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压缩感知(CS)理论指出,如果信号在某个变换域内是稀疏的或可压缩的,那么就可以用与变换基不相干的低维线性观测矩阵实现信号的压缩测量。压缩感知充分利用信号固有的稀疏性或可压缩性,以远低于奈奎斯特频率,直接对信号中的重要信息进行采样,此时,采样速率不再决定于信号的带宽,而是决定于信号的结构和内容中所包含的信息,或者说是信号的信息速率。这种新型的信息获取方式带来了信号处理技术的革新,在各类模拟和数字系统中得到了广泛的应用。在无线通信系统的应用主要包括认知无线电、稀疏信道估计、无线传感器网络、阵列信号处理等方面。
Compressed sensing (CS) theory points out that if the signal is sparse or compressible in a transform domain, the signal can be compressed and measured using a low-dimensional linear observation matrix that is independent of the transform basis. Compressed sensing takes full advantage of the inherent sparsity or compressibility of the signal to directly sample important information in the signal far below the Nyquist frequency, at which point the sampling rate no longer depends on the bandwidth of the signal but rather on the decision The information contained in the structure and content of the signal, or the informational rate of the signal. This new approach to information acquisition has revolutionized signal processing and has found wide application in a variety of analog and digital systems. Applications in wireless communication systems mainly include cognitive radio, sparse channel estimation, wireless sensor networks, array signal processing and so on.