Ultra-wideband digital channel modeling based on generative adversarial network

ZHUGE Bin; WANG Zhengxian; WANG Ying; CAI Xiaodan; DONG Ligang; ZHANG Zitian; JIANG Xian; LI Hua; XU Yueqian

doi:10.11959/j.issn.1000-0801.2024242

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Ultra-wideband digital channel modeling based on generative adversarial network

Research and Development | 更新时间：2025-03-17

- Ultra-wideband digital channel modeling based on generative adversarial network
- Telecommunications Science Vol. 40, Issue 11, Pages: 27-39(2024)
- 作者机构：
  
  1.浙江工商大学信息与电子工程学院，浙江杭州 310018
  2.UT斯达康通讯有限公司，浙江杭州 310059
  3.浙江工商大学英贤慈善学院，浙江杭州 310018
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62301488);Zhejiang Key Laboratory of New Network Standards and Application Technology(2013E10012);The Natural Science Foundation of Zhejiang Province(LZ23F010003);The Key Project of Higher Education Research of Zhejiang Higher Education Association in 2022(KT2022017);The Basic Research Funds for Provincial Universities of Zhejiang Gongshang University(QRK23010);The Major Project of “Digital+” Discipline Construction Management of Zhejiang Gongshang University(SZJ2022A003)
- DOI：10.11959/j.issn.1000-0801.2024242
  CLC： TP301
- Received：15 July 2024，
  
  Revised：17 October 2024，
  
  Published：20 November 2024
- 稿件说明：
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诸葛斌,王正贤,汪盈等.基于生成对抗网络的超宽带数字信道建模[J].电信科学,2024,40(11):27-39.

ZHUGE Bin,WANG Zhengxian,WANG Ying,et al.Ultra-wideband digital channel modeling based on generative adversarial network[J].Telecommunications Science,2024,40(11):27-39.
诸葛斌,王正贤,汪盈等.基于生成对抗网络的超宽带数字信道建模[J].电信科学,2024,40(11):27-39. DOI： 10.11959/j.issn.1000-0801.2024242.

ZHUGE Bin,WANG Zhengxian,WANG Ying,et al.Ultra-wideband digital channel modeling based on generative adversarial network[J].Telecommunications Science,2024,40(11):27-39. DOI： 10.11959/j.issn.1000-0801.2024242.

摘要

在超宽带通信技术中，获取高质量的信道冲激响应数据对系统设计和性能优化至关重要。引入最小二乘生成对抗网络和改进的损失函数，能显著提升信道数据的捕捉和复现能力。结合特征匹配技术和条件生成对抗网络，可以增强生成数据的细节准确性和多样性，还能使模型根据不同通信环境和信号场景进行数据生成。在模型训练阶段，采用能够代表全局特征的重构信道数据，而在测试阶段使用了经历无线衰落的实际信道数据。实验结果显示，模型在小样本数据集和复杂衰落信道环境下的表现优于带有梯度惩罚的Wasserstein生成对抗网络（WGAN-GP），识别准确率提高4.8%，模式崩溃问题减少5%。

Abstract

In ultra-wideband communication technology

high-quality channel impulse response data is crucial for system design and performance optimization. A least squares generative adversarial network (LSGAN) and an improved loss function were introduced

which significantly enhanced the ability to capture and reproduce channel data. By combining feature matching techniques with conditional generative adversarial networks (CGAN)

it was able to improve the detail accuracy and diversity of the generated data. The model was allowed to generate data according to different communication environments and signal scenarios. During the model training phase

reconstructed channel data representing global features were used

while actual channel data experiencing wireless fading were employed during the testing phase. Experimental results demonstrate that the model outperforms the WGAN-GP in small sample datasets and complex fading channel environments

with a 4.8% increase in recognition accuracy and a 5% reduction in mode collapse issues.

关键词

Keywords

references

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