Nonconvex Regularized Gradient Projection Sparse Reconstruction for Massive MIMO Channel Estimation

Novel sparse reconstruction algorithms are proposed for beamspace channel estimation in massive multiple-input multiple-output systems. The proposed algorithms minimize a least-squares objective having a nonconvex regularizer. This regularizer removes the penalties on a few large-magnitude elements...

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Vydané v:	IEEE transactions on communications Ročník 69; číslo 11; s. 7722 - 7735
Hlavní autori:	Wu, Pengxia, Cheng, Julian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.11.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Algorithms Approximation algorithms Channel estimation Convergence DC programming Downlink Fines & penalties Forecasting gradient projection Massive MIMO nonconvex optimization Optimization Programming Reconstruction sparse channel reconstruction
ISSN:	0090-6778, 1558-0857
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