Improved sparse multiuser detection based on modulation-alphabets exploitation

Messai, Malek; Bey, Abdeldjalil Aïssa El; Amis, Karine; Guilloud, Frédéric

doi:10.1016/j.dsp.2017.08.011

Cited by 1 publication

(2 citation statements)

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“…In addition to sparsity and depending on the application, other constraints can be considered in the recovery problem formulation. For example in multi-band signals [14], face recognition [15], spectrum sensing, equalization of sparse communication channels, the non-zero values are not only few, but they are also clustered in blocks [16]- [18]. Such a signal is said to be block-sparse.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of Finite-Alphabet Block-Sparse Signals From MAP Support Detection

et al. 2019

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This paper addresses finite-alphabet block-sparse signal recovery by considering support detection and data estimation separately. To this aim, we propose a maximum a posteriori (MAP) support detection criterion that takes into account the finite alphabet of the signal as a constraint. We then incorporate the MAP criterion in a compressed sensing detector based on a greedy algorithm for support estimation. We also propose to consider the finite-alphabet property of the signal in the bound-constrained least-squares optimization algorithm for data estimation. The MAP support detection criterion is investigated in two different contexts: independent linear modulation symbols and dependent binary continuous phase modulation (CPM) symbols. The simulations are carried out in the context of sporadic multiuser communications and show the efficiency of proposed algorithms compared to selected state-of-the-art algorithms both in terms of support detection and data estimation.

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Section: Introductionmentioning

confidence: 99%

“…However a discrete set (non-zero values) means a non-convex solution space, which complicates its consideration in the problem formulation. This is the reason why most published CS algorithms, which are dedicated to finite-alphabet recovery, propose to relax the discrete set constraint to convex constraints [18]- [21].…”

Section: Introductionmentioning

confidence: 99%