Name
Abstract | ||
|---|---|---|
In this paper, we propose a compressed sensing (CS) framework that consists of three parts: a unitnorm tight frame (UTF), a random diagonal matrix and a column-wise orthonormal matrix. We prove that this structure satisfies the restricted isometry property (RIP) with high probability if the number of measurements m = O(s log2 s log2 n) for s-sparse signals of length n and if the column-wise orthonormal matrix is bounded. Some existing structured sensing models can be studied under this framework, which then gives tighter bounds on the required number of measurements to satisfy the RIP. More importantly, we propose several structured sensing models by appealing to this unified framework, such as a general sensing model with arbitrary/determinisic subsamplers, a fast and efficient block compressed sensing scheme, and structured sensing matrices with deterministic phase modulations, all of which can lead to improvements on practical applications. In particular, one of the constructions is applied to simplify the transceiver design of CS-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TSP.2015.2425809 | Signal Processing, IEEE Transactions |
Keywords | Field | DocType |
compressed sensing,golay sequence,arbitrary/ deterministic subsampling,coherence analysis,phase modulation,structured sensing matrix,unit-norm tight frame | Topology,Orthogonal matrix,Matrix (mathematics),Control theory,Theoretical computer science,Diagonal matrix,Orthogonal frequency-division multiplexing,Restricted isometry property,Mathematics,Compressed sensing,Signal reconstruction,Bounded function | Journal |
Volume | Issue | ISSN |
PP | 99 | 1053-587X |
Citations | PageRank | References |
4 | 0.42 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Zhang | 1 | 116 | 13.26 |
| Lu Gan | 2 | 324 | 25.46 |
| Sumei Sun | 3 | 1276 | 144.61 |
| Cong Ling | 4 | 688 | 68.90 |