TY - GEN
T1 - Sparse channel estimation with regularization methods in massive mimo systems
AU - Peken, Ture
AU - Tandon, Ravi
AU - Bose, Tamal
N1 - Funding Information:
This project was partially supported by the Broadband Wireless Access and Applications Center (BWAC); NSF Award No. 1265960.
Publisher Copyright:
© held by the author; distribution rights International Foundation for Telemetering.
PY - 2018
Y1 - 2018
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
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M3 - Conference contribution
AN - SCOPUS:85062732385
T3 - Proceedings of the International Telemetering Conference
BT - 54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018
PB - International Foundation for Telemetering
T2 - 54th Annual International Telemetering Conference and Technical Exhibition: Reliable and Secure Data, Links and Networks, ITC 2018
Y2 - 5 November 2018 through 8 November 2018
ER -