为了提高MIMO-OFDM系统稀疏信道估计的准确度及减少导频子载波的数目，利用频率选择性衰落信道的冲激响应在时域具有稀疏性的先验信息，将MIMO-OFDM系统的信道估计建模为压缩感知框架里受到噪声干扰的复数信号重构，提出了分别基于稀疏度自适应匹配追踪(sparsity adaptive matching pursuit, SAMP)和
变量分离近似稀疏重构(sparse reconstruction by separable approximation, SRSA)的两种MIMO-OFDM系统稀疏信道估计方法. 仿真结果表明：与传统最小二乘法的信道估计相比，在相同信噪比条件下能获得相等的估计性能，且这两种方法不必将信道的稀疏度作为先验知识，也能减少40%的导频子载波. 在估计准确度方面，基于SAMP的方法优于采用SRSA的方法，前者的MSE和BER性能更接近Cramer-Rao界；但在算法参数设置方面，后者比前者在实际应用中更容易准确设定.
To improve accuracy of sparse channel estimation and reduce the pilot number in MIMO-OFDM systems, we use the sparse prior information of the channel impulse response in the time domain, and model the estimation of frequency selective fading channel for MIMO-OFDM systems as the reconstruction of complex sparse signal interfered by noise in compressed sensing. Two methods of sparse channel estimation in MIMOOFDM systems are proposed, based on sparsity adaptive matching pursuit (SAMP) and sparse reconstruction by separable approximation (SRSA), respectively. Simulation shows that, under the same signal-to-noise ratio and for the same performance of MSE and BER without prior information of the sparsity, the two proposed methods can reduce pilot signals by 40% as compared to the conventional least square method. In the two methods, the one based on SAMP runs faster and is closer to the Cramer-Rao bound, while parameters of the one based on SRSA are easier to be set in practical applications.
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