Optimum Detector for Spatial Modulation using Sparsity Recovery in Compressive Sensing
نویسندگان
چکیده
منابع مشابه
Estimation of block sparsity in compressive sensing
Explicitly using the block structure of the unknown signal can achieve better recovery performance in compressive censing. An unknown signal with block structure can be accurately recovered from underdetermined linear measurements provided that it is sufficiently block sparse. However, in practice, the block sparsity level is typically unknown. In this paper, we consider a soft measure of block...
متن کاملCompressive Sensing Recovery of Spike Trains Using A Structured Sparsity Model
The theory of Compressive Sensing (CS) exploits a well-known concept used in signal compression – sparsity – to design new, efficient techniques for signal acquisition. CS theory states that for a length-N signal x with sparsity level K , M = O(K log(N/K)) random linear projections of x are sufficient to robustly recover x in polynomial time. However, richer models are often applicable in real-...
متن کاملCompressive Sensing MRI with Wavelet Tree Sparsity
In Compressive Sensing Magnetic Resonance Imaging (CS-MRI), one can reconstruct a MR image with good quality from only a small number of measurements. This can significantly reduce MR scanning time. According to structured sparsity theory, the measurements can be further reduced to O(K + log n) for tree-sparse data instead of O(K +K log n) for standard K-sparse data with length n. However, few ...
متن کاملHierarchical Recovery in Compressive Sensing
A combinatorial approach to compressive sensing based on a deterministic column replacement technique is proposed. Informally, it takes as input a pattern matrix and ingredient measurement matrices, and results in a larger measurement matrix by replacing elements of the pattern matrix with columns from the ingredient matrices. This hierarchical technique yields great flexibility in sparse signa...
متن کاملUsing Correlated Subset Structure for Compressive Sensing Recovery
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the results in compressive sensing concern random sampling matrices such as Gaussian and Bernoulli matrices. In common physically feasible signal acquisition and reconstruction scenarios such as super-resolution of images,...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Indian Journal of Science and Technology
سال: 2016
ISSN: 0974-5645,0974-6846
DOI: 10.17485/ijst/2016/v9i36/102114