TY - JOUR
T1 - Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems
AU - Zaib, Alam
AU - Masood, Mudassir
AU - Ali, Anum
AU - Xu, Weiyu
AU - Al-Naffouri, Tareq Y.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2016-KKI-2899.
Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2016-KKI-2899.
PY - 2016/7/22
Y1 - 2016/7/22
N2 - By virtue of large antenna arrays, massive MIMO systems have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. This paper addresses uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. We propose an efficient distributed minimum mean square error (MMSE) algorithm that can achieve near optimal channel estimates at low complexity by exploiting the strong spatial correlation among antenna array elements. The proposed method involves solving a reduced dimensional MMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring array elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. Furthermore, we use stochastic geometry to quantify the pilot contamination, and in turn use this information to analyze the effect of pilot contamination on channel MSE. The simulation results validate our analysis and show near optimal performance of the proposed estimation algorithms.
AB - By virtue of large antenna arrays, massive MIMO systems have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. This paper addresses uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. We propose an efficient distributed minimum mean square error (MMSE) algorithm that can achieve near optimal channel estimates at low complexity by exploiting the strong spatial correlation among antenna array elements. The proposed method involves solving a reduced dimensional MMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring array elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. Furthermore, we use stochastic geometry to quantify the pilot contamination, and in turn use this information to analyze the effect of pilot contamination on channel MSE. The simulation results validate our analysis and show near optimal performance of the proposed estimation algorithms.
UR - http://hdl.handle.net/10754/622535
UR - http://ieeexplore.ieee.org/document/7519073/
UR - http://www.scopus.com/inward/record.url?scp=84999828433&partnerID=8YFLogxK
U2 - 10.1109/TCOMM.2016.2593924
DO - 10.1109/TCOMM.2016.2593924
M3 - Article
SN - 0090-6778
VL - 64
SP - 4607
EP - 4621
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
IS - 11
ER -