智能反射面辅助的无线通信系统波束赋形及智能反射面相移技术综述

邢智童; 李云; 吴广富; 夏士超

doi:10.11999/JEIT250790

智能反射面辅助的无线通信系统波束赋形及智能反射面相移技术综述

doi: 10.11999/JEIT250790 cstr: 32379.14.JEIT250790

邢智童¹,
李云^{1, 2, ,},
吴广富^{1, 2},
夏士超^{1, 2}

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
重庆邮电大学计算机科学与技术学院重庆 400065

基金项目: 国家自然科学基金(62301100)，重庆市教委科学技术研究计划项目青年项目(KJQN202200606, KJQN202300638)，重庆市自然科学基金(CSTB2024NSCQ-MSX0210, CSTB2024NSCQ-QCXMX0063, CSTC2024YCJH-BGZXM003)，重庆市自然科学基金创新发展联合基金(CSTB2025NSCQ-LZX0053)

详细信息

作者简介:
邢智童：男，讲师，研究方向为无线通信传输、大规模MIMO和智能反射面

李云：男，教授，研究方向为蜂窝通信、智能反射面和边缘计算

吴广富：男，副教授，研究方向为卫星通信和大规模MIMO系统

夏士超：男，讲师，研究方向为大规模MIMO系统和边缘计算

通讯作者:
李云　liyun@cqupt.edu.cn

中图分类号: TN919.72
计量
- 文章访问数: 61
- HTML全文浏览量: 30
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2025-08-25
- 修回日期: 2025-11-03
- 录用日期: 2025-11-05
- 网络出版日期: 2025-11-13

A Review on Phase Rotation and Beamforming Scheme for Intelligent Reflecting Surface Assisted Wireless Communication Systems

XING Zhitong¹,
LI Yun^{1, 2
, ,},
WU Guangfu^{1, 2},
XIA Shichao^{1, 2}

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

Funds: The National Natural Science Foundation of China (62301100), Scientific and Technological Research Program of Chongqing Municipal Education Commission Youth Project (KJQN202200606, KJQN202300638), Chongqing Natural Science Foundation (CSTB2024NSCQ-MSX0210, CSTB2024NSCQ-QCXMX0063, CSTC2024YCJH-BGZXM003), Chongqing Natural Science Foundation Innovation and Development Joint Fund(CSTB2025NSCQ-LZX0053)

