• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Communications >Blind Channel Equalization Using Variational Autoencoders
【24h】

Blind Channel Equalization Using Variational Autoencoders

机译：使用变形自动置换器盲信道均衡

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A new maximum likelihood estimation approach for blind channel equalization, using variational autoencoders (VAEs), is introduced. Significant and consistent improvements in the error rate of the reconstructed symbols, compared to constant modulus equalizers, are demonstrated. In fact, for the channels that were examined, the performance of the new VAE blind channel equalizer was close to the performance of a nonblind adaptive linear minimum mean square error equalizer. The new equalization method enables a significantly lower latency channel acquisition compared to the constant modulus algorithm (CMA). The VAE uses a convolutional neural network with two layers and a very small number of free parameters. Although the computational complexity of the new equalizer is higher compared to CMA, it is still reasonable, and the number of free parameters to estimate is small.
机译：介绍了使用变化自动额（VAES）的盲信道均衡的新的最大似然估计方法。与恒定模量均衡器相比，重建符号的误差率的显着且一致的改进是显着的。实际上，对于检查的频道，新的VAE盲信道均衡器的性能接近非牢断自适应线性最小均方误差均衡器的性能。与恒定模量算法（CMA）相比，新的均衡方法能够显着降低延迟信道采集。 VAE使用具有两层的卷积神经网络和非常少量的自由参数。尽管与CMA相比，新均衡器的计算复杂性更高，但它仍然是合理的，并且估计的自由参数的数量很小。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号