• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Demodulation of Chaos Phase Modulation Spread Spectrum Signals Using Machine Learning Methods and Its Evaluation for Underwater Acoustic Communication
【2h】

Demodulation of Chaos Phase Modulation Spread Spectrum Signals Using Machine Learning Methods and Its Evaluation for Underwater Acoustic Communication

机译：机器学习方法对混沌相位调制扩频信号的解调及其对水下声通信的评估

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The chaos phase modulation sequences consist of complex sequences with a constant envelope, which has recently been used for direct-sequence spread spectrum underwater acoustic communication. It is considered an ideal spreading code for its benefits in terms of large code resource quantity, nice correlation characteristics and high security. However, demodulating this underwater communication signal is a challenging job due to complex underwater environments. This paper addresses this problem as a target classification task and conceives a machine learning-based demodulation scheme. The proposed solution is implemented and optimized on a multi-core center processing unit (CPU) platform, then evaluated with replay simulation datasets. In the experiments, time variation, multi-path effect, propagation loss and random noise were considered as distortions. According to the results, compared to the reference algorithms, our method has greater reliability with better temporal efficiency performance.
机译：混沌相位调制序列由具有恒定包络的复杂序列组成，最近已被用于直接序列扩频水下声通信。由于其在代码资源量大，良好的相关特性和高安全性方面的优势，被认为是理想的扩展代码。然而，由于复杂的水下环境，解调该水下通信信号是一项艰巨的工作。本文将这个问题作为目标分类任务解决，并构想了一种基于机器学习的解调方案。所提出的解决方案是在多核中央处理器（CPU）平台上实施和优化的，然后使用重播仿真数据集进行评估。在实验中，时间变化，多径效应，传播损耗和随机噪声被视为失真。根据结果​​，与参考算法相比，我们的方法具有更高的可靠性和更好的时间效率性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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