摘要

摘要: 自2020年5G设备开始大规模商用部署后，全球业界已经开始了6G技术的研究。在5G/6G时代，通信系统需要适应更加复杂的信道环境，如超高密度的城市环境、远海、沙漠、森林等地域。因此，如果能够有一种低能耗的方式，对无线通信信道进行自适应的调整和重构，将不仅有助于无线通信设备向传输时延更低、传输速率更快、接收能力更强等方面进一步迈进，而且可以帮助无线通信设备更好地部署于复杂信道环境的地域。智能反射面(IRS)被认为是实现信道环境重构的一种有效的设备。这些IRS设备大多是无源设备，因此，不会带来过多的能耗。当IRS与单输入单输出(SISO)、多输入单输出(MISO)、多输入多输出(MIMO)等技术相结合，将进一步提高无线通信传输的传输速率、降低无线通信的能耗、增强无线通信设备对复杂信道环境的适应性。该文对IRS辅助的SISO,MISO和MIMO系统的信号传输模型进行系统总结，分析了IRS辅助的SISO,MISO和MIMO的信号传输建模方式，并对IRS辅助的SISO,MISO和MIMO系统的波束赋形和相移技术进行了综述。
- 智能反射面 /
- 波束赋形 /
- 相移技术 /
- 信号传输
Abstract: Objective Since the large-scale commercial deployment of 5G networks in 2020 and the continued development of 6G technology, modern communication systems need to function under increasingly complex channel conditions. These include ultra-high-density urban environments and remote areas such as oceanic regions, deserts, and forests. To meet these challenges, low-energy solutions capable of dynamically adjusting and reconfiguring wireless channels are required. Such solutions would improve transmission performance by lowering latency, increasing data rates, and strengthening signal reception, and would support more efficient deployment in demanding environments. The Intelligent Reflecting Surface (IRS) has gained attention as a promising approach for reshaping channel conditions. Unlike traditional active relays, an IRS operates passively and adds minimal energy consumption. When integrated with communication architectures such as Single Input Single Output (SISO), Multiple Input Single Output (MISO), and Multiple Input Multiple Output (MIMO), an IRS can improve transmission efficiency, reduce power consumption, and enhance adaptability in complex scenarios. This paper reviews IRS-assisted communication systems, with emphasis on signal transmission models, beamforming methods, and phase-shift optimization strategies. Methods This review examines IRS technology in modern communication systems by analyzing signal transmission models across three fundamental configurations. The discussion begins with IRS-assisted SISO systems, in which IRS control of incident signals through reflection and phase shifting improves single-antenna communication by mitigating traditional propagation constraints. The analysis then extends to MISO and MIMO architectures, where the relationship between IRS phase adjustments and MIMO precoding is assessed to determine strategies that support high spectral efficiency. Based on these transmission models, this review surveys joint optimization and precoding methods tailored for IRS-enhanced MIMO systems. These algorithms can be grouped into four categories that meet different operational requirements. The first aims to minimize power consumption by reducing total energy use while maintaining acceptable communication quality, which is important for energy-sensitive applications such as IoT systems and green communication scenarios. The second seeks to maximize energy efficiency by optimizing the ratio of achievable data rate to power consumption rather than lowering energy use alone, thereby improving performance per unit of energy. The third focuses on maximizing the sum rate by increasing aggregated throughput across users to strengthen overall system capacity in high-density 5G and 6G environments. The fourth prioritizes fairness-aware rate maximization by applying resource allocation methods that ensure equitable bandwidth distribution among users while sustaining high Quality of Service (QoS). Together, these optimization approaches provide a framework for advancing IRS-assisted MIMO systems and allow engineers and researchers to balance performance, energy efficiency, and user fairness according to specific application needs in next-generation wireless networks. Results and Discussions This review shows that IRS assisted communication systems provide important capabilities for next-generation wireless networks through four major advantages. First, IRS strengthens system performance by reconfiguring propagation environments and improving signal strength and coverage in non-line-of-sight conditions, including urban canyons, indoor environments, and remote regions, while also maintaining reliable connectivity in high-mobility cases such as vehicular communication. Second, the technology supports high energy efficiency because of its passive operation, which adds minimal power overhead yet improves spectral efficiency. This characteristic is valuable for sustainable large-scale IoT deployments and green 6G systems that may incorporate energy-harvesting designs. Third, IRS shows strong adaptability when integrated with different communication architectures, including SISO for basic signal enhancement, MISO for improved beamforming, and MIMO for spatial multiplexing, enabling use across environments ranging from ultra-dense urban networks to remote or airborne communication platforms. Finally, recent progress in beamforming and phase-shift optimization strengthens system performance through coherent signal combining, interference suppression in multi-user settings, and low-latency operation for time-critical applications. Machine learning methods such as deep reinforcement learning are also being investigated for real-time optimization. Together, these capabilities position IRS as a key technology for future 6G networks with the potential to support smart radio environments and broad-area connectivity, although further study is required to address challenges in channel estimation, scalability, and standardization. Conclusions This review highlights the potential of IRS technology in next-generation wireless communication systems. By enabling dynamic channel reconfiguration with minimal energy overhead, IRS strengthens the performance of SISO, MISO, and MIMO systems and supports reliable operation in complex propagation environments. The surveyed signal transmission models and optimization methods form a technical basis for continued development of IRS-assisted communication frameworks. As research and industry move toward 6G, IRS is expected to support ultra-reliable, low-latency, and energy-efficient global connectivity. Future studies should address practical deployment challenges such as hardware design, real-time signal processing, and progress toward standardization.
- IRS) /
- Beamforming /
- Phase rotation scheme /
- Signal transmission

