Ding Yong, Deng Honggao, Xie Yuelei, Wang Haitao, Sun Shaoshuai
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.
State and Local Joint Engineering Research Center for Satellite Navigation and Location Service, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel). 2024 Jun 1;24(11):3581. doi: 10.3390/s24113581.
For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.
对于高移动性场景下的正交频分复用（OFDM）系统，时变多径信道的估计不仅误差大，影响系统性能，而且需要大量导频，导致频谱效率低。为了解决这些问题，我们利用多个OFDM符号持续时间内宽带无线信道的时间相关性和联合延迟稀疏性，提出了一种基于分布式压缩感知和多符号复指数基扩展模型（MS-CE-BEM）的时变多径信道估计方法。此外，在所提方法中，采用具有导频载波间干扰（ICI）自抵消功能的稀疏导频模式来减少MS-CE-BEM的输入参数误差，并引入对称扩展技术来减少建模误差。仿真结果表明，与现有方法相比，该方法在稀疏时变信道的信道估计和频谱利用方面具有优越的性能。
