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Downlink channel estimation of FDD based massive MIMO using spatial partial-common sparsity modeling
Physical Communication ( IF 2.2 ) Pub Date : 2020-05-29 , DOI: 10.1016/j.phycom.2020.101138
N. Shalavi , M. Atashbar , M. Mohassel Feghhi

Downlink channel estimation in FDD massive MIMO systems is a challenge in 5G wireless communication systems. Using orthogonal pilots for downlink channel estimation leads to the pilot overhead problem. To cope with this problem, spatio-temporal common sparsity feature of delay domain beside the compressive sensing algorithm has used for channel estimation. In a practical affair, the spatial common sparsity of the adjacent antennas groups is not entirely separate. In this paper, we model the FDD massive MIMO downlink frequency selective channel estimation problem by a spatial partial-common sparsity, in which it is assumed that the spatial sparsity pattern of antennas in each group has a common part and an uncommon part. For the proposed model, we design a proper pilot sequence, and finally, we propose an estimation method associated with this model to solve the problem. Our proposed method has better NMSE and BER performance than reference methods in the same pilot overhead ratio, which is shown in the simulation results.



中文翻译:

基于空间部分公共稀疏性模型的基于FDD的大规模MIMO的下行链路信道估计

FDD大规模MIMO系统中的下行链路信道估计是5G无线通信系统中的一个挑战。使用正交导频进行下行链路信道估计会导致导频开销问题。为了解决该问题,除了压缩感测算法之外,延迟域的时空公共稀疏特征已经用于信道估计。在实际操作中,相邻天线组的空间公共稀疏性不是完全分开的。在本文中,我们通过空间部分公共稀疏性对FDD大规模MIMO下行链路频率选择性信道估计问题进行建模,其中假设每组天线的空间稀疏性模式具有公共部分和不公共部分。对于建议的模型,我们设计了适当的导频序列,最后,我们提出了与此模型关联的估计方法来解决该问题。仿真结果表明,在相同的导频开销比下,我们的方法具有比参考方法更好的NMSE和BER性能。

更新日期:2020-05-29
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