超大规模可重构智能表面混合远-近场信道估计

邵凯; 花凡玉; 王光宇

doi:10.11999/JEIT250306

超大规模可重构智能表面混合远-近场信道估计

doi: 10.11999/JEIT250306 cstr: 32379.14.JEIT250306

邵凯^{1, 2, ,},
花凡玉¹,
王光宇¹

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
移动通信技术重庆市重点实验室重庆 400065

详细信息

作者简介:
邵凯：男，副教授，硕士生导师，研究方向为智能感知与信息系统、信号与信息智能处理等

花凡玉：女，硕士生，研究方向为可重构智能表面、信道估计等

王光宇：男，教授，硕士生导师，研究方向为数字信号处理、滤波器组理论等

通讯作者:
邵凯　shaokai@cqupt.edu.cn

中图分类号: TN929
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- HTML全文浏览量: 47
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- 被引次数: 0
出版历程
- 收稿日期: 2025-04-25
- 修回日期: 2025-08-20
- 网络出版日期: 2025-08-27

Hybrid Far-Near Field Channel Estimation for XL-RIS Assisted Communication Systems

SHAO Kai^{1, 2
, ,},
HUA Fanyu¹,
WANG Guangyu¹

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Mobile Communications Technology, Chongqing 400065, China

摘要

摘要: 超大规模可重构智能表面(XL-RIS)辅助通信系统的信道估计，需解决混合远-近场级联信道建模、远/近场分量区分及参数估计等问题。该文建立了混合远-近场级联信道参数化模型，并针对性地提出两阶段的混合场级联信道参数估计方案：第1阶段估计基站侧的角度参数；第2阶段基于所提出的混合场前向空间平滑降秩多信号分类算法估计RIS侧的远/近场角度参数和近场距离参数，其中根据混合场效应处理远/近场分量，设计了功率谱对比方案区分远/近场分量及路径数量。仿真结果表示，相比于单一远场、近场估计方案和基于混合场正交匹配跟踪算法的估计方案，所提算法可以实现更高的估计精度。
- 超大规模可重构智能表面 /
- 信道估计 /
- 混合远近场 /
- 多信号分类算法
Abstract: Objective With the rapid development of sixth-generation mobile communication, Extra-Large Reconfigurable Intelligent Surfaces (XL-RIS) have attracted significant attention due to their potential to enhance spectral efficiency, expand coverage, and reduce energy consumption. However, conventional channel estimation methods, primarily based on Far-Field (FF) or near-field (NF) models, face limitations in addressing the hybrid far-NF environment that arises from the coexistence of NF spherical waves and FF planar waves in XL-RIS deployments. These limitations restrict the intelligent control capability of RIS technology due to inaccurate channel modeling and reduced estimation accuracy. To address these challenges, this paper constructs a hybrid-field channel model for XL-RIS and proposes a robust channel estimation method to resolve parameter estimation challenges under coupled FF and NF characteristics, thereby improving channel estimation accuracy in complex propagation scenarios. Methods For channel estimation in XL-RIS-aided communication systems, several key challenges must be addressed, including the modeling of hybrid far-NF cascaded channels, separation of FF and NF channel components, and individual parameter estimation. To capture the hybrid-field effects of XL-RIS, a hybrid-field cascaded channel model is constructed. The RIS-to-User Equipment (UE) channel is modeled as a hybrid far-NF channel, whereas the Base Station (BS)-to-RIS channel is characterized under the FF assumption. A unified representation of FF and NF models is established by introducing equivalent cascaded angles for the angle of departure and angle of arrival on the RIS side. The XL-RIS hybrid-field cascaded channel is parameterized through BS arrival angles, RIS-UE cascaded angles, and distances. To reduce the computational complexity of joint parameter estimation, a Two-Stage Hybrid-Field (TS-HF) channel estimation scheme is proposed. In the first stage, the BS arrival angle is estimated using the MUltiple SIgnal Classification (MUSIC) algorithm. In the second stage, a Hybrid-Field forward spatial smoothing Rank-reduced MUSIC (HF-RM) algorithm is proposed to estimate the parameters of the RIS-UE hybrid-field channel. The received signals are pre-processed using a forward spatial smoothing technique to mitigate multipath coherence effects. Subsequently, the Rank-reduced MUSIC (RM) algorithm is applied to separately estimate the FF and NF angle parameters, as well as the NF distance parameter. During this stage, a power spectrum comparison scheme is designed to distinguish FF and NF angles based on power spectral characteristics, thereby providing high-precision angular information to support NF distance estimation. Finally, channel attenuation is estimated using the least squares method. To validate the effectiveness of the proposed hybrid-field channel