认知无线电网络中宽频谱的信号分离算法

Novel signal separation algorithm based on compressed sensing for wideband spectrum sensing in cognitive radio networks

在认知无线电网络中,认知用户随机接入宽带频谱进行数据传输,但是这样很容易受到恶意用户的干扰,这些恶意用户随意地接入共享频带进行信号传输,这些信号会干扰主用户和认知用户。为此,提出了一种基于压缩感知的信号分离方法。该方法可以很好地从宽带信号中分离出恶意用户信号。算法主要采用以下三个步骤:(1)所有认知用户采用压缩感知技术从宽带频谱中恢复各信号;(2)认知用户将分离的信号发送到融合中心,融合中心通过小波边缘检测的方法确定频谱边缘,并按照边缘特性将频谱分成若干频段;(3)融合中心根据具体特征对每个子频段进行信号分离。分析和仿真结果表明,这种新的基于压缩感知的宽频带信号分离方法能很好地从宽带信号中将含有恶意用户干扰的混合信号分离出来。

In cognitive radio networks, since cognitive terminals use the shared wideband frequency spectrum for data transmissions, they are susceptible to malicious denial-of-service attacks, where adversaries try to corrupt communication by actively transmitting interference signals. To address this issue, this paper proposes a novel signal separation algorithm based on compressed sensing, which can not only recover the entire spectrum but also separate mixed occupying signals. Specifically, the proposed algorithm is executed following three steps： （1）each cognitive terminal attempts to recover all signals over entire wideband spectrum employing compressed sensing technique; （2）all cognitive terminals send their recovered signals to the fusion center where wavelet edge detection method is adopted to locate spectrum edges of these signals and then divide the entire spectrum into several sub-bands; （3）the fusion center separates its received signals on each spectrum sub-band into different categories according to their features. Both analytical and simulation results indicate that this novel compressed sensing based algorithm can effectively separate wideband signals at a low cost and combat interference of the malicious terminals in cognitive radio networks as well.