智能反射面通信近邻互耦模型与可调阻抗优化方法

吴炜; 王文鼐

doi:10.11999/JEIT251109

智能反射面通信近邻互耦模型与可调阻抗优化方法

doi: 10.11999/JEIT251109 cstr: 32379.14.JEIT251109

吴炜,
王文鼐^,

1.
南京邮电大学通信与信息工程学院南京 210000
2.
宽带无线通信与传感技术教育部重点实验室南京 210000

基金项目: 南京邮电大学校引进人才科研启动基金项目(自然科学)(NY222118)

详细信息

作者简介:
吴炜：女，讲师，研究方向为无线宽带通信网络、移动通信

王文鼐：男，教授，研究方向为无线宽带通信网络、网络仿真与性能评估

通讯作者:
王文鼐　wangwn@njupt.edu.cn

中图分类号: TN92
计量
- 文章访问数: 99
- HTML全文浏览量: 52
- PDF下载量: 10
- 被引次数: 0
出版历程
- 收稿日期: 2025-10-20
- 修回日期: 2025-11-25
- 录用日期: 2025-12-02
- 网络出版日期: 2025-12-10

Neighboring Mutual-Coupling Channel Model and Tunable-Impedance Optimization Method for Reconfigurable-Intelligent-Surface Aided Communications

WU Wei,
WANG Wennai^,

1.
College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210000, China
2.
Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210000, China

Funds: The Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications (NY222118)

摘要

摘要: 智能反射面(RIS)通常由大量可编程反射单元密集排布而成，当反射单元间距小于入射信号半波长时，电磁互耦效应会显著影响RIS部署的整体性能。为此，该文针对RIS辅助的无线通信系统，研究基于近邻互耦矩阵的简化信道模型以及可调阻抗优化方法。首先，依据互阻抗强度随间隔单元数增加而快速衰减的电磁特性，提取紧邻和次紧邻互耦参数，并结合对应的映射矩阵构建近邻互耦矩阵；其次，在远场条件下，基于等效耦合距离对收发端与RIS间互阻抗计算表达式进行简化，进而建立低复杂度互耦感知信道模型。进一步，基于简化模型并采用阻抗分解法，推导RIS可调阻抗的最优闭式解，其求解复杂度显著低于诺伊曼级数近似算法，并且不受反射单元间距和数量影响。仿真结果表明，所提信道模型和阻抗优化方法在反射单元间距小于等于1/4信号波长时具备较高的准确性和有效性。
- 智能反射面 /
- 互耦效应 /
- 信道建模 /
- 可调阻抗优化
Abstract: Objective Reconfigurable Intelligent Surfaces (RIS) attract increasing attention due to their ability to controllably manipulate electromagnetic wave propagation. A typical RIS consists of a dense array of Reflecting Elements (REs) with inter-element spacing no greater than half a wavelength, under which electromagnetic mutual coupling inevitably occurs between adjacent REs. This effect becomes more pronounced when the element spacing is smaller than half a wavelength and can significantly affect the performance and efficiency of RIS-assisted systems. Accurate modeling of mutual coupling is therefore essential for RIS optimization. However, existing mutual-coupling-aware channel models usually suffer from high computational complexity because of the large dimensionality of the mutual-impedance matrix, which restricts their practical use. To address this limitation, a simplified mutual-coupling-aware channel model based on a sparse neighboring mutual-coupling matrix is proposed, together with an efficient optimization method for configuring RIS tunable impedances. Methods First, a simplified mutual-coupling-aware channel model is established through two main steps. (1) A neighboring mutual-coupling matrix is constructed by exploiting the exponential decay of mutual impedance with inter-element distance. (2) A closed-form approximation of the mutual impedance between the transmitter or receiver and the REs is derived under far-field conditions. By taking advantage of the rapid attenuation of mutual impedance as spacing increases, only eight or three mutual-coupling parameters, together with one self-impedance parameter, are retained. These parameters are arranged into a neighboring mutual-coupling matrix using predefined support matrices. To further reduce computational burden, the distance term in the mutual-impedance expression is approximated by a central value under far-field assumptions, which allows the original integral formulation to be simplified into a compact analytical expression. Based on the resulting channel model, an efficient optimization method for RIS tunable impedances is developed. Through impedance decomposition, a closed-form expression for the optimal tunable-impedance matrix is derived, enabling low-complexity RIS configuration with computational cost independent of the number of REs. Results and Discussions The accuracy and computational efficiency of the proposed simplified models, as well as the effectiveness of the proposed impedance optimization method, are validated through numerical simulations. First, the two simplified models are evaluated against a reference model. The first simplified model accounts for mutual coupling among elements separated by at most one intermediate unit, whereas the second model considers only immediately adjacent elements. Results indicate that channel gain increases as element spacing decreases, with faster growth observed at smaller spacings (Fig. 4). The modeling error between the simplified models and the reference model remains below 0.1 when the spacing does not exceed λ/4, but increases noticeably at larger spacings. Error curves further show that the modeling errors of both simplified models become negligible when the spacing is below λ/4, indicating that the second model can be adopted to further reduce complexity (Fig. 6). Second, the computational complexity of the proposed models is compared with that of the reference model. When the number of REs exceeds four, the complexity of computing the mutual-coupling matrix in the reference model exceeds that of the proposed neighboring mutual-coupling model. As the number of REs increases, the complexity of the reference model grows rapidly, whereas that of the proposed model remains constant (Fig. 5). Finally, the proposed impedance optimization method is compared with two benchmark methods (Fig. 7, Fig. 8). When the element spacing is no greater than λ/4, the channel gain achieved by the proposed method approaches that of the benchmark method. As the spacing increases beyond this range, a clear performance gap emerges. In all cases, the proposed method yields higher channel gain than the coherent phase-shift optimization method. Conclusions The integration of a large number of densely arranged REs in a RIS introduces notable mutual coupling effects, which can substantially influence system performance and therefore must be considered in channel modeling and impedance optimization. A simplified mutual-coupling-aware channel model based on a neighboring mutual-coupling matrix has been proposed, together with an efficient tunable-impedance optimization method. By combining the neighboring mutual-coupling matrix with a simplified mutual-impedance expression derived under far-field assumptions, a low-complexity channel model is obtained. Based on this model, a closed-form solution for the optimal RIS tunable impedances is derived using impedance decomposition. Simulation results confirm that the proposed channel model and optimization method maintain satisfactory accuracy and effectiveness when the element spacing does not exceed λ/4. The proposed framework provides practical theoretical support and useful design guidance for analyzing and optimizing RIS-assisted systems under mutual coupling effects.
- Reconfigurable Intelligent Surface (RIS) /
- Mutual-coupling effect /
- Channel modeling /
- Reconfigurable impedance optimization

