利用MIMO-Alamouti方案改进认知无线电频谱识别性能

Mohamed Ismail Ibrahim, Dina M. Ellaithy
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摘要

本文研究了多输入多输出(MIMO)-Alamouti方案在认知无线电(CR)频谱感知中的高效执行。因此,使用MIMO-Alamouti方案可以提高整体性能和检测概率。此外,在低信噪比下,采用不同频谱识别技术之间的协同频谱识别算法,提高了检测概率,也解决了隐节点问题。利用Matlab软件对不同方案的检测概率与信噪比进行了仿真。在信噪比(SNR)为−15 dB,虚警概率(Pf)为0.1的情况下,检测概率(Pd)比传统技术提高50%。在信噪比为−10 dB、辅助用户数量为5时,在虚警概率为0.1的情况下,与采用多数原则的普通频谱感知方法相比,检测概率至少提高10%。
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Improvement the Performing of Spectrum Distinguishing in Cognitive Radio using MIMO-Alamouti Scheme
This paper exploits the efficient performing of the Multiple Input Multiple Output (MIMO)-Alamouti scheme for spectrum sensing in cognitive radio (CR). Consequently, enhancement in the overall performance and the detection probability by using the MIMO-Alamouti scheme is achieved. Moreover, at low signal-to-noise ratio (SNR), the cooperative spectrum distinguishing algorithm among the different spectrum distinguishing techniques is employed to raise the probability of detection and also solving the hidden node problem. Matlab software is used to simulate the detection probability versus SNR for different schemes. Up to 50% enhancement in detection probability (Pd) as compared with the conventional technique under signal to noise ratio (SNR) equals −15 dB and false alarm probability (Pf) equals 0.1. As compared with the common spectrum sensing approach in case of the majority rule, at least 10% advance in the probability of detection at false alarm probability equals 0.1 under SNR equals −10 dB and the number of secondary user (SU) equals 5.
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