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通过在D2D毫米波通信中基于深度学习的预测来最大限度地降低中断概率和能源消耗
1School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.
Scientific reports
|February 13, 2026
概括
本研究引入了一个深度学习模型,以减少设备对设备 (D2D) 毫米波通信中的中断概率和能源使用. 新的FEHO+DSNN方法提高了网络的稳定性和效率.
科学领域:
- 无线通信无线通信
- 深度学习 (Deep Learning) 是一种深度学习.
- 优化技术 优化技术
背景情况:
- 设备对设备 (D2D) 通信对于信息交换至关重要,毫米波 (mmWave) 集成由于复杂的无线通道而带来可靠性挑战.
- 断电概率和能源消耗是评估D2D毫米波网络性能和覆盖力的关键指标.
研究的目的:
- 引入一个支持优化的深度学习 (DL) 模型,以最大限度地降低D2D毫米波系统中断概率和能源消耗.
- 提高D2D毫米波通信网络的稳定性和效率.
主要方法:
- 模拟D2D毫米波通信,考虑连贯的单个集群近似和非连贯的下界覆盖概率机制.
- 雇佣的弗拉明戈鹿群优化 (FEHO) 以尽量减少停机概率.
- 利用深度尖端神经网络 (DSNN) 来设计一个最佳的值,以最大限度地减少联合能源消耗和覆盖概率.
主要成果:
- 拟议的FEHO+DSNN模型与现有技术相比,表现出优越的性能.
- 实现了平均发射功率为39.056dBm,故障概率为0.0015.5.
- 成功地将停电概率和能源消耗降到最低.
结论:
- 开发的支持优化的DL模型有效地降低了D2D毫米波通信中的中断概率和能源消耗.
- FEHO+DSNN方法为提高下一代无线网络的可靠性和效率提供了一个有希望的解决方案.
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