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 language | English (US) |
|---|---|
| Title of host publication | 54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018 |
| Subtitle of host publication | Reliable and Secure Data, Links and Networks |
| Publisher | International Foundation for Telemetering |
| ISBN (Electronic) | 9780000000002 |
| State | Published - Jan 1 2018 |
| Event | 54th Annual International Telemetering Conference and Technical Exhibition: Reliable and Secure Data, Links and Networks, ITC 2018 - Glendale, United States Duration: Nov 5 2018 → Nov 8 2018 |
Publication series
| Name | Proceedings of the International Telemetering Conference |
|---|---|
| Volume | 2018-November |
| ISSN (Print) | 0884-5123 |
Conference
| Conference | 54th Annual International Telemetering Conference and Technical Exhibition: Reliable and Secure Data, Links and Networks, ITC 2018 |
|---|---|
| Country | United States |
| City | Glendale |
| Period | 11/5/18 → 11/8/18 |
Fingerprint
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
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 proceeding › Conference contribution
}
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 -