estimation scheme, comparative analyses are conducted against FF, NF, and the proposed TS-HF-RM schemes. The FF estimation approximates the hybrid-field channel using a FF channel model and estimates FF angle parameters with the MUSIC algorithm, referred to as the TS-FF-M scheme. The NF estimation applies a NF channel model to characterize the hybrid channel and estimates angle and distance parameters using the RM algorithm, referred to as the TS-NF-RM scheme. To further evaluate the estimation performance, additional benchmark schemes are considered, including the Two-Stage Near-Field Orthogonal Matching Pursuit (TS-NOMP) scheme, the Two-Stage Hybrid Orthogonal Matching Pursuit with Prior (TS-HOMP-P) scheme that requires prior knowledge of FF and NF quantities, and the Two-Stage Hybrid Orthogonal Matching Pursuit with No Prior (TS-HOMP-NP) scheme that operates without requiring such prior information. Results and Discussions Compared with the TS-FF-M and TS-NF-RM schemes, the proposed TS-HF-RM approach achieves effective separation and accurate estimation of both FF and NF components by jointly modeling the hybrid-field channel. The method consistently demonstrates superior estimation accuracy across a wide range of Signal-to-Noise Ratio (SNR) conditions (Fig. 4). These results confirm both the necessity of hybrid-field channel modeling and the effectiveness of the proposed estimation scheme. Experimental findings show that the TS-HF-RM approach significantly improves channel estimation performance in XL-RIS-assisted communication systems. Further comparative analysis reveals that the TS-HF-RM scheme outperforms TS-NOMP and TS-HOMP-P by mitigating power leakage effects and overcoming limitations associated with unknown path numbers through distinct processing of FF and NF components. Without requiring prior knowledge of the propagation environment, the proposed method achieves lower Normalized Mean Square Error (NMSE) while demonstrating improved robustness and estimation precision (Fig. 5). Although TS-HOMP-NP also operates without prior field information, the TS-HF-RM scheme provides superior parameter resolution, attributed to its subspace decomposition principle. Additionally, both the TS-HF-RM and TS-HOMP-P schemes exhibit improved performance as the number of pilot signals increases. However, TS-HF-RM consistently outperforms TS-HOMP-P under low-SNR conditions (0 dB). At high SNR (10 dB) with a limited number of pilot signals (<280), TS-HOMP-P temporarily achieves better performance due to its higher sensitivity to SNR. Nevertheless, the proposed TS-HF-RM approach demonstrates greater stability and adaptability under low-SNR and resource-constrained conditions (Fig. 6). Conclusions This study addresses the challenge of hybrid-field channel estimation for XL-RIS by constructing a hybrid-field cascaded channel model and proposing a two-stage estimation scheme. The HF-RM algorithm is specifically designed for accurate hybrid component estimation in the second stage. Theoretical analysis and simulation results demonstrate the following: (1) The hybrid-field model reduces inaccuracies associated with traditional single-field assumptions, providing a theoretical foundation for reliable parameter estimation in complex propagation environments; (2) The proposed TS-HF-RM algorithm enables high-resolution parameter estimation with effective separation of FF and NF components, achieving lower NMSE compared to hybrid-field OMP-based methods.
- Extra-Large Reconfigurable Intelligent Surface (XL-RIS) /
- Channel estimation /
- Hybrid far- and near-field /
- MUltiple SIgnal Classification (MUSIC) algorithm

HTML全文

图 1 XL-RIS辅助系统模型

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图 2 系统框图

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图 3 级联角度和距离估计过程

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图 4 不同信噪比下所提方案和单一远近场方案性能对比

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图 5 不同信噪比下各个方案归一化均方误差性能对比

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图 6 TS-HF-RM和TS-HOMP-P方案性能对比

