公开(公告)号：US20160204963A1

公开(公告)日：2016-07-14

申请号：US14596680

申请日：2015-01-14

Applicant: Javad ABDOLI ,  Ming JIA

Inventor： Javad ABDOLI ,  Ming JIA

IPC: H04L25/03

CPC classification number: H04L25/03286 ,  H03M7/3062 ,  H04L25/03178 ,  H04L2025/03636

Abstract: Nonzero elements of a signal vector, which may be a sparse signal vector, may be determined based on an observation vector representing a set of underdetermined observations using a compressed sensing optimization and a non-underdetermined estimation method such as iterative linear minimum mean-square error (“LMMSE”) estimation. Compressed sensing optimization may be used to obtain a subset of potentially nonzero elements of the signal vector, and LMMSE estimation may then be used to find the nonzero elements among the potentially nonzero elements. The identification of nonzero elements may then be used to recover the signal vector from the observation vector. This technique is useful for recovering compressed data such as a sparse frequency space representation of audio or video data from a measurement. The technique is also useful for identifying at a base station a relatively small number active devices in an overloaded communication network.

Abstract translation: 可以使用压缩感测优化和诸如迭代线性最小均方误差之类的未确定的估计方法，基于表示一组不确定观测值的观测向量来确定可以是稀疏信号向量的信号向量的非零元素 （“LMMSE”）估计。 可以使用压缩感测优化来获得信号向量的潜在非零元素的子集，然后可以使用LMMSE估计来查找潜在非零元素中的非零元素。 然后可以使用非零元素的识别来从观测向量恢复信号向量。 这种技术对于恢复压缩数据（例如来自测量的音频或视频数据的稀疏频率空间表示）是有用的。 该技术对于在基站中在过载的通信网络中识别相对较小数量的有源设备也是有用的。