认知无人机网络中多机协作频谱感知研究

多无人机协同工作模式在未来通信中有着重要的应用前景。结合频谱资源短缺的问题,建立认知无人机网络模型,并研究多机协作频谱感知性能,提出一种最佳融合准则来优化检测性能。针对无人机数量较多的大型认知无人机网络,提出一种快速高效的协作频谱感知算法,并比较该算法在瑞利衰落以及Nakagami衰落2种信道环境下的性能。仿真结果表明:①采用最佳融合准则可以使协作频谱感知总错误率达到最小;②快速协作频谱感知算法可以利用较少的无人机来保证协作频谱感知的检测准确度,避免了不必要的感知过程,减少了参与协作频谱感知的次级用户数量,降低了协作感知时间,从而节省了感知过程开销,而且相比于瑞利衰落信道,该算法在Nakagami衰落信道环境下具有更好的性能。

Cooperative Spectrum Sensing of Multi UAV in Cognitive Drone Networks

Multi-UAV(unmanned aerial vehicle) cooperative work mode will have important application prospects in future communication. In consideration of the shortage of spectrum resources, this paper establishes a cognitive drone network model, studies the spectrum sensing performance of multi UAV cooperative work, and proposes an optimal fusion criterion to optimize the detection performance. A fast and efficient cooperative spectrum sensing algorithm is proposed to compare the performance of two algorithms under condition of Rayleigh fading and Nakagami fading channels. The simulation results show that: 1) The total error rate of cooperative spectrum sensing can be minimized by using the optimal fusion criterion. 2) The fast cooperative spectrum sensing algorithm can utilize the fewer UAVs for ensuring the detection accuracy of cooperative spectrum sensing, avoiding unnecessary sensing process, reducing the number of secondary users participating in cooperative spectrum sensing, reducing cooperative sensing time, and saving the cost of sensing process. Moreover, compared with the Rayleigh fading channel, this algorithm has better performance under condition of the Nakagami fading channel.