基于压缩感知的自适应导频信道估计

Adaptive Pilot Channel Estimation Based on Compressive Sensing

在无线通信系统中,如何提升信道估计的准确度对提升无线通信的系统性能至关重要。在信道估计中,导频开销占据了较多的频谱资源,且传统的信道估计算法不能根据信道状态实时调整信道估计中所需要的导频数量。而压缩感知信道估计算法,可以利用无线信道的稀疏特性,提高信道估计的精确度,减少导频子载波的开销。基于此特点,将压缩感知与信道估计相结合,研究了基于压缩感知的稀疏度未知情况的信道估计,并提出一种适用于LTE-A系统的导频自适应信道估计算法。仿真结果表明:与传统的LS信道估计和LMMSE信道估计相比,所提出的导频自适应算法能够将导频数量减少40%左右,并能获得更准确的信道估计性能。

In wireless communication system, how to improve the accuracy of channel estimation is very important for improvement of the performance for wireless communication system. In the channel estimation, the pilot overhead occupies a large amount of spectrum resources,and the traditional channel estimation algorithm cannot adjust the pilot channel estimation according to the channel state. The compressed sensing channel estimation algorithm, taking advantage of the sparse characteristic of wireless channel, improves the accuracy of channel estimation and decreases the pilot overhead. Based on these features ,combined compressive sensing with channel estimation, investigating channel estimation with unknown channel sparsity based on compressive sensing, an adaptive pilot channel estimation algorithm is put forward for LTE-Advanced systems. Simulation shows that compared with the traditional LS and LMMSE,it can reduce the number of pilot by 40% and obtain more accurate channel estimation performance.