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可重构智能表面中低复杂度毫米波信道估计算法

蒲旭敏 孙致南 李静洁 黄琼 陈前斌

蒲旭敏, 孙致南, 李静洁, 黄琼, 陈前斌. 可重构智能表面中低复杂度毫米波信道估计算法[J]. 电子与信息学报, 2022, 44(7): 2281-2288. doi: 10.11999/JEIT211602
引用本文: 蒲旭敏, 孙致南, 李静洁, 黄琼, 陈前斌. 可重构智能表面中低复杂度毫米波信道估计算法[J]. 电子与信息学报, 2022, 44(7): 2281-2288. doi: 10.11999/JEIT211602
PU Xumin, SUN Zhinan, LI Jingjie, HUANG Qiong, CHEN Qianbin. A Low Complexity Millimeter Wave Channel Estimation Algorithm in Reconfigurable Intelligent Surface[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2281-2288. doi: 10.11999/JEIT211602
Citation: PU Xumin, SUN Zhinan, LI Jingjie, HUANG Qiong, CHEN Qianbin. A Low Complexity Millimeter Wave Channel Estimation Algorithm in Reconfigurable Intelligent Surface[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2281-2288. doi: 10.11999/JEIT211602

可重构智能表面中低复杂度毫米波信道估计算法

doi: 10.11999/JEIT211602
基金项目: 国家自然科学基金(61701062),中国博士后科学基金(2019M651649),江苏省博士后科研基金(2018K041c),重庆市教育委员会科学技术研究项目(KJQN202100649)
详细信息
    作者简介:

    蒲旭敏:男,1983年生,副教授,硕士生导师,研究方向为新一代无线通信理论,聚焦其信息理论、信道估计和信号检测

    孙致南:男,1997年生,硕士生,研究方向为大规模MIMO信号检测、信道估计

    李静洁:女,1998年生,硕士生,研究方向为可重构智能表面、信道估计

    黄琼:女,1971年生,教授,硕士生导师,研究方向为宽带通信网理论、5G/6G移动通信网络技术

    陈前斌:男,1967年生,教授,博士生导师,研究方向为个人通信、多媒体信息处理与传输、下一代移动通信网络

    通讯作者:

    蒲旭敏 puxm@cqupt.edu.cn

  • 中图分类号: TN92

A Low Complexity Millimeter Wave Channel Estimation Algorithm in Reconfigurable Intelligent Surface

Funds: The National Natural Science Foundation of China (61701062), The China Postdoctoral Science Foundation (2019M651649), The Jiangsu Planned Projects for Postdoctoral Research Funds (2018K041c),The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202100649)
  • 摘要: 针对可重构智能表面(RIS)辅助的大规模多输入多输出(MIMO)毫米波系统中信道估计复杂度高的问题,该文提出一种低复杂度的信道估计算法。在该方案中,将RIS部分元素连接射频(RF)链,分离估计基站/用户和RIS之间的信道,分开获取信道有助于提升用户移动性场景下信道估计的灵活性。在所考虑系统中,首次使用低复杂度的2维快速傅里叶变换(2D-FFT)算法对角度进行估计,并考虑信号补零以获得更加精准的角度估计值,最后利用信号2维空间谱的谱峰和其对应的辐角得到路径增益估计。仿真结果表明,该算法达到了优良的信道估计性能,且在确保信道估计性能的系统参数设置下,该算法具有压倒性的复杂度优势。
  • 图  1  RIS辅助无线通信系统

