Abstract
Compressed sensing deals with efficient recovery of analog signals from linear encodings. This paper presents a statistical study of compressed sensing by modeling the input signal as an i.i.d. process with known distribution. Three classes of encoders are considered, namely optimal nonlinear, optimal linear, and random linear encoders. Focusing on optimal decoders, we investigate the fundamental tradeoff between measurement rate and reconstruction fidelity gauged by error probability and noise sensitivity in the absence and presence of measurement noise, respectively. The optimal phase-transition threshold is determined as a functional of the input distribution and compared to suboptimal thresholds achieved by popular reconstruction algorithms. In particular, we show that Gaussian sensing matrices incur no penalty on the phase-transition threshold with respect to optimal nonlinear encoding. Our results also provide a rigorous justification of previous results based on replica heuristics in the weak-noise regime.
Original language | English (US) |
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Article number | 6228534 |
Pages (from-to) | 6241-6263 |
Number of pages | 23 |
Journal | IEEE Transactions on Information Theory |
Volume | 58 |
Issue number | 10 |
DOIs | |
State | Published - 2012 |
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Library and Information Sciences
Keywords
- Compressed sensing
- Rényi information dimension
- Shannon theory
- joint source-channel coding
- minimum mean-square error (MMSE) dimension
- phase transition
- random matrix