基于Golay互补序列的压缩感知稀疏信道估计算法

该文针对现有基于压缩感知的信道估计方法峰均功率比高、计算量大等问题，使用确定性格雷(Golay)序列作为训练序列对稀疏信道进行信道估计，在接收端实现了对信道冲激响应的估计，给出了估计模型和具体的算法推演，推导了该方法的峰均功率比，并与基于随机高斯序列的压缩感知信道估计方法的性能、峰均功率比和计算量进行了比较。仿真实验表明:格雷序列以及随机高斯序列两种序列都可以重构出稀疏信道非零抽头系数，但是格雷序列对稀疏信道冲激响应的确定性观测估计值的均方误差(MSE)和匹配度性能(Match Rate, MR)均优于随机高斯序列，计算量降低了许多，且在OFDM系统中峰均功率比大大降低。

Compressed Sensing Channel Estimation Algorithm Based on Deterministic Sensing with Golay Complementary Sequences

To deal with problems of large Peak-to-Average Power Ratio (PAPR) and computation complexity in existing channel estimation algorithm based on compressed sensing, Golay complementary sequence is utilized to estimate sparse channel as a deterministic training sequence. Estimation model and algorithm are provided in detail. The PAPR of this method is deduced and its performance, PAPR and computation complexity are compared with Gaussian random sequence. The simulation result indicates that Golay sequence and Gaussian random sequence can reconstruct nonzero tap coefficients. But Golay sequence outperforms Gaussian random sequence both in the Mean Square Error (MSE) and Match Rate (MR) for estimating sparse channel impulse response. And the computation and PAPR are reduced significantly in the OFDM system.