Separable multidimensional orthogonal matching pursuit and its application to joint localization and communication at mmWave
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.
Code (0)
등록된 구현이 없습니다.
Tasks
PositionSimilar Papers 제목 키워드 기반
Multidimensional orthogonal matching pursuit: theory and application to high accuracy joint localization and communication at mmWave
Greedy approaches in general, and orthogonal matching pursuit in particular, are the most commonly used sparse recovery techniques in a wide range of applications. The complexity of these approaches is highly dependent o…
Multidimensional Orthogonal Matching Pursuit-based RIS-aided Joint Localization and Channel Estimation at mmWave
RIS-aided millimeter wave wireless systems benefit from robustness to blockage and enhanced coverage. In this paper, we study the ability of RIS to also provide enhanced localization capabilities as a by-product of commu…
Low complexity joint position and channel estimation at millimeter wave based on multidimensional orthogonal matching pursuit
Compressive approaches provide a means of effective channel high resolution channel estimates in millimeter wave MIMO systems, despite the use of analog and hybrid architectures. Such estimates can also be used as part o…
PositionSimultaneous Optimized Orthogonal Matching Pursuit with Application to ECG Compression
A greedy pursuit strategy which finds a common basis for approximating a set of similar signals is proposed. The strategy extends the Optimized Orthogonal Matching Pursuit approach to selecting the subspace containing th…
Sparse Representation-Based Classification: Orthogonal Least Squares or Orthogonal Matching Pursuit?
Spare representation of signals has received significant attention in recent years. Based on these developments, a sparse representation-based classification (SRC) has been proposed for a variety of classification and re…
BenchmarkingClassificationFace RecognitionGeneral Classification+4