Compressed Sensing With Quantized Measurements

By A. Zymnis, Stephen P. Boyd, E.J. Candes. IEEE Signal Processing Letters, 2010

324 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2dab9b8c9c991baeebe82a224307633b47832915

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Richard Baraniuk
2. Yaniv Plan
3. Robert W. Heath
4. Mark A. Davenport
5. Jianhua Mo
6. Philip Schniter
7. Jason N. Laska
8. Petros T. Boufounos

In Computer Networks and Communications

1. Yaniv Plan
2. Robert W. Heath
3. Richard Baraniuk
4. Jianhua Mo
5. Jason N. Laska
6. Mark A. Davenport
7. Philip Schniter
8. Junil Choi

In Electrical and Electronic Engineering

1. Robert W. Heath
2. Philip Schniter
3. Jianhua Mo
4. Richard Baraniuk
5. Junil Choi
6. Jason N. Laska
7. Petros T. Boufounos
8. Mark A. Davenport

Citations in the corpus

• Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors (2011, background): “Several reconstruction techniques that specifically address CS quantization have also been proposed [21–26].”
• Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs (2016, methodology): “4We also tried the 1 -based method from [37], but we experienced numerical problems with their first-order method.”
• Near Maximum-Likelihood Detector and Channel Estimator for Uplink Multiuser Massive MIMO Systems With One-Bit ADCs (2015, methodology): “ate the problem as ˇx(2) R,ML = argmax ´x R∈R2K×1 k´x Rk2≤K X2 i=1 XN r n=1 logΦ p 2ρegT R,n,i x´R (28) which is a convex optimization problem that can be efficientl y solved [44]. Similar to [30], [45], we provide a simple yet effective iterative approach to solve (28) in Algorithm 1. In Step 1, we”
• Matrix recovery from quantized and corrupted measurements (2014, unknown): “, [9–11, 25]).”
• 1-Bit Matrix Completion (2012, background): “This can be easily checked in the case of the logistic model and can also be verified in the case of the probit model (e.g., see [68]).”
• One‐Bit Compressed Sensing by Linear Programming (2011, unknown): “antized compressed sensing problem to the noisy classical compressed sensing problem. Further, in some cases the theory and algorithms of noisy compressed sensing may be adapted to this problem as in [28, 11, 17, 25]; the method of quantization may be specialized in order to minimize the recovery er”