130 research outputs found
Extended Successive Convex Approximation for Phase Retrieval with Dictionary Learning
Phase retrieval aims at reconstructing unknown signals from magnitude
measurements of linear mixtures. In this paper, we consider the phase retrieval
with dictionary learning problem, which includes an additional prior
information that the measured signal admits a sparse representation over an
unknown dictionary. The task is to jointly estimate the dictionary and the
sparse representation from magnitude-only measurements. To this end, we study
two complementary formulations and develop efficient parallel algorithms by
extending the successive convex approximation framework using a smooth
majorization. The first algorithm is termed compact-SCAphase and is preferable
in the case of less diverse mixture models. It employs a compact formulation
that avoids the use of auxiliary variables. The proposed algorithm is highly
scalable and has reduced parameter tuning cost. The second algorithm, referred
to as SCAphase, uses auxiliary variables and is favorable in the case of highly
diverse mixture models. It also renders simple incorporation of additional side
constraints. The performance of both methods is evaluated when applied to blind
sparse channel estimation from subband magnitude measurements in a
multi-antenna random access network. Simulation results demonstrate the
efficiency of the proposed techniques compared to state-of-the-art methods.Comment: This work has been submitted to the IEEE Transactions on Signal
Processing for possible publication. Copyright may be transferred without
notice, after which this version may no longer be accessibl
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