隐空间采样与隐蔽特征提取的CR-GAN复杂无线信道建模

姜斌; 程子巍; 包建荣; 吕鑫; 赵宜楠

doi:10.12263/DZXB.20230406

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隐空间采样与隐蔽特征提取的CR-GAN复杂无线信道建模

学术论文 | 更新时间：2025-12-11

- 隐空间采样与隐蔽特征提取的CR-GAN复杂无线信道建模
- CR-GAN Complex Wireless Channel Modeling with Hidden Space Sampling and Hidden Feature Extraction
- 电子学报 2024年52卷第6期页码：1817-1823
- 作者机构：
  
  1.杭州电子科技大学通信工程学院，浙江杭州 310018
  2.杭州电子科技大学电子信息学院，浙江杭州 310018
- 作者简介：
  
  [ "姜斌男，1980年生，浙江衢州人.现为杭州电子科技大学通信工程学院高级实验师，在职博士生.主要研究方向为无线通讯、信号处理、无线传感网络等. E-mail: jiangbin@hdu.edu.cn" ]
  [ "程子巍男，1998年生，山东潍坊人.现为杭州电子科技大学通信工程学院研究生. E-mail: 1611861153 @qq.com" ]
  [ "包建荣男，1978年生，浙江杭州人.杭州电子科技大学教授、博导，研究方向为空间无线通信、协同信息论与编码、分布式多天线联合迭代检测等.中国电子学会会员编号：E190006599S.E-mail: baojr@hdu.edu.cn" ]
- 基金信息：
  
  浙江省自然科学基金(LZ24F010005)
- DOI：10.12263/DZXB.20230406
  中图分类号： TN929.5
- 收稿：2023-05-06，
  
  修回：2024-01-12，
  
  纸质出版：2024-06-25
- 稿件说明：
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姜斌,程子巍,包建荣,等. 隐空间采样与隐蔽特征提取的CR-GAN复杂无线信道建模[J]. 电子学报,2024,52(06):1817-1823.

JIANG Bin, CHENG Zi-wei, BAO Jian-rong, et al. CR-GAN Complex Wireless Channel Modeling with Hidden Space Sampling and Hidden Feature Extraction[J]. Acta Electronica Sinica, 2024, 52(06): 1817-1823.
姜斌,程子巍,包建荣,等. 隐空间采样与隐蔽特征提取的CR-GAN复杂无线信道建模[J]. 电子学报,2024,52(06):1817-1823. DOI：10.12263/DZXB.20230406

JIANG Bin, CHENG Zi-wei, BAO Jian-rong, et al. CR-GAN Complex Wireless Channel Modeling with Hidden Space Sampling and Hidden Feature Extraction[J]. Acta Electronica Sinica, 2024, 52(06): 1817-1823. DOI：10.12263/DZXB.20230406

摘要

为了更准确地建模随机无线信道，提出一种自适应增强条件生成对抗网络信道建模方法.其采用扩展的生成对抗网络（Generative Adversarial Network，GAN）开展训练，以近似估计无线信道响应，模拟真实无线环境信道.为了改善GAN训练稳定性和学习能力，引入条件信息和梯度惩罚项，并提出一种增强条件生成对抗网络框架，用于提取信道隐蔽特征.此外，还提出隐空间采样策略，以增加随机变量与生成数据的互信息量，提高所提框架的信道建模性能.仿真表明：所提框架能很好地模拟复杂无线信道分布.在信噪比为10 dB时，与现有GAN训练方法相比，其归一化均方误差性能改善约24%.

Abstract

To accurately model random wireless channels

an adaptive channel modeling framework based on a strengthened conditional generative adversarial network (GAN) is proposed. It utilizes the extended GAN for training to approximately estimate the response of wireless channels and thus stimulate the actual wireless channels. To improve both the GAN training stability and learning capability

conditional information and gradient penalty terms are introduced. Besides

a strengthened conditional GAN frame

named condition reinforcement GAN (CR-GAN)

is proposed to extract the essential hidden characteristics of wireless channels. In addition

a hidden space sampling strategy is utilized to increase the mutual information between the potential variables and generative data for the improved channel modeling performance of the proposed framework. Simulation results demonstrate that

at a signal-to-noise ratio of 10 dB

the proposed CR-GAN framework outperforms current GAN-based models by reducing 24% of the normalized mean squared error.

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references

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