基于XL-RIS混合场系统的快速波束训练方案

杨黎明; 邱多; 李俊峰

doi:10.11959/j.issn.1000-0801.2025153

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基于XL-RIS混合场系统的快速波束训练方案

研究与开发 | 更新时间：2025-09-04

- 基于XL-RIS混合场系统的快速波束训练方案
- Fast beam training scheme in hybrid near-far field based on XL-RIS system
- 电信科学 2025年41卷第8期页码：76-85
- 作者机构：
  
  重庆邮电大学通信与信息工程学院，重庆 400065
- 作者简介：
  
  [ "杨黎明（1976- ），女，重庆邮电大学通信与信息工程学院高级工程师，主要研究方向为移动通信协议栈软件设计及测试、移动通信空口安全。" ]
  [ "邱多（2001- ），男，重庆邮电大学通信与信息工程学院硕士生，主要研究方向为移动通信物理层协议与智能反射面。" ]
  [ "李俊峰（2001- ），男，重庆邮电大学通信与信息工程学院硕士生，主要研究方向为移动通信与大规模MIMO系统预编码。" ]
- 基金信息：
  
  重庆市自然科学基金创新发展联合基金（中国星网）(CSTB2023NSCQ-LZX0114)
- DOI：10.11959/j.issn.1000-0801.2025153
  中图分类号： TN928
- 收稿：2025-02-28，
  
  修回：2025-04-02，
  
  纸质出版：2025-08-20
- 稿件说明：
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杨黎明,邱多,李俊峰.基于XL-RIS混合场系统的快速波束训练方案[J].电信科学,2025,41(08):76-85.

YANG Liming,QIU Duo,LI Junfeng.Fast beam training scheme in hybrid near-far field based on XL-RIS system[J].Telecommunications Science,2025,41(08):76-85.
杨黎明,邱多,李俊峰.基于XL-RIS混合场系统的快速波束训练方案[J].电信科学,2025,41(08):76-85. DOI： 10.11959/j.issn.1000-0801.2025153.

YANG Liming,QIU Duo,LI Junfeng.Fast beam training scheme in hybrid near-far field based on XL-RIS system[J].Telecommunications Science,2025,41(08):76-85. DOI： 10.11959/j.issn.1000-0801.2025153.

摘要

针对单一的反射链路模型无法准确衡量超大规模智能反射面（extremely large-scale reconfigurable intelligent suface，XL-RIS）系统环境的问题，构造了一种XL-RIS近场和基站（base station，BS）远场区域相叠加的通信模型；在此基础上，为了减少训练开销，提出了一种高效波束训练方案。首先，综合考虑近场球面波束和远场平面波束路径增益，引入影响系数

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2.20133328

，推导了适配XL-RIS近场和BS远场重叠区域的信道模型；此外，为了提高接收信号功率、使直射波束和反射波束同相相加，引入相位修正参数，构造了匹配重叠区域的训练码本；最后，针对该模型设计了一种可变步长的空间分层方案，具体而言，空间的每一层采样间隔由原点沿半径向外依次递增。仿真结果表明，在信噪比为0时，双链路混合场信道模型能达到完美信道条件下93.7%的速率性能，相对于近场反射模型和远场反射模型分别提升了57.6%和205.4%；新的空间分层方案与传统的分层方案相比平均可达速率误差在1%以内，但训练开销减少了63.6%。

Abstract

Aiming at the problem that a single reflected link model cannot accurately measure the environment of extremely large-scale reconfigurable intelligent suface (XL-RIS) system

a communication model of superimposed XL-RIS near-field region and BS far-field region was constructed. On this basis

an efficient beam training scheme was proposed to reduce the training cost. Firstly

considering the path gain of near-field spherical beam and far-field planar beam

the influence coefficient

was introduced to derive the channel model suitable for XL-RIS near-field and BS far-field superposition region. In addition

in order to improve the received signal power and make the direct beam and the reflected beam in phase superposition

the phase correction parameter was introduced

and the training codebook matching the hybrid near-far field channel was constructed. Finally

a spatial layering scheme with variable step size was designed for the model. Speci

fically

the sampling interval of each layer increases from the origin along the radius. The simulation results show that the dual link hybrid near-far field model can achieve 93.7% rate performance under perfect channel condition when the SNR is 0

and the rate performance is improved by 57.6% and 205.4% compared with the near-field reflection model and the far-field reflection model respectively. The average reachable rate error of the new spatial layering scheme is less than 1% compared with the traditional layering scheme

but the training cost is reduced by 63.6%.

关键词

Keywords

references

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