Abstract
This article discusses the performance of the oracle receiver in recovering high dimensional sparse signals, which possesses the knowledge of the signals' support set. We consider a general framework, in which the sensing matrix and the measurements are disturbed simultaneously. The entries of the sensing matrix are i.i.d. Bernoulli random variables. We introduce the lower and upper bounds of the normalized mean square error of the reconstruction, which are proved to hold with high probability and verified by numerical simulations. The result is then compared with previous works on Gaussian sensing matrices. The average recovery error is derived as a generalization of the conclusion in [12] for the Gaussian ensemble and measurement noise only case.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