HTML全文

图 1 RE间互耦效应及等效阻抗示意图

下载: 全尺寸图片幻灯片

图 2 互耦强度随间隔单元数变化趋势及近邻RE示意图

下载: 全尺寸图片幻灯片

图 3 映射矩阵构造示例

下载: 全尺寸图片幻灯片

图 4 本文所提近邻互耦模型与文献[18]模型对比

下载: 全尺寸图片幻灯片

图 5 近邻矩阵与全互耦矩阵计算复杂度对比

下载: 全尺寸图片幻灯片

图 6 本文所提近邻模型与参考模型误差比较

下载: 全尺寸图片幻灯片

图 7 本文所提可调阻抗优化方法与文献方法对比

下载: 全尺寸图片幻灯片

图 8 所提方法的信道增益变化曲线

下载: 全尺寸图片幻灯片

参考文献(24)

[1]	MU Xidong, XU Jiaqi, LIU Yuanwei, et al. Reconfigurable intelligent surface-aided near-field communications for 6G: Opportunities and challenges[J]. IEEE Vehicular Technology Magazine, 2024, 19(1): 65–74. doi: 10.1109/MVT.2023.3345608.
[2]	BASAR E, ALEXANDROPOULOS G C, LIU Yuanwei, et al. Reconfigurable intelligent surfaces for 6G: Emerging hardware architectures, applications, and open challenges[J]. IEEE Vehicular Technology Magazine, 2024, 19(3): 27–47. doi: 10.1109/MVT.2024.3415570.
[3]	WANG Jinghe, TANG Wankai, LIANG Jingcheng, et al. Reconfigurable intelligent surface: Power consumption modeling and practical measurement validation[J]. IEEE Transactions on Communications, 2024, 72(9): 5720–5734. doi: 10.1109/TCOMM.2024.3382332.
[4]	费丹, 陈晨, 郑鹏, 等. 基于智能超表面的室内覆盖增强技术研究与实验验证[J]. 电子与信息学报, 2022, 44(7): 2374–2381. doi: 10.11999/JEIT220068. FEI Dan, CHEN Chen, ZHENG Peng, et al. Research and experimental verification of reconfigurable intelligent surface in indoor coverage enhancement[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2374–2381. doi: 10.11999/JEIT220068.
[5]	SIDDIKY M N A, RAHMAN M E, UZZAL M S, et al. A comprehensive exploration of 6G wireless communication technologies[J]. Computers, 2025, 14(1): 15. doi: 10.3390/computers14010015.
[6]	KUMAR A, SHARMA S, KUMAR M H, et al. RIS-assisted terahertz communications for 6G networks: A comprehensive overview[J]. IEEE Access, 2025, 13: 96337–96364. doi: 10.1109/ACCESS.2025.3574476.
[7]	BALANIS C A. Antenna Theory: Analysis and Design[M]. 4th ed. Hoboken: Wiley, 2016.
[8]	DI RENZO M, DANUFANE F H, and TRETYAKOV S. Communication models for reconfigurable intelligent surfaces: From surface electromagnetics to wireless networks optimization[J]. Proceedings of the IEEE, 2022, 110(9): 1164–1209. doi: 10.1109/JPROC.2022.3195536.
[9]	TANG Wankai, CHEN Mingzheng, CHEN Xiangyu, et al. Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement[J]. IEEE Transactions on Wireless Communications, 2021, 20(1): 421–439. doi: 10.1109/TWC.2020.3024887.
[10]	LI Dong. How many reflecting elements are needed for energy- and spectral-efficient intelligent reflecting surface-assisted communication[J]. IEEE Transactions on Communications, 2022, 70(2): 1320–1331. doi: 10.1109/TCOMM.2021.3128544.
[11]	GRADONI G and DI RENZO M. End-to-end mutual coupling aware communication model for reconfigurable intelligent surfaces: An electromagnetic-compliant approach based on mutual impedances[J]. IEEE Wireless Communications Letters, 2021, 10(5): 938–942. doi: 10.1109/LWC.2021.3050826.
[12]	MURSIA P, PHANG S, SCIANCALEPORE V, et al. SARIS: Scattering aware reconfigurable intelligent surface model and optimization for complex propagation channels[J]. IEEE Wireless Communications Letters, 2023, 12(11): 1921–1925. doi: 10.1109/LWC.2023.3299304.