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1 混合场前向空间平滑降秩MUSIC算法

输入：$ \tilde \varphi _B^{{\text{AOA}}} $，接收信号$ {\boldsymbol{Y}} $，瑞利距离$ {r_{{\mathrm{Ra}}}} $，远场角度集合　$ {\tilde {\boldsymbol{\vartheta}} _f} = \varnothing $和近场角度集合$ {\tilde {\boldsymbol{\vartheta}} _n} = \varnothing$；
输出：级联信道$ {{\tilde {\boldsymbol{H}}}} $。
(1)根据式(19)得到处理的接收信号$ {{\bar {\boldsymbol{Y}}}} $；
(2)根据式(20)得到协方差矩阵$ {{\boldsymbol{R}}_{\bar Y}} $；
(3)根据式(24)得到空间平滑处理的协方差矩阵$ {{\boldsymbol{R}}_S} $；
(4)根据式(28)得到估计的级联角度$ {\tilde \vartheta _l} $，$ l = 1,2, \cdots ,L $；
(5)for $ l = 1,2, \cdots ,L $ do
(a)根据式(29)计算$ P({\tilde \vartheta _l},\infty ) $，根据式(30)计算$ P({\tilde \vartheta _l},{r_{{\mathrm{Ra}}}}) $；
(b)if $ P({\tilde \vartheta _l},\infty ) > P({\tilde \vartheta _l},{r_{{\mathrm{Ra}}}}) $ then
(i)$ {\tilde {\boldsymbol{\vartheta}} _f} = {\tilde{\boldsymbol{ \vartheta}} _f} \cup {\tilde \vartheta _l} $
(c)else
(i)$ {\tilde{\boldsymbol{ \vartheta}} _n} = {\tilde {\boldsymbol{\vartheta}} _n} \cup {\tilde \vartheta _l} $
(d)end
(6)end
(7)根据式(31)计算近场距离$ {{\boldsymbol{\tilde r}}_n} $；
(8)根据式(33)得到信道衰减$ \tilde {\boldsymbol{\beta}} $；
(9)根据式(34)得到级联信道$ {\boldsymbol{\tilde H}} $；

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表 1 仿真参数

参数	数值
BS天线数量$ M $	81
XL-RIS元件数量$ N $	401 ^[20]
载波频率(GHz)	30^[20]
载波波长$ \lambda $(m)	0.01
RIS元件间距$ d $	$ \lambda /4 $^[30]
远场分量路径数$ {L_f} $	2
近场分量路径数$ {L_n} $	4
瑞利距离(m)	200
近场范围(m)	10～80^[20]
导频数量范围	51～401

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表 2 计算复杂度对比

方案	计算复杂度
TS-NOMP	$ O(SQL) + O(NS) + O({K_\theta }{M^2}) $
TS-HOMP-P	$ O(NQ{L_f}) + O(SQ{L_n}) + O(NS) + O({K_\theta }{M^2}) $
TS-HOMP-NP	$ O(NQL) + O(SQL(L + 1)) + O(N(N + S)) + O({K_\theta }{M^2}) $
TS-HF-RM	$ O(N{T^{\text{2}}}) + O({T^3}) + O({K_\theta }{T^2}) + O({L_n}{K_r}{T^2}) $

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参考文献(31)

