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Efficient Noise Variance Estimation under Pilot Contamination for Large-Scale MIMO Systems

Tutkimustuotosvertaisarvioitu

Standard

Efficient Noise Variance Estimation under Pilot Contamination for Large-Scale MIMO Systems. / Iscar Vergara, Jorge; Guvenc, Ismail; Dikmese, Sener; Rupasinghe, Nadisanka.

julkaisussa: IEEE Transactions on Vehicular Technology, Vuosikerta 67, Nro 4, 2018, s. 2982-2996.

Tutkimustuotosvertaisarvioitu

Harvard

Iscar Vergara, J, Guvenc, I, Dikmese, S & Rupasinghe, N 2018, 'Efficient Noise Variance Estimation under Pilot Contamination for Large-Scale MIMO Systems', IEEE Transactions on Vehicular Technology, Vuosikerta. 67, Nro 4, Sivut 2982-2996. https://doi.org/10.1109/TVT.2017.2766226

APA

Iscar Vergara, J., Guvenc, I., Dikmese, S., & Rupasinghe, N. (2018). Efficient Noise Variance Estimation under Pilot Contamination for Large-Scale MIMO Systems. IEEE Transactions on Vehicular Technology, 67(4), 2982-2996. https://doi.org/10.1109/TVT.2017.2766226

Vancouver

Iscar Vergara J, Guvenc I, Dikmese S, Rupasinghe N. Efficient Noise Variance Estimation under Pilot Contamination for Large-Scale MIMO Systems. IEEE Transactions on Vehicular Technology. 2018;67(4):2982-2996. https://doi.org/10.1109/TVT.2017.2766226

Author

Iscar Vergara, Jorge ; Guvenc, Ismail ; Dikmese, Sener ; Rupasinghe, Nadisanka. / Efficient Noise Variance Estimation under Pilot Contamination for Large-Scale MIMO Systems. Julkaisussa: IEEE Transactions on Vehicular Technology. 2018 ; Vuosikerta 67, Nro 4. Sivut 2982-2996.

Bibtex - Lataa

@article{b3b8afb65d2c461ebe8352e35616b047,
title = "Efficient Noise Variance Estimation under Pilot Contamination for Large-Scale MIMO Systems",
abstract = "Massive multiple-input multiple-output (MIMO) is expected to be one of the enabling technologies for fifth generation (5G) cellular networks. One of the major challenges in massive MIMO systems is the accurate joint estimation of the channel and noise variance, which significantly affects the performance of wireless communications in practical scenarios. In this paper, we first derive a novel maximum likelihood (ML) estimator for the noise variance at the receiver of massive MIMO systems considering practical impairments such as pilot contamination. Then, this estimate is used to compute the minimum mean square error (MMSE) estimate of the channel. In order to measure the performance of the proposed noise variance estimator, we derive the corresponding Cramer-Rao lower bound (CRLB). Simulation results show that the estimator is efficient in certain scenarios, outperforming existing approaches in the literature. Furthermore, we develop the estimator and CRLB for equal and different noise variance at the receive antennas. Although the proposed estimator is valid for all antenna array sizes, its use is particularly effective for massive MIMO systems.",
keywords = "5G, Channel estimation, Contamination, Covariance matrices, CRLB, massive MIMO, maximum likelihood, Maximum likelihood estimation, method of moments, MIMO, MMSE channel estimation, noise variance estimation, Partial transmit sequences, pilot contamination",
author = "{Iscar Vergara}, Jorge and Ismail Guvenc and Sener Dikmese and Nadisanka Rupasinghe",
year = "2018",
doi = "10.1109/TVT.2017.2766226",
language = "English",
volume = "67",
pages = "2982--2996",
journal = "IEEE Transactions on Vehicular Technology",
issn = "0018-9545",
publisher = "Institute of Electrical and Electronics Engineers",
number = "4",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Efficient Noise Variance Estimation under Pilot Contamination for Large-Scale MIMO Systems

AU - Iscar Vergara, Jorge

AU - Guvenc, Ismail

AU - Dikmese, Sener

AU - Rupasinghe, Nadisanka

PY - 2018

Y1 - 2018

N2 - Massive multiple-input multiple-output (MIMO) is expected to be one of the enabling technologies for fifth generation (5G) cellular networks. One of the major challenges in massive MIMO systems is the accurate joint estimation of the channel and noise variance, which significantly affects the performance of wireless communications in practical scenarios. In this paper, we first derive a novel maximum likelihood (ML) estimator for the noise variance at the receiver of massive MIMO systems considering practical impairments such as pilot contamination. Then, this estimate is used to compute the minimum mean square error (MMSE) estimate of the channel. In order to measure the performance of the proposed noise variance estimator, we derive the corresponding Cramer-Rao lower bound (CRLB). Simulation results show that the estimator is efficient in certain scenarios, outperforming existing approaches in the literature. Furthermore, we develop the estimator and CRLB for equal and different noise variance at the receive antennas. Although the proposed estimator is valid for all antenna array sizes, its use is particularly effective for massive MIMO systems.

AB - Massive multiple-input multiple-output (MIMO) is expected to be one of the enabling technologies for fifth generation (5G) cellular networks. One of the major challenges in massive MIMO systems is the accurate joint estimation of the channel and noise variance, which significantly affects the performance of wireless communications in practical scenarios. In this paper, we first derive a novel maximum likelihood (ML) estimator for the noise variance at the receiver of massive MIMO systems considering practical impairments such as pilot contamination. Then, this estimate is used to compute the minimum mean square error (MMSE) estimate of the channel. In order to measure the performance of the proposed noise variance estimator, we derive the corresponding Cramer-Rao lower bound (CRLB). Simulation results show that the estimator is efficient in certain scenarios, outperforming existing approaches in the literature. Furthermore, we develop the estimator and CRLB for equal and different noise variance at the receive antennas. Although the proposed estimator is valid for all antenna array sizes, its use is particularly effective for massive MIMO systems.

KW - 5G

KW - Channel estimation

KW - Contamination

KW - Covariance matrices

KW - CRLB

KW - massive MIMO

KW - maximum likelihood

KW - Maximum likelihood estimation

KW - method of moments

KW - MIMO

KW - MMSE channel estimation

KW - noise variance estimation

KW - Partial transmit sequences

KW - pilot contamination

U2 - 10.1109/TVT.2017.2766226

DO - 10.1109/TVT.2017.2766226

M3 - Article

VL - 67

SP - 2982

EP - 2996

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

SN - 0018-9545

IS - 4

ER -