.
- 使用现实数据为用户服务在一个定义的区域,覆盖标准.
- 在700 MHz,2.3 GHz和3.5 GHz的5G频谱中对功率值进行模拟分析.
- 验证多目标优化算法,如花花授粉和子搜索.
主要成果:
- 拟议的混合集群算法与元启发式优化有效地识别最佳的小细胞位置.
- 实现了信号减弱的最小化,提高了智能城市用户的服务质量.
- 模拟演示了不同功率值对关键5G频率的服务连续性的影响.
结论:
- 开发的智能小电池定位方法为提高5G网络在城市环境中的性能提供了可行的解决方案.
- 超启发式优化技术,特别是生物启发的算法,显示出解决复杂网络部署挑战的前景.
- 这项研究有助于充分实现智慧城市所需的基础基础设施.
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