[1]	XIE Hao and LI Dong. Number configuration for RIS-aided systems: Opportunities and challenges[J]. IEEE Internet of Things Journal, 2025, 12(6): 6396–6417. doi: 10.1109/JIOT.2025.3532892.
[2]	CHEN Zhen, CHEN Gaojie, TANG Jie, et al. Reconfigurable-intelligent-surface-assisted B5G/6G wireless communications: Challenges, solution, and future opportunities[J]. IEEE Communications Magazine, 2023, 61(1): 16–22. doi: 10.1109/MCOM.002.2200047.
[3]	DEMIR Ö T, BJÖRNSON E, and SANGUINETTI L. Efficient channel estimation with shorter pilots in RIS-aided communications: Using array geometries and interference statistics[J]. IEEE Transactions on Wireless Communications, 2025, 24(1): 462–478. doi: 10.1109/TWC.2024.3495226.
[4]	WEI Xiuhong, SHEN Decai, and DAI Linglong. Channel estimation for RIS assisted wireless communications—Part I: Fundamentals, solutions, and future opportunities[J]. IEEE Communications Letters, 2021, 25(5): 1398–1402. doi: 10.1109/LCOMM.2021.3052822.
[5]	JENSEN T L and DE CARVALHO E. An optimal channel estimation scheme for intelligent reflecting surfaces based on a minimum variance unbiased estimator[C]. ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain, 2020: 5000–5004. doi: 10.1109/ICASSP40776.2020.9053695.
[6]	ZHOU Zhengyi, GE Ning, WANG Zhaocheng, et al. Joint transmit precoding and reconfigurable intelligent surface phase adjustment: A decomposition-aided channel estimation approach[J]. IEEE Transactions on Communications, 2021, 69(2): 1228–1243. doi: 10.1109/TCOMM.2020.3034259.
[7]	PAN Cunhua, ZHOU Gui, ZHI Kangda, et al. An overview of signal processing techniques for RIS/IRS-aided wireless systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2022, 16(5): 883–917. doi: 10.1109/JSTSP.2022.3195671.
[8]	WANG Peilan, FANG Jun, DUAN Huiping, et al. Compressed channel estimation for intelligent reflecting surface-assisted millimeter wave systems[J]. IEEE Signal Processing Letters, 2020, 27: 905–909. doi: 10.1109/LSP.2020.2998357.
[9]	邵凯, 鲁奔, 王光宇. 可重构智能表面辅助通信系统时变级联信道估计[J]. 通信学报, 2024, 45(1): 119–128. doi: 10.11959/j.issn.1000-436x.2024028. SHAO Kai, LU Ben, and WANG Guangyu. Time-varying channel estimation in reconfigurable intelligent surface assisted communication system[J]. Journal on Communications, 2024, 45(1): 119–128. doi: 10.11959/j.issn.1000-436x.2024028.
[10]	WU Jiao, KIM S, and SHIM B. Parametric sparse channel estimation for RIS-assisted terahertz systems[J]. IEEE Transactions on Communications, 2023, 71(9): 5503–5518. doi: 10.1109/TCOMM.2023.3285759.
[11]	ARDAH K, GHEREKHLOO S, DE ALMEIDA A L F, et al. TRICE: A channel estimation framework for RIS-aided millimeter-wave MIMO systems[J]. IEEE Signal Processing Letters, 2021, 28: 513–517. doi: 10.1109/LSP.2021.3059363.
[12]	HE Jiguang, WYMEERSCH H, and JUNTTI M. Channel estimation for RIS-Aided mmWave MIMO systems via atomic norm minimization[J]. IEEE Transactions on Wireless Communications, 2021, 20(9): 5786–5797. doi: 10.1109/TWC.2021.3070064.
[13]	YANG Songjie, LYU Wanting, HU Zhenzhen, et al. Channel estimation for near-field XL-RIS-Aided mmWave hybrid beamforming architectures[J]. IEEE Transactions on Vehicular Technology, 2023, 72(8): 11029–11034. doi: 10.1109/TVT.2023.3261340.
[14]	MASOOD K F, TONG Jun, XI Jiangtao, et al. Inductive matrix completion and Root-MUSIC-based channel estimation for intelligent reflecting surface (IRS)-aided hybrid MIMO systems[J]. IEEE Transactions on Wireless Communications, 2023, 22(11): 7917–7931. doi: 10.1109/TWC.2023.3257138.
[15]	LEE J, CHUNG H, CHO Y, et al. Near-field channel estimation for XL-RIS assisted multi-user XL-MIMO systems: Hybrid beamforming architectures[J]. IEEE Transactions on Communications, 2025, 73(3): 1560–1574. doi: 10.1109/TCOMM.2024.3454032.