[13]	SHEN Shanpu, CLERCKX B, and MURCH R. Modeling and architecture design of reconfigurable intelligent surfaces using scattering parameter network analysis[J]. IEEE Transactions on Wireless Communications, 2022, 21(2): 1229–1243. doi: 10.1109/TWC.2021.3103256.
[14]	ABRARDO A, TOCCAFONDI A, and DI RENZO M. Design of reconfigurable intelligent surfaces by using S-parameter multiport network theory—optimization and full-wave validation[J]. IEEE Transactions on Wireless Communications, 2024, 23(11): 17084–17102. doi: 10.1109/TWC.2024.3450722.
[15]	NERINI M, SHEN Shanpu, LI Hongyu, et al. Beyond diagonal reconfigurable intelligent surfaces utilizing graph theory: Modeling, architecture design, and optimization[J]. IEEE Transactions on Wireless Communications, 2024, 23(8): 9972–9985. doi: 10.1109/TWC.2024.3367631.
[16]	NERINI M, SHEN Shanpu, LI Hongyu, et al. A universal framework for multiport network analysis of reconfigurable intelligent surfaces[J]. IEEE Transactions on Wireless Communications, 2024, 23(10): 14575–14590. doi: 10.1109/TWC.2024.3416825.
[17]	ZHENG Pinjun, WANG Ruiqi, SHAMIM A, et al. Mutual coupling in RIS-aided communication: Model training and experimental validation[J]. IEEE Transactions on Wireless Communications, 2024, 23(11): 17174–17188. doi: 10.1109/TWC.2024.3451548.
[18]	QIAN Xuewen and DI RENZO M. Mutual coupling and unit cell aware optimization for reconfigurable intelligent surfaces[J]. IEEE Wireless Communications Letters, 2021, 10(6): 1183–1187. doi: 10.1109/LWC.2021.3061449.
[19]	ABRARDO A, DARDARI D, DI RENZO M, et al. MIMO interference channels assisted by reconfigurable intelligent surfaces: Mutual coupling aware sum-rate optimization based on a mutual impedance channel model[J]. IEEE Wireless Communications Letters, 2021, 10(12): 2624–2628. doi: 10.1109/LWC.2021.3109017.
[20]	PEROVIć N S, TRAN L N, DI RENZO M, et al. Optimization of RIS-aided SISO systems based on a mutually coupled loaded wire dipole model[C]. 2023 57th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, 2023: 145–150. doi: 10.1109/IEEECONF59524.2023.10476915.
[21]	LI Hongyu, SHEN Shanpu, NERINI M, et al. Beyond diagonal reconfigurable intelligent surfaces with mutual coupling: Modeling and optimization[J]. IEEE Communications Letters, 2024, 28(4): 937–941. doi: 10.1109/LCOMM.2024.3361648.
[22]	SUN Wenlong, SUN Shaohui, SHI Tong, et al. A new model of beyond diagonal reconfigurable intelligent surfaces (BD-RIS) for the corresponding quantization and optimization[J]. IEEE Transactions on Wireless Communications, 2024, 23(9): 11521–11534. doi: 10.1109/TWC.2024.3382750.
[23]	LI Hongyu, NERINI M, SHEN Shanpu, et al. Beyond diagonal reconfigurable intelligent surfaces in wideband OFDM communications: Circuit-based modeling and optimization[J]. IEEE Transactions on Wireless Communications, 2025, 24(4): 3623–3636. doi: 10.1109/TWC.2025.3532616.
[24]	LI Dong. Ergodic capacity of intelligent reflecting surface-assisted communication systems with phase errors[J]. IEEE Communications Letters, 2020, 24(8): 1646–1650. doi: 10.1109/LCOMM.2020.2997027.