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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on communications Ročník 69; číslo 11; s. 7722 - 7735
Hlavní autoři: Wu, Pengxia, Cheng, Julian
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.11.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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 from the conventional <inline-formula> <tex-math notation="LaTeX">\ell _{1} </tex-math></inline-formula>-norm regularizer, and thus it only forces penalties on the remaining elements that are expected to be zeros. Accurate and fast reconstructions can be achieved by performing gradient projection updates within the framework of difference of convex functions (DC) programming. A double-loop algorithm and a single-loop algorithm are proposed via different DC decompositions, and these two algorithms have distinct computational complexities and convergence rates. An extension algorithm is further proposed by designing new step sizes for the single-loop algorithm. The extension algorithm has a faster convergence rate and can achieve approximately the same level of accuracy as the proposed double-loop algorithm. Numerical results show significant advantages of the proposed algorithms over existing reconstruction algorithms in terms of reconstruction accuracies and runtimes. Compared with the benchmark channel estimation approaches, the proposed algorithms can achieve smaller channel reconstruction error and higher achievable spectral efficiency.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2021.3107582