基于压缩采样匹配追踪算法的混合场信道估计方案

Hybrid-Field Channel Estimation Scheme Based on Compressed Sampling Matching Pursuit Algorithm

针对现有的远场和近场信道估计方案不能直接用于精确估计混合场信道状态信息的问题,提出了一种有效的基于压缩采样匹配追踪(compression sampling matching pursuit, CoSaMP)算法的混合场信道估计方案。通过精确建模混合场信道模型来捕捉其远场和近场区域可能存在不同的散射体这一特征,分别对其远场和近场路径分量进行估计。结果表明,所提方案与现有其他信道估计方案相比,在相同低导频开销情况下,该方案能获得更高的归一化均方误差性能和更高的系统和速率,计算复杂度较低。可见,所提出的方案具有很好的参考价值。

Aiming at the problem that the existing far-field and near-field channel estimation schemes would not be directly used to accurately estimate the mixed-field channel state information, an effective mixed-field channel estimation scheme based on compression sampling matching pursuit(CoSaMP) was proposed. The hybrid-field channel model was accurately modeled to capture the feature that different scatterers may exist in the far-field and near-field regions, and the far-field and near-field path components were estimated respectively. The results show that compared with other channel estimation schemes, the proposed scheme can obtain better normalized mean square error performance and lower computational complexity under the same low pilot overhead. It is concluded that the proposed scheme has good reference value.