使用联合学习和LSTM网络进行隐私保护交付优化
Wei-Che Chien1, Yu Huang1, Bo-Yu Chang1
1Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien City 974301, Taiwan.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
本研究介绍了一种使用联合学习和长短期记忆网络的新动态交接算法. 它通过准确预测信号强度和减少不必要的交接来提高无线网络性能.
科学领域:
- 无线通信系统无线通信系统
- 机器学习应用 机器学习应用
- 网络性能优化 网络性能优化
背景情况:
- 像3GPP Event A3这样的传统交付算法面临着波动的信号强度和用户移动性的挑战.
- 这些局限性导致频繁,低于最佳的交付和无线网络中低效的资源利用.
研究的目的:
- 为无线通信系统开发一个先进的,数据驱动的交付机制.
- 通过提高交付决策准确性和效率来提高网络性能.
- 通过联合学习方法确保数据隐私.
主要方法:
- 结合联合学习 (FL) 用于保护隐私的数据分析和长短期记忆 (LSTM) 网络用于时间信号预测.
- 开发一个动态交付算法,根据预测的参考信号接收功率 (RSRP) 和历史性能调整值.
- 利用现实世界的数据进行广泛的实验验证.
主要成果:
- 拟议的动态交付算法显著优于传统的3GPP事件A3算法.
- 对参考信号接收功率 (RSRP) 实现了更高的预测准确性.
- 证明了减少不必要的移交和改善整体网络性能.
结论:
- 新的FL-LSTM方法为未来的无线网络提供了更有效和可靠的交付机制.
- 这种数据驱动的,保护隐私的方法提高了连接性和网络资源利用率.
- 动态算法为不断发展的无线环境提供了至关重要的适应性性能.
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