无人机辅助的虚拟合作频谱传感用于认知无线电网络
Noor Gul1,2, Su Min Kim2, Jehad Ali3
1Department of Electronics, University of Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan.
PloS one
|September 5, 2023
概括
本研究介绍了使用顺序决策融合 (SDF) 的无人机 (UAV) 的虚拟合作频谱传感 (CSS). 这种新的方法通过优化频谱传感来提高无人机网络的能效和吞吐量.
科学领域:
- 无线通信无线通信
- 频谱传感 频谱传感
- 无人驾驶飞行器 (UAV) 网络
背景情况:
- 协同频谱传感 (CSS) 能够使次级用户 (SU) 报告主要用户 (PU) 通道状态到融合中心 (FC).
- 传统的CSS在无人驾驶飞行器 (UAV) 网络中面临功率限制,原因是开销高.
- 现有的方法在动态无人机环境中难以有效利用频谱和电力限制.
研究的目的:
- 为无人机网络提出一种新的虚拟CSS方案,以克服功率限制并提高频谱传感效率.
- 引入连续决策融合 (SDF) 方法,与机器学习集成,以实现动态适应.
- 提高无人机通信中的频谱利用率和吞吐量.
主要方法:
- 一个虚拟的CSS方案,使用单个无人机在圆形轨迹上,具有定义的框架结构 (传感/传输周期,迷你传感槽).
- 实现顺序决策融合 (SDF) 来从迷你槽中积累本地传感决策.
- 利用AdaBoost集成分类器 (ENC) 来动态重新配置小时段,并确定SDF (SDF-ENCODT) 的最佳决策值 (ODT).
主要成果:
- 与传统的决策聚变 (TDF) 相比,拟议的SDF-ENCODT方案显示出更高的能源效率和更好的吞吐量.
- 模拟结果验证了SDF-ENCODT在各种参数上的有效性,包括无人机速度,半径,检测概率和SNR.
- 虚拟CSS方法有效地解决了无人机通信网络中频谱稀缺的挑战.
结论:
- 虚拟CSS与SDF提供了一个有前途的解决方案,以提高频谱传感性能,克服无人机网络中的功率限制.
- 集成ML和最佳决策值显著提高了拟议方案的效率.
- 该研究为未来无人机通信系统的高效频谱管理提供了坚实的框架.
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