Array LDPC Code-based Compressive Sensing

Lotfi, Mahsa and Vidyasagar, Mathukumalli (2018) Array LDPC Code-based Compressive Sensing. In: 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018.

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In this paper, we focus on the problem of compressive sensing using binary measurement matrices, and basis pursuit as the recovery algorithm. We obtain new lower bounds on the number of samples to achieve robust sparse recovery using binary matrices and derive sufficient conditions for a binary matrix with fixed column-weight to satisfy the robust null space property. Next we prove that any column-regular binary matrix with girth 6 has nearly optimal number of measurements. Then we show that the parity check matrices of array LDPC codes are nearly optimal in the sense of having girth six and almost satisfying the lower bound on the number of samples. Array code parity check matrices demonstrate an example of binary matrices that achieve guaranteed recovery via robust null-space property and in practice for n \leq 10^{6} provide faster recovery compared to the Gaussian counterpart. This is an extended abstract without proofs. The full paper with additional details can be found in [1].

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Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Indexed in Scopus
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Library Staff
Date Deposited: 23 Oct 2019 09:32
Last Modified: 23 Oct 2019 09:32
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