基于分布式压缩感知的信道估计导频优化策略

Pilot optimization strategy of channel estimation based on distributed compressed sensing

为了保证为LTE-R系统用户提供可靠的无线通信服务,需要通过信道估计获取信道状态信息。在高速移动性场景下,无线信道呈现频率-时间双选择性,若要实现信道估计,则需引入大量导频。针对上述问题,提出一种结合分布式压缩感知理论的信道估计导频优化方案。首先,根据时延域中无线信道的稀疏特性挖掘基函数系数之间的联合稀疏性并对估计方程进行去耦处理。接着,引入分布式压缩感知理论,获得一种能够抑制子载波间干扰的新型导频图样。仿真结果表明,对导频图样的优化处理,可使信道估计方案的系统性能显著优于传统方案。

In order to provide the users of long term evolution for railway（LTE-R） system with reliable wireless communication service, the channel state information need to be obtained by channel estimation. In high-speed mobility scenarios, the wireless channel is a time-frequency doubly-selective channel. In order to estimate the channel, a large number of pilots will be introduced. To tackle this problem, a pilot optimization strategy of channel estimation based on a distributed compressed sensing （DCS） method was proposed. First of all, according to the channel sparsity in the delay domain, the joint sparsity between basis function coefficients was excavated. And the estimating equation was decoupled. Then, a novel sparse pilot pattern based on the DCS theory which can remove inter-carrier interference was solved out. The simulation results show that the system performance of channel estimation strategy is significantly superior to the existing ones by the optimization of pilot pattern.