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arXiv 2104.01221cs.ITmath.IT
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智能反射面辅助无线通信信道估计

Channel Estimation for Intelligent Reflecting Surface Assisted Wireless Communications

Mangqing Guo, M. Cenk Gursoy

摘要:

研究了智能反射面(IRS)辅助无线通信系统中的最小均方误差(MMSE)信道估计问题。在所考虑的设置中,从基站(BS)到用户的等效信道矩阵的每个行向量被示出为贝塞尔$K$分布,并且所有这些行向量彼此独立。通过引入高斯尺度混合模型,我们得到了一个封闭形式的等效信道的MMSE估计的表达式,并确定分析上界和下界的均方误差。利用中心极限定理,我们对MMSE估计进行了渐近分析,并证明了当IRS的反射单元数趋于无穷大时,MMSE估计的均方误差的上界等于MMSE估计的渐近均方误差.数值模拟表明,在中等信噪比和中等单元数下,上、下界之间的距离很小,且几乎重合.

英文摘要:

In this paper, the minimum mean square error (MMSE) channel estimation for intelligent reflecting surface (IRS) assisted wireless communication systems is investigated. In the considered setting, each row vector of the equivalent channel matrix from the base station (BS) to the users is shown to be Bessel $K$ distributed, and all these row vectors are independent of each other. By introducing a Gaussian scale mixture model, we obtain a closed-form expression for the MMSE estimate of the equivalent channel, and determine analytical upper and lower bounds on the mean square error. Using the central limit theorem, we conduct an asymptotic analysis of the MMSE estimate, and show that the upper bound on the mean square error of the MMSE estimate is equal to the asymptotic mean square error of the MMSE estimation when the number of reflecting elements at the IRS tends to infinity. Numerical simulations show that the gap between the upper and lower bounds are very small, and they almost overlap with each other at medium signal-to-noise ratio (SNR) levels and moderate number of elements at the IRS.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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