Please use this identifier to cite or link to this item:
Title: Compressive sensing based low complexity user selection for massive MIMO systems
Authors: Yılmaz, Saadet Simay
Özbek, Berna
Keywords: compressive sensing
Massive MIMO
Sparse channel
User selection
Publisher: Institute of Electrical and Electronics Engineers
Abstract: Massive Multiple-input Multiple-output (MIMO) is widely considered as a key enabler of the next-generation networks. In these systems, user selection strategies are important to achieve spatial diversity and maximize spectral efficiency. In this paper, a user selection algorithm is proposed with the reconstruction of the sparse Massive MIMO channel using Compressive Sensing (CS) algorithm. The proposed algorithm eliminates the users based on the channel correlation by employing the CS algorithm which reduces the feedback overhead in the system. The simulation results show that the proposed algorithm outperforms the traditional user selection algorithms in terms of sum data rate and computational complexity. Moreover, the effects of the sparsity level and feedback measurement on the performance are examined. © 2020 IEEE.
ISBN: 978-172815207-3
ISSN: 1550-2252
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Files in This Item:
File SizeFormat 
Compressive_Sensing.pdf132.39 kBAdobe PDFView/Open
Show full item record

CORE Recommender


checked on Jun 29, 2024

Page view(s)

checked on Jul 1, 2024


checked on Jul 1, 2024

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.