Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
KAUST Grant NumberOSR-2016-KKI-2899.
Online Publication Date2016-07-22
Print Publication Date2016-11
Permanent link to this recordhttp://hdl.handle.net/10754/622535
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AbstractBy 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.
CitationZaib A, Masood M, Ali A, Xu W, Al-Naffouri TY (2016) Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems. IEEE Transactions on Communications 64: 4607–4621. Available: http://dx.doi.org/10.1109/TCOMM.2016.2593924.
SponsorsThis 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.