Sub-linear time compressed sensing for support recovery using left and right regular sparse-graph codes

In [1], [2], two schemes have been proposed to recover the support of a K-sparse N-dimensional signal from noisy linear measurements. Both schemes use left-regular sparse-graph code based sensing matrices and a simple peeling-based decoding algorithm. Both the schemes require O(K logN) measurements...

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Veröffentlicht in:2016 IEEE Information Theory Workshop (ITW) S. 429 - 433
Hauptverfasser: Vem, Avinash, Janakiraman, Nagaraj Thenkarai, Narayanan, Krishna
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2016
Schlagworte:
Complexity theory
Compressed sensing
Conferences
Decoding
Noise measurement
Sensors
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Zusammenfassung:In [1], [2], two schemes have been proposed to recover the support of a K-sparse N-dimensional signal from noisy linear measurements. Both schemes use left-regular sparse-graph code based sensing matrices and a simple peeling-based decoding algorithm. Both the schemes require O(K logN) measurements and the first scheme require O(N logN) computations whereas the second scheme requires O(K logN) computations (sub-linear time complexity when K is sub-linear in N). We show that by replacing the left-regular ensemble with left and right regular ensemble, we can reduce the number of measurements required of these schemes to the optimal order of O(K log N/K) with decoding complexities of O(K log N/K) and O(N log N/K), respectively.
DOI:10.1109/ITW.2016.7606870