[16]	AN Jiancheng, YUEN C, DAI Linglong, et al. Near-field communications: Research advances, potential, and challenges[J]. IEEE Wireless Communications, 2024, 31(3): 100–107. doi: 10.1109/MWC.004.2300450.
[17]	CUI Mingyao and DAI Linglong. Channel estimation for extremely large-scale MIMO: Far-field or near-field?[J]. IEEE Transactions on Communications, 2022, 70(4): 2663–2677. doi: 10.1109/TCOMM.2022.3146400.
[18]	GÜRGÜNOĞLU D, KOSASIH A, RAMEZANI P, et al. Performance analysis of a 2D-MUSIC algorithm for parametric near-field channel estimation[J]. IEEE Wireless Communications Letters, 2025, 14(5): 1496–1500. doi: 10.1109/LWC.2025.3547154.
[19]	ZHANG Xiaofei, CHEN Weiyang, ZHENG Wang, et al. Localization of near-field sources: A reduced-dimension MUSIC algorithm[J]. IEEE Communications Letters, 2018, 22(7): 1422–1425. doi: 10.1109/LCOMM.2018.2837049.
[20]	WEI Xiuhong and DAI Linglong. Channel estimation for extremely large-scale massive MIMO: Far-field, near-field, or hybrid-field?[J]. IEEE Communications Letters, 2022, 26(1): 177–181. doi: 10.1109/LCOMM.2021.3124927.
[21]	YANG Wanning, LI Ming, and LIU Qian. A practical channel estimation strategy for XL-MIMO communication systems[J]. IEEE Communications Letters, 2023, 27(6): 1580–1583. doi: 10.1109/LCOMM.2023.3266821.
[22]	PENG Xingxing, ZHAO Lei, JIANG Yuan, et al. Channel estimation for extremely large-scale massive MIMO systems in hybrid-field channel[C]. 2023 IEEE/CIC International Conference on Communications in China (ICCC), Dalian, China, 2023: 1–6. doi: 10.1109/ICCC57788.2023.10233609.
[23]	LI Zhuoran, GAO Zhen, and LI Tuan. Sensing user's channel and location with terahertz extra-large reconfigurable intelligent surface under hybrid-field beam squint effect[J]. IEEE Journal of Selected Topics in Signal Processing, 2023, 17(4): 893–911. doi: 10.1109/JSTSP.2023.3278942.
[24]	CHEN Shaobin, WAN Ziwei, TAO Yi, et al. Hybrid-field full-dimensional channel estimation for reconfigurable intelligent surfaces with extremely-large aperture[C]. 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates, 2024: 1–5. doi: 10.1109/WCNC57260.2024.10571039.
[25]	YANG Songjie, XIE Chenfei, LYU Wanting, et al. Near-field channel estimation for extremely large-scale reconfigurable intelligent surface (XL-RIS)-aided wideband mmWave systems[J]. IEEE Journal on Selected Areas in Communications, 2024, 42(6): 1567–1582. doi: 10.1109/JSAC.2024.3389120.
[26]	WEI Xiuhong, SHEN Decai, and DAI Linglong. Channel estimation for RIS assisted wireless communications—Part II: An improved solution based on double-structured sparsity[J]. IEEE Communications Letters, 2021, 25(5): 1403–1407. doi: 10.1109/LCOMM.2021.3052787.
[27]	HE Jin, SWAMY M N S, and AHMAD M O. Efficient application of MUSIC algorithm under the coexistence of far-field and near-field sources[J]. IEEE Transactions on Signal Processing, 2012, 60(4): 2066–2070. doi: 10.1109/TSP.2011.2180902.
[28]	CHEN Jie, LIANG Yingchang, CHENG H V, et al. Channel estimation for reconfigurable intelligent surface aided multi-user mmWave MIMO systems[J]. IEEE Transactions on Wireless Communications, 2023, 22(10): 6853–6869. doi: 10.1109/TWC.2023.3246264.
[29]	QU Kaiqian, GUO Shuaishuai, YE Jia, et al. Two-stage beamspace MUSIC-based near-field channel estimation for hybrid XL-MIMO[J]. IEEE Communications Letters, 2024, 28(8): 1949–1953. doi: 10.1109/LCOMM.2024.3409411.
[30]	XI Yang, ZHU Fuqiang, ZHOU Binggui, et al. Gridless hybrid-field channel estimation for extra-large aperture array massive MIMO systems[J]. IEEE Wireless Communications Letters, 2024, 13(2): 496–500. doi: 10.1109/LWC.2023.3333531.
[31]	ZHENG Peicong, LYU Xuantao, WANG Ye, et al. Convolutional dictionary learning based hybrid-field channel estimation for XL-RIS-aided massive MIMO systems[J]. IEEE Transactions on Wireless Communications, 2025: 1–1. doi: 10.1109/TWC.2025.3570631. (查阅网上资料,未找到卷期页码信息,请补充).