    图  2  UE到RIS水平RF链接收信号的2维空间谱

    图  3  不同补零维度及RF链个数下$ {H_k} $信道估计性能对比

    图  4  信道${{\boldsymbol{H}}_k}$与信道$ {\mathbf{G}} $估计性能对比

    图  5  不同算法估计信道${{\boldsymbol{H}}_k}$性能对比

    表  1  本文算法流程表

     算法:基于2D-FFT的RIS辅助通信系统信道估计
     (1) 输入:RIS射频链接收信号:${\boldsymbol{Y}}_k^x$,${\boldsymbol{Y}}_k^y$;BS接收信号:${\boldsymbol{Y}}_{ {\text{BS} } }^x$ ,${\boldsymbol{Y}}_{ {\text{BS} } }^y$;导频: ${\boldsymbol{\varGamma}} _k^{}$,${\boldsymbol{\varGamma}} _{\text{R} }^x$,${\boldsymbol{\varGamma}} _{\text{R} }^y$;反射矩阵:${\boldsymbol{\varPhi}}$
     (2) 所有接收信号与导频信号解相关,如${\boldsymbol{Y} }'^x_k = {\boldsymbol{Y} }_k^x{\boldsymbol{\varGamma} } _k^x = {\boldsymbol{H} }_k^x + {\boldsymbol{N} }'^x_k$
     (3) 信号补0处理,并对其进行2D-FFT得到峰值点位置。如${\boldsymbol{Y} }'^x_k $补0得到${\boldsymbol{Y}}'^{x,{\text{pad} } }_k$,对其进行2D-FFT变换得到第$l_a $条路径峰值位置$ (m_{l_a}^x,n_{l_a}^x)$
     (4) 得到链路对应角度估计值,如
     $\hat \phi _{ { {\mathbf{H} }_k} }^{ {l_a} } = \arcsin \left(\dfrac{\lambda }{ { {d_{\text{R} } } } } \cdot \dfrac{ {m_{ {l_a} }^x} }{ {L'} }\right),{\text{ } }\hat \varphi _{ { {\mathbf{H} }_k} }^{ {l_a} } = \arcsin \left(\dfrac{\lambda }{ { {d_{\text{U} } } } } \cdot \dfrac{ {n_{ {l_a} }^x} }{ {M'} }\right)$
     并以相同思路分别得到RIS竖直RF链AOA的仰角估计值$\hat \theta _{{{\mathbf{H}}_k}}^{{l_a}}$,BS端的AoA估计值$\hat \psi_{l_b} $,RIS到BS的仰角和方位角的估计值$\gamma_{l_b} $, $ \hat\varphi_{l_b} $
     (5) 依据补0后接收信号空间谱得到路径增益,如$ {\hat a_{{l_a}}}{\text{ = }}{\hat \beta _{{l_a}}}{{\text{e}}^{{\text{j}}{{\hat \alpha }_{{l_a}}}}} $其中
     ${\hat \beta _{ {l_a},k} } \approx \dfrac{1}{ {LM} }{\text{|} }\tilde {\boldsymbol{Y}}'^{x,{\text{pad} } }_k(m_{ {l_a} }^x,n_{ {l_a} }^x){\text{|, } }{\hat \alpha _{ {l_a} } } \approx \arctan \dfrac{ { {\text{imag} }(\tilde {\boldsymbol{Y}}'^{x,{\text{pad} } }_k(m_{ {l_a} }^x,n_{ {l_a} }^x))} }{ { {\text{real} }(\tilde {\boldsymbol{Y}}'^{x,{\text{pad} } }_k(m_{ {l_a} }^x,n_{ {l_a} }^x))} }$
     并以相同思路得到RIS到BS端路径增益估计$\hat b_{l_b} $
     (6) 输出:依据以上参数估计值,得到信道估计值
     ${\hat {\boldsymbol{H}}_k} = \displaystyle\sum\limits_{ {l_a} = 0}^{ {L_a} - 1} { { {\hat a}_{ {l_a} } }a_{ {\text{UR} } }^{}(\hat \theta _{ { {\mathbf{H} }_k} }^{ {l_a} },\hat \phi _{ { {\mathbf{H} }_k} }^{ {l_a} })a_{\text{U} }^{\text{H} }(\hat \varphi _{ { {\mathbf{H} }_k} }^{ {l_a} })} {\text{, } }\hat G{\text{ = } }\sum\limits_{ {l_b} = 0}^{ {L_b} - 1} { { {\hat b}_{ {l_b} } }{a_{\text{B} } }({ {\hat \psi }_{ {l_b} } })a_{ {\text{RB} } }^{\text{H} }({ {\hat \gamma }_{ {l_b} } },{ {\hat \varphi }_{ {l_b} } })}$
    下载: 导出CSV

    表  2  不同算法计算复杂度对比

    信道估计算法计算复杂度
    基于子空间的信道估计算法$\begin{gathered} {\text{ } }O(\frac{ {32} }{3}({N^3} + {P^3}) + ({P^2} + {N^2})(2T + 2{L_a} - 2) + \\ +PM(2T + 2P - 2) + NP(2T + 2N - 2)) \\ \end{gathered}$
    MUSIC+ML$\begin{gathered} {\text{ } }O(\frac{ {52} }{3}({(LT)^3} + {(NT)^3}) + 2({(NT)^2} + {(LT)^2}) + 2{L_a}{D_{ {\text{MUSIC} } } }(8MT - 4T \\ + 4({(LT)^2} + {(NT)^2}) - 4(L{L_a}T + N{L_a}T) + 2T(L + N) - 2) + 12L_a^3 \\ +{\text{2} }L_a^2(4(L + N) + 8T - 6) + 2{L_a}(2 - 2N + 2T(L + N) + 6T(M + L) - 8T)) \\ \end{gathered}$
    Root-MUSIC+ML$\begin{gathered} {\text{ } }O(26({(LT)^3} + {(NT)^3}) + 3T({L^2} + {N^2}) + 18L_a^3 + {\text{3} }L_a^2(4(L + N) + 8T - 6) \\ + 3{L_a}(2 - L - N + 2T(N + L) + 6T(M + L) - 8T)) \\ \end{gathered}$
    本文算法${\text{ }}2({L^2}(M + N) + 3(M\log (M) + N\log (N)) + 6L\log (L) + 2L(2T - 1)(M + N)$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-30
  • 修回日期:  2022-06-09
  • 网络出版日期:  2022-06-20
  • 刊出日期:  2022-07-25

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