Pilot Decontamination Using Coordinated Wiener Predictor in Massive-MIMO System

Mohammad Hasbullah Mazlan, Ehab Ali, Athirah Mohd Ramly, Rosdiadee Nordin, Mahamod Ismail, Aduwati Sali

Research output: Contribution to journalArticle

Abstract

The massive multiple-input-multiple-output (MIMO) technology relies on many antennas at base stations to offer high throughput to meet 5G network requirements. However, each base station requires accurate channel state information during channel estimation. Pilot contamination is one of the significant challenges that limit higher order MIMO deployment because it causes channel estimation error. In this paper, a Wiener predictor (WP) using the temporal-based prediction technique was first proposed and then extended as the coordinated WP (CoWP). The WP method is based on prediction using information that has been stored and processed at the base station from a modified subframe, whereas the extended CoWP method requires neighboring base station coordination to assign collection and prediction with other base stations. Results showed a reduction in channel estimation error due to pilot contamination after the Wiener-based prediction and coordinated base station techniques were used. The proposed CoWP technique can increase the performance of channel estimation error by up to 25 and 30 dB relative to the performance of other research technique and conventional MMSE channel estimation. This paper indicates that the pilot contamination effect can be minimized by exploiting the temporal dimension, and this approach increases the number of MIMO order for future 5G base stations, which currently limited due to pilot contamination.

Original languageEnglish
Article number8537902
Pages (from-to)73180-73190
Number of pages11
JournalIEEE Access
Volume6
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint

Decontamination
Base stations
Channel estimation
Contamination
Channel state information
Throughput
Antennas

Keywords

  • Channel estimation
  • large-scale antenna
  • massive-MIMO
  • pilot contamination
  • Wiener filter

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Cite this

Pilot Decontamination Using Coordinated Wiener Predictor in Massive-MIMO System. / Mazlan, Mohammad Hasbullah; Ali, Ehab; Mohd Ramly, Athirah; Nordin, Rosdiadee; Ismail, Mahamod; Sali, Aduwati.

In: IEEE Access, Vol. 6, 8537902, 01.01.2018, p. 73180-73190.

Research output: Contribution to journalArticle

Mazlan, Mohammad Hasbullah ; Ali, Ehab ; Mohd Ramly, Athirah ; Nordin, Rosdiadee ; Ismail, Mahamod ; Sali, Aduwati. / Pilot Decontamination Using Coordinated Wiener Predictor in Massive-MIMO System. In: IEEE Access. 2018 ; Vol. 6. pp. 73180-73190.
@article{622ffc05eb5e42458cb87ef0cc8ef4f9,
title = "Pilot Decontamination Using Coordinated Wiener Predictor in Massive-MIMO System",
abstract = "The massive multiple-input-multiple-output (MIMO) technology relies on many antennas at base stations to offer high throughput to meet 5G network requirements. However, each base station requires accurate channel state information during channel estimation. Pilot contamination is one of the significant challenges that limit higher order MIMO deployment because it causes channel estimation error. In this paper, a Wiener predictor (WP) using the temporal-based prediction technique was first proposed and then extended as the coordinated WP (CoWP). The WP method is based on prediction using information that has been stored and processed at the base station from a modified subframe, whereas the extended CoWP method requires neighboring base station coordination to assign collection and prediction with other base stations. Results showed a reduction in channel estimation error due to pilot contamination after the Wiener-based prediction and coordinated base station techniques were used. The proposed CoWP technique can increase the performance of channel estimation error by up to 25 and 30 dB relative to the performance of other research technique and conventional MMSE channel estimation. This paper indicates that the pilot contamination effect can be minimized by exploiting the temporal dimension, and this approach increases the number of MIMO order for future 5G base stations, which currently limited due to pilot contamination.",
keywords = "Channel estimation, large-scale antenna, massive-MIMO, pilot contamination, Wiener filter",
author = "Mazlan, {Mohammad Hasbullah} and Ehab Ali and {Mohd Ramly}, Athirah and Rosdiadee Nordin and Mahamod Ismail and Aduwati Sali",
year = "2018",
month = "1",
day = "1",
doi = "10.1109/ACCESS.2018.2881743",
language = "English",
volume = "6",
pages = "73180--73190",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Pilot Decontamination Using Coordinated Wiener Predictor in Massive-MIMO System

AU - Mazlan, Mohammad Hasbullah

AU - Ali, Ehab

AU - Mohd Ramly, Athirah

AU - Nordin, Rosdiadee

AU - Ismail, Mahamod

AU - Sali, Aduwati

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The massive multiple-input-multiple-output (MIMO) technology relies on many antennas at base stations to offer high throughput to meet 5G network requirements. However, each base station requires accurate channel state information during channel estimation. Pilot contamination is one of the significant challenges that limit higher order MIMO deployment because it causes channel estimation error. In this paper, a Wiener predictor (WP) using the temporal-based prediction technique was first proposed and then extended as the coordinated WP (CoWP). The WP method is based on prediction using information that has been stored and processed at the base station from a modified subframe, whereas the extended CoWP method requires neighboring base station coordination to assign collection and prediction with other base stations. Results showed a reduction in channel estimation error due to pilot contamination after the Wiener-based prediction and coordinated base station techniques were used. The proposed CoWP technique can increase the performance of channel estimation error by up to 25 and 30 dB relative to the performance of other research technique and conventional MMSE channel estimation. This paper indicates that the pilot contamination effect can be minimized by exploiting the temporal dimension, and this approach increases the number of MIMO order for future 5G base stations, which currently limited due to pilot contamination.

AB - The massive multiple-input-multiple-output (MIMO) technology relies on many antennas at base stations to offer high throughput to meet 5G network requirements. However, each base station requires accurate channel state information during channel estimation. Pilot contamination is one of the significant challenges that limit higher order MIMO deployment because it causes channel estimation error. In this paper, a Wiener predictor (WP) using the temporal-based prediction technique was first proposed and then extended as the coordinated WP (CoWP). The WP method is based on prediction using information that has been stored and processed at the base station from a modified subframe, whereas the extended CoWP method requires neighboring base station coordination to assign collection and prediction with other base stations. Results showed a reduction in channel estimation error due to pilot contamination after the Wiener-based prediction and coordinated base station techniques were used. The proposed CoWP technique can increase the performance of channel estimation error by up to 25 and 30 dB relative to the performance of other research technique and conventional MMSE channel estimation. This paper indicates that the pilot contamination effect can be minimized by exploiting the temporal dimension, and this approach increases the number of MIMO order for future 5G base stations, which currently limited due to pilot contamination.

KW - Channel estimation

KW - large-scale antenna

KW - massive-MIMO

KW - pilot contamination

KW - Wiener filter

UR - http://www.scopus.com/inward/record.url?scp=85056707596&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85056707596&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2018.2881743

DO - 10.1109/ACCESS.2018.2881743

M3 - Article

VL - 6

SP - 73180

EP - 73190

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 8537902

ER -