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认知无线电频谱感知技术要求能够快速、准确地对高达上千兆赫兹的带宽进行频谱感知,它需要很高速率的模数转换器(A/D),这对传统的工作在Nyquist采样率下的频谱估计方法提出了很大的挑战。利用实际信号频谱在开放式频谱接入环境中的稀巯性,提出了基于压缩采样和小波包分解的宽带频谱感知方案。通过SIMULINK仿真表明,该方法对宽带信号能以远远低于Nyquist采样率的速率采样,并且能够精确地估计出可用信道列表。
Cognitive Radio Spectrum Sensing requires the ability to quickly and accurately spectrum sense up to a gigahertz bandwidth, requiring a very high rate A / D (A / D), which traditionally operates at the Nyquist sampling rate The method of spectrum estimation poses a great challenge. Using the thinning-out of the actual signal spectrum in an open spectrum access environment, a broadband spectrum sensing scheme based on compressed sampling and wavelet packet decomposition is proposed. SIMULINK simulation shows that this method can sample wideband signals at a rate much lower than the Nyquist sampling rate and can accurately estimate the available channel list.