论文标题
可分离的多维正交匹配追求及其在MMWave的联合定位和通信中的应用
Separable multidimensional orthogonal matching pursuit and its application to joint localization and communication at mmWave
论文作者
论文摘要
贪婪的稀疏恢复已成为许多应用中的流行工具，尽管必须利用大型稀疏字典或传感矩阵时的复杂性仍然过于刺激。在本文中，我们首先制定了一类新的稀疏恢复问题，这些问题利用了多维词典以及在某些问题中出现的测量矩阵的可分离性。然后，我们开发了一种新的算法，可分离的多维正交匹配追踪（SMOMP），可以解决此类问题的复杂性低。最后，我们将Smomp应用于MMWave的联合定位和通信问题，并在数值上显示其有效性，以合理的复杂性，高精度渠道和位置估计。
Greedy sparse recovery has become a popular tool in many applications, although its complexity is still prohibitive when large sparsifying dictionaries or sensing matrices have to be exploited. In this paper, we formulate first a new class of sparse recovery problems that exploit multidimensional dictionaries and the separability of the measurement matrices that appear in certain problems. Then we develop a new algorithm, Separable Multidimensional Orthogonal Matching Pursuit (SMOMP), which can solve this class of problems with low complexity. Finally, we apply SMOMP to the problem of joint localization and communication at mmWave, and numerically show its effectiveness to provide, at a reasonable complexity, high accuracy channel and position estimations.