HTML全文

图 1 IRS的基本结构图

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图 2 IRS辅助的SISO信号传输图

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图 3 IRS辅助的MISO信号传输图

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图 4 IRS辅助的MIMO信号传输图

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表 1 最小化功耗波束赋形典型优化算法总结

文献	核心目标	方法/算法	适用场景	优缺点
[6]	最小化发射功率，满足用户SINR约束	交替优化、半正定松弛	多用户MISO下行	优点：理论完整；缺点：假设连续相移，不切实际
[7]	离散相移下的功耗优化	分支定界、隐枚举法	硬件受限系统	优点：更实用；缺点：性能损失，复杂度高
[11]	NOMA分簇下的功率最小化	ADMM算法	密集用户场景	优点：提升频谱效率；缺点：簇间干扰难控制
[12,13]	SWIPT系统中功率最小化	最小均方误差+交替优化	能量收集通信	优点：能同时满足速率与能量约束；缺点：非凸问题难收敛
[16]	存在硬件损伤下的安全功耗优化	联合波束与相位设计	全双工NOMA安全系统	优点：考虑实际损伤；缺点：模型复杂，难以实现

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表 2 最大化能效波束赋形典型优化算法总结

文献	核心目标	方法/算法	适用场景	优缺点
[21]	反射元器件分组服务的能效优化	基于位置的相位优化	多用户SISO系统	优点：复杂度低，易于实现；缺点：性能受用户分布影响大
[22]	用户中心网络的能效最大化	分式规划+最大值最小化算法	上行用户中心网络	优点：兼顾用户体验与能效；缺点：需要精确的信道信息
[23]	通感一体系统的能效优化	多约束联合优化	通信感知一体化	优点：实现通感功能平衡；缺点：优化变量多，收敛性差
[24]	STAR-IRS赋能的能效优化	联合优化反射系数与功率分配	速率分割多址系统	优点：全空间覆盖，频谱效率高；缺点：能量守恒约束增加复杂度
[26]	MIMO-SWIPT系统的能效优化	基于均方误差的交替优化	无线携能通信	优点：同时服务信息与能量接收端；缺点：非凸问题求解困难

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表 3 最大化和速率束赋形典型优化算法总结

文献	核心目标	方法/算法	适用场景	优缺点
[28]	混合多址接入下的和速率最大化	时分多址+非正交多址	物联网上行	优点：支持多设备；缺点：相位分配策略复杂
[33]	功率受限下的和速率最大化	ZF传输+最大最小化算法	多用户MISO	优点：低复杂度；缺点：性能受限于迫零预编码
[36]	非理想相移模型下的和速率优化	交替优化	实际IRS系统	优点：模型更真实；缺点：对误差敏感
[42]	变频IRS下的速率最大化	频率偏移调度算法	频域分集系统	优点：提升频域分集；缺点：硬件实现难度大
[43]	有源IRS下的和速率优化	深度学习	高功率、高复杂度场景	优点：自适应强；缺点：训练开销大

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表 4 最大化公平通信速率波束赋形典型优化算法总结

文献	核心目标	方法/算法	适用场景	优缺点
[57]	最大化最小波束方向图增益	联合发射与反射波束设计	通感一体化系统	优点：兼顾感知与通信；缺点：目标冲突难平衡
[58]	最小化最大均方误差	联合预编码与STAR-IRS设计	全空间覆盖系统	优点：提升用户公平性；缺点：优化变量多，收敛慢
[59]	最大化最小用户SINR	联合波束与相位优化	NOMA系统	优点：适用于功率域复用；缺点：对信道差异敏感
[60]	分布式IRS的最小用户速率最大化	中央协调的分布方案	广域覆盖网络	优点：时隙灵活；缺点：调度策略复杂

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