<P>We consider a compressed sensing (CS) framework over finite fields. We derive sufficient and necessary conditions for recovery of sparse signals in terms of the ambient dimension of the signal space, the sparsity of the signal, the number of ...
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https://www.riss.kr/link?id=A107680473
2013
-
SCOPUS,SCIE
학술저널
1976-1979(4쪽)
0
상세조회0
다운로드다국어 초록 (Multilingual Abstract)
<P>We consider a compressed sensing (CS) framework over finite fields. We derive sufficient and necessary conditions for recovery of sparse signals in terms of the ambient dimension of the signal space, the sparsity of the signal, the number of ...
<P>We consider a compressed sensing (CS) framework over finite fields. We derive sufficient and necessary conditions for recovery of sparse signals in terms of the ambient dimension of the signal space, the sparsity of the signal, the number of measurements, and the field size. We show that the sufficient condition coincides with the necessary condition if the sensing matrix is sufficiently dense while both the length of the signal and the field size grow to infinity. One of the interesting conclusions includes that unless the signal is very sparse, the sensing matrix does not have to be dense to have the upper bound coincide with the lower bound.</P>
Optimal Power Allocation in an 'Off' Spectrum Sensing Interval for Cognitive Radio