基于压缩感知的自适应加权匹配追踪算法

压缩感知（compressed sensing，CS）技术通过减少发射导频数来提高频谱的利用率。将CS技术应用于导频辅助的稀疏度未知的正交频分复用（orthogonal frequency division multiplexing，OFDM）信道估计中，提出一种自适应加权匹配追踪（CS-based adaptive weighting &matching pursuit，AWMP）算法。该算法使用自适应加权、匹配追踪的方法估计信道时域脉冲响应，按照估计信噪比和匹配原则，利用多次迭代进行自适应加权和寻找最佳稀疏度，实现未知信道稀疏度与信噪比的情况下，准确估计信道信息。仿真验证表明，与传统的信道估计算法相比，采用基于AWMP的信道估计方法，能够利用较少的导频信息获得更低的误码率和均方误差。

Adaptive weighting & matching pursuit algorithm based on compressed sensing

Compressed Sensing (CS) technology can improve the spectrum efficiency by reducing transmitted pilots. In order to estimate the pilot-aided and sparse-unknown channel utilizing compressed sensing in OFDM system, an adaptive weighting & matching pursuit (AWMP) algorithm is proposed. The algorithm estimates the time-domain channel impulse response by using the proposed method, weights adaptively and searches optimal sparse degree after iteration by the estimated signal to noise ratio (SNR) and the matching principle. Then the channel information is estimated accurately without knowing SNR and sparse degree. Simulation indicates that, compared to the traditional channel estimation algorithm, the method proposed here can obtain lower bit-error-rate (BER) and mean square error (MSE) with fewer pilots.