基于Golay互补序列的压缩感知稀疏信道估计算法

发布时间：2018-01-07 10:36

本文关键词：基于Golay互补序列的压缩感知稀疏信道估计算法　出处：《电子与信息学报》2016年02期 　论文类型：期刊论文

【摘要】：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。
[Abstract]:In order to solve the problems of high peak-to-average power ratio (PAPR) and large computational complexity of existing channel estimation methods based on compressed sensing, the deterministic Gray Grey Golay-sequence is used as the training sequence to estimate the sparse channel. The estimation of channel impulse response is realized at the receiving end, the estimation model and algorithm are given, and the peak-to-average power ratio of this method is deduced. And the performance of the compressed perceptual channel estimation method based on random Gao Si sequence is also discussed. The simulation results show that both Gray sequence and random Gao Si sequence can reconstruct the non-zero tap coefficient of sparse channel. However, the mean square error (MSE) and match Rateen MRs of grey sequence for impulse response of sparse channel are better than those of random Gao Si sequence. The computational complexity is much reduced, and the peak-to-average power ratio is greatly reduced in OFDM systems.
【作者单位】：湘潭大学智能计算与信息处理教育部重点实验室;
【基金】：国家自然科学基金(61372127) 湖南省自然科学基金(13JJ3065)~~
【分类号】：TN92
【正文快照】： 1引言在无线通信系统中,接收端需要利用信道状态信息进行信道均衡与相干检测,准确的信道估计是其他许多信号处理的前提,其性能直接影响整个系统的信道容量和误码率等,因此信道估计是无线通信系统中最重要的技术之一。按照是否在发射端需要发送辅助导频,信道估计技术可以分成两

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