IRS辅助MIMO系统的低复杂度自适应相位优化算法

潘蒙蒙; 王中鹏

doi:10.11959/j.issn.1000-0801.DXKX250697

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IRS辅助MIMO系统的低复杂度自适应相位优化算法

研究与开发 | 更新时间：2026-06-17

- IRS辅助MIMO系统的低复杂度自适应相位优化算法
- Low-complexity adaptive phase optimization algorithm for IRS-assisted MIMO system
- 电信科学 2026年42卷第5期页码：112-122
- 作者机构：
  
  浙江科技大学人工智能与信息工程学院，浙江杭州 310023
- 作者简介：
  
  潘蒙蒙（1998- ），女，浙江科技大学人工智能与信息工程学院硕士生，主要研究方向为智能反射面相位优化。
  王中鹏（1966- ），男，博士，浙江科技大学人工智能与信息工程学院教授，主要研究方向为无线通信、智能反射面相位优化。
- 基金信息：
  
  浙江省自然科学基金重点项目(LZ21F010001)
- DOI：10.11959/j.issn.1000-0801.DXKX250697
  中图分类号： TN925
- 收稿：2025-12-02，
  
  修回：2025-12-29，
  
  录用：2026-01-08，
  
  纸质出版：2026-05-20
- 稿件说明：
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潘蒙蒙,王中鹏.IRS辅助MIMO系统的低复杂度自适应相位优化算法[J].电信科学,2026,42(05):112-122.

Pan Mengmeng,Wang Zhongpeng.Low-complexity adaptive phase optimization algorithm for IRS-assisted MIMO system[J].Telecommunications Science,2026,42(05):112-122.
潘蒙蒙,王中鹏.IRS辅助MIMO系统的低复杂度自适应相位优化算法[J].电信科学,2026,42(05):112-122. DOI： 10.11959/j.issn.1000-0801.DXKX250697.

Pan Mengmeng,Wang Zhongpeng.Low-complexity adaptive phase optimization algorithm for IRS-assisted MIMO system[J].Telecommunications Science,2026,42(05):112-122. DOI： 10.11959/j.issn.1000-0801.DXKX250697.

摘要

智能反射面（intelligent reflecting surface，IRS）可通过优化反射矩阵的相位，有效地改善信道质量并提升系统容量，因此，设计高性能且低复杂度的相位优化算法成为关键问题。针对IRS辅助多入多出（multiple-input multiple-output，MIMO）系统的信道容量最大化问题，不同于以信道增益为导向的传统方法，直接以系统信道容量为优化目标，推导了IRS单元相位的实数闭式更新公式。在此基础上，进一步提出了固定步长和自适应步长两种低复杂度相位优化算法，其中，自适应步长算法在保持固定步长容量性能的前提下，通过动态调节相位更新幅度，进一步加快了算法收敛速度。仿真结果表明，当发射功率为10 dBm、IRS单元数为100时，所提算法相较于维度正弦最大化（dimensional sine maximization，DSM）算法容量提升了5 bit/s，迭代次数与平均运行时间分别降低了81.4%、78.3%；与固定步长算法相比，自适应步长算法在不损失容量性能的情况下，迭代次数与平均运行时间分别降低了49.2%、42.1%，且在大规模IRS配置下低复杂度优势更突出。

Abstract

Intelligent reflecting surface (IRS) can effectively enhance channel quality and improve system capacity by optimizing the phase of the reflection matrix

making the design of high-performance and low-complexity phase optimization algorithms a critical issue. For the problem of maximizing the channel capacity in IRS-assisted multiple-input multiple-output (MIMO) systems

a closed-form update formula for the real-valued phase of each IRS element was derived directly based on the system capacity

which differed from the conventional approaches that targeted the channel gain. On this basis

two low-complexity phase optimization algorithms

namely fixed-step and adaptive-step methods

were proposed. The adaptive-step algorithm further accelerated convergence by dynamically adjusting the phase update magnitude while maintaining the capacity performance of the fixed-step algorithm. Simulation results demonstrate that

under the transmit power of 10 dBm and with 100 IRS elements

the proposed algorithm achieves a capacity improvement of approximately 5 bit/s over the dimensional sine maximization (DSM) method

with the number of iterations and average runtime reducing by 81.4% and 78.3%

respectively. Compared with the fixed-step algorithm

the adaptive-step algorithm further reduces the number of iterations and average runtime by 49.2% and 42.1% without sacrificing the capacity performance

and exhibits more pronounced low-complexity advantages in large-scale IRS deployments.

关键词

Keywords

references

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