Sparse channel estimation with regularization methods in massive mimo systems

Ture Peken, Ravi Tandon, Tamal Bose

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Massive multiple-input multiple-output (MIMO) technology has recently gained a lot of attention as a candidate technology for the next generation wireless systems. With a higher number of antennas, pilot-based channel estimation faces a limitation in the number of orthogonal pilots to be used among users in all cells. Sparse channel estimation by using regularization methods can reduce the pilots compared to pilot-based channel estimation. In this paper, we study two regularization methods: least absolute shrinkage and selection operator (lasso) and elastic net. We investigate the performance of least squares (LS), lasso, and elastic net when the sparsity of the channel changes over time. We study the optimum tuning parameters for lasso and elastic net based channel estimators to achieve the best performance with the different number of pilots and values of signal-to-noise ratio (SNR). Finally, we present the asymptotic analysis of LS, lasso, and elastic net based channel estimators.

Original languageEnglish (US)
Title of host publication54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018
Subtitle of host publicationReliable and Secure Data, Links and Networks
PublisherInternational Foundation for Telemetering
ISBN (Electronic)9780000000002
StatePublished - Jan 1 2018
Event54th Annual International Telemetering Conference and Technical Exhibition: Reliable and Secure Data, Links and Networks, ITC 2018 - Glendale, United States
Duration: Nov 5 2018Nov 8 2018

Publication series

NameProceedings of the International Telemetering Conference
Volume2018-November
ISSN (Print)0884-5123

Conference

Conference54th Annual International Telemetering Conference and Technical Exhibition: Reliable and Secure Data, Links and Networks, ITC 2018
CountryUnited States
CityGlendale
Period11/5/1811/8/18

Fingerprint

Channel estimation
shrinkage
operators
estimators
Asymptotic analysis
Signal to noise ratio
MIMO (control systems)
Tuning
Antennas
signal to noise ratios
antennas
tuning
cells

Keywords

  • Elastic net
  • Lasso
  • Massive MIMO
  • Sparse channel estimation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation
  • Computer Networks and Communications
  • Signal Processing

Cite this

Peken, T., Tandon, R., & Bose, T. (2018). Sparse channel estimation with regularization methods in massive mimo systems. In 54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018: Reliable and Secure Data, Links and Networks (Proceedings of the International Telemetering Conference; Vol. 2018-November). International Foundation for Telemetering.

Sparse channel estimation with regularization methods in massive mimo systems. / Peken, Ture; Tandon, Ravi; Bose, Tamal.

54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018: Reliable and Secure Data, Links and Networks. International Foundation for Telemetering, 2018. (Proceedings of the International Telemetering Conference; Vol. 2018-November).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Peken, T, Tandon, R & Bose, T 2018, Sparse channel estimation with regularization methods in massive mimo systems. in 54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018: Reliable and Secure Data, Links and Networks. Proceedings of the International Telemetering Conference, vol. 2018-November, International Foundation for Telemetering, 54th Annual International Telemetering Conference and Technical Exhibition: Reliable and Secure Data, Links and Networks, ITC 2018, Glendale, United States, 11/5/18.
Peken T, Tandon R, Bose T. Sparse channel estimation with regularization methods in massive mimo systems. In 54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018: Reliable and Secure Data, Links and Networks. International Foundation for Telemetering. 2018. (Proceedings of the International Telemetering Conference).
Peken, Ture ; Tandon, Ravi ; Bose, Tamal. / Sparse channel estimation with regularization methods in massive mimo systems. 54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018: Reliable and Secure Data, Links and Networks. International Foundation for Telemetering, 2018. (Proceedings of the International Telemetering Conference).
@inproceedings{ad668d9f024a4faa9774d689c0adcc9a,
title = "Sparse channel estimation with regularization methods in massive mimo systems",
abstract = "Massive multiple-input multiple-output (MIMO) technology has recently gained a lot of attention as a candidate technology for the next generation wireless systems. With a higher number of antennas, pilot-based channel estimation faces a limitation in the number of orthogonal pilots to be used among users in all cells. Sparse channel estimation by using regularization methods can reduce the pilots compared to pilot-based channel estimation. In this paper, we study two regularization methods: least absolute shrinkage and selection operator (lasso) and elastic net. We investigate the performance of least squares (LS), lasso, and elastic net when the sparsity of the channel changes over time. We study the optimum tuning parameters for lasso and elastic net based channel estimators to achieve the best performance with the different number of pilots and values of signal-to-noise ratio (SNR). Finally, we present the asymptotic analysis of LS, lasso, and elastic net based channel estimators.",
keywords = "Elastic net, Lasso, Massive MIMO, Sparse channel estimation",
author = "Ture Peken and Ravi Tandon and Tamal Bose",
year = "2018",
month = "1",
day = "1",
language = "English (US)",
series = "Proceedings of the International Telemetering Conference",
publisher = "International Foundation for Telemetering",
booktitle = "54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018",

}

TY - GEN

T1 - Sparse channel estimation with regularization methods in massive mimo systems

AU - Peken, Ture

AU - Tandon, Ravi

AU - Bose, Tamal

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Massive multiple-input multiple-output (MIMO) technology has recently gained a lot of attention as a candidate technology for the next generation wireless systems. With a higher number of antennas, pilot-based channel estimation faces a limitation in the number of orthogonal pilots to be used among users in all cells. Sparse channel estimation by using regularization methods can reduce the pilots compared to pilot-based channel estimation. In this paper, we study two regularization methods: least absolute shrinkage and selection operator (lasso) and elastic net. We investigate the performance of least squares (LS), lasso, and elastic net when the sparsity of the channel changes over time. We study the optimum tuning parameters for lasso and elastic net based channel estimators to achieve the best performance with the different number of pilots and values of signal-to-noise ratio (SNR). Finally, we present the asymptotic analysis of LS, lasso, and elastic net based channel estimators.

AB - Massive multiple-input multiple-output (MIMO) technology has recently gained a lot of attention as a candidate technology for the next generation wireless systems. With a higher number of antennas, pilot-based channel estimation faces a limitation in the number of orthogonal pilots to be used among users in all cells. Sparse channel estimation by using regularization methods can reduce the pilots compared to pilot-based channel estimation. In this paper, we study two regularization methods: least absolute shrinkage and selection operator (lasso) and elastic net. We investigate the performance of least squares (LS), lasso, and elastic net when the sparsity of the channel changes over time. We study the optimum tuning parameters for lasso and elastic net based channel estimators to achieve the best performance with the different number of pilots and values of signal-to-noise ratio (SNR). Finally, we present the asymptotic analysis of LS, lasso, and elastic net based channel estimators.

KW - Elastic net

KW - Lasso

KW - Massive MIMO

KW - Sparse channel estimation

UR - http://www.scopus.com/inward/record.url?scp=85062732385&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062732385&partnerID=8YFLogxK

M3 - Conference contribution

T3 - Proceedings of the International Telemetering Conference

BT - 54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018

PB - International Foundation for Telemetering

ER -