基于生成对抗网络的超宽带数字信道建模
Ultra-wideband digital channel modeling based on generative adversarial network
诸葛斌 1王正贤 1汪盈 1蔡晓丹 1董黎刚 1张子天 1蒋献 1李华 2徐越倩3
作者信息
- 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
- 折叠
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Wasser-stein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 sys-tem 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 im-prove the detail accuracy and diversity of the generated data.The model was allowed to generate data according to dif-ferent 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 datas-ets and complex fading channel environments,with a 4.8%increase in recognition accuracy and a 5%reduction in mode collapse issues.
关键词
数字孪生/信道建模/生成对抗网络/智能通信网络Key words
digital twin/channel modeling/generative adversarial network/intelligent communication network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据