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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Bibliographic Details
Published in:	IEEE transactions on communications Vol. 69; no. 11; pp. 7722 - 7735
Main Authors:	Wu, Pengxia, Cheng, Julian
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.11.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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