• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Theory, IEEE Transactions on >Sparsity-Based Recovery of Finite Alphabet Solutions to Underdetermined Linear Systems
【24h】

Sparsity-Based Recovery of Finite Alphabet Solutions to Underdetermined Linear Systems

机译：欠定线性系统基于稀疏性的有限字母解的恢复

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We consider the problem of estimating a deterministic finite alphabet vector from underdetermined measurements , where is a given (random) matrix. Two new convex optimization methods are introduced for the recovery of finite alphabet signals via -norm minimization. The first method is based on regularization. In the second approach, the problem is formulated as the recovery of sparse signals after a suitable sparse transform. The regularization-based method is less complex than the transform-based one. When the alphabet size equals 2 and grows proportionally, the conditions under which the signal will be recovered with high probability are the same for the two methods. When , the behavior of the transform-based method is established. Experimental results support this theoretical result and show that the transform method outperforms the regularization-based one.
机译：我们考虑了从欠定的度量估计确定性有限字母向量的问题，其中给定（随机）矩阵。引入了两种新的凸优化方法，用于通过-norm最小化恢复有限字母信号。第一种方法基于正则化。在第二种方法中，问题被表述为在进行适当的稀疏变换之后恢复稀疏信号。基于正则化的方法不如基于变换的方法复杂。当字母大小等于2并成比例增长时，这两种方法将以高概率恢复信号的条件相同。当为时，将建立基于变换的方法的行为。实验结果支持了这一理论结果，并表明该变换方法优于基于正则化的变换方法。

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