Compressive sensing based pilot reduction technique for massive MIMO systems

Jun Won Choi; Byonghyo Shim

doi:10.1109/ITA.2015.7308974

Compressive sensing based pilot reduction technique for massive MIMO systems

Jun Won Choi, Byonghyo Shim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

Massive multi-input multi-output (MIMO) technique deploys a number of transmit antennas in base-station (BS) to support large number of users and high data throughput. Since BS needs to acquire channel state information from all transmit antennas, substantial amount of downlink pilot signals is required. In this paper, we suggest a new downlink pilot allocation strategy, inspired by the compressed sensing principle, that reduces the density of the pilot significantly. Key observation in the proposed approach is that the sparse structure of the channel impulse response (CIR) tends to change slower than the OFDM symbol rate. Through computer simulations, we show that the proposed scheme outperforms the conventional compressed sensing methods, achieving the performance bound provided by the Oracle-based Kalman smoother.

Original language	English
Title of host publication	2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	115-118
Number of pages	4
ISBN (Print)	9781479971954
DOIs	https://doi.org/10.1109/ITA.2015.7308974
Publication status	Published - 2015 Oct 27
Externally published	Yes
Event	Information Theory and Applications Workshop, ITA 2015 - San Diego, United States Duration: 2015 Feb 1 → 2015 Feb 6

Other

Other	Information Theory and Applications Workshop, ITA 2015
Country/Territory	United States
City	San Diego
Period	15/2/1 → 15/2/6

Keywords

OFDM
Q measurement
Signal to noise ratio

ASJC Scopus subject areas

Computer Science Applications
Information Systems

Access to Document

10.1109/ITA.2015.7308974

Cite this

Choi, JW & Shim, B 2015, Compressive sensing based pilot reduction technique for massive MIMO systems. in 2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings., 7308974, Institute of Electrical and Electronics Engineers Inc., pp. 115-118, Information Theory and Applications Workshop, ITA 2015, San Diego, United States, 15/2/1. https://doi.org/10.1109/ITA.2015.7308974

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title = "Compressive sensing based pilot reduction technique for massive MIMO systems",

abstract = "Massive multi-input multi-output (MIMO) technique deploys a number of transmit antennas in base-station (BS) to support large number of users and high data throughput. Since BS needs to acquire channel state information from all transmit antennas, substantial amount of downlink pilot signals is required. In this paper, we suggest a new downlink pilot allocation strategy, inspired by the compressed sensing principle, that reduces the density of the pilot significantly. Key observation in the proposed approach is that the sparse structure of the channel impulse response (CIR) tends to change slower than the OFDM symbol rate. Through computer simulations, we show that the proposed scheme outperforms the conventional compressed sensing methods, achieving the performance bound provided by the Oracle-based Kalman smoother.",

keywords = "OFDM, Q measurement, Signal to noise ratio",

author = "Choi, {Jun Won} and Byonghyo Shim",

year = "2015",

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doi = "10.1109/ITA.2015.7308974",

language = "English",

isbn = "9781479971954",

pages = "115--118",

booktitle = "2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "Information Theory and Applications Workshop, ITA 2015 ; Conference date: 01-02-2015 Through 06-02-2015",

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AU - Choi, Jun Won

AU - Shim, Byonghyo

PY - 2015/10/27

Y1 - 2015/10/27

N2 - Massive multi-input multi-output (MIMO) technique deploys a number of transmit antennas in base-station (BS) to support large number of users and high data throughput. Since BS needs to acquire channel state information from all transmit antennas, substantial amount of downlink pilot signals is required. In this paper, we suggest a new downlink pilot allocation strategy, inspired by the compressed sensing principle, that reduces the density of the pilot significantly. Key observation in the proposed approach is that the sparse structure of the channel impulse response (CIR) tends to change slower than the OFDM symbol rate. Through computer simulations, we show that the proposed scheme outperforms the conventional compressed sensing methods, achieving the performance bound provided by the Oracle-based Kalman smoother.

AB - Massive multi-input multi-output (MIMO) technique deploys a number of transmit antennas in base-station (BS) to support large number of users and high data throughput. Since BS needs to acquire channel state information from all transmit antennas, substantial amount of downlink pilot signals is required. In this paper, we suggest a new downlink pilot allocation strategy, inspired by the compressed sensing principle, that reduces the density of the pilot significantly. Key observation in the proposed approach is that the sparse structure of the channel impulse response (CIR) tends to change slower than the OFDM symbol rate. Through computer simulations, we show that the proposed scheme outperforms the conventional compressed sensing methods, achieving the performance bound provided by the Oracle-based Kalman smoother.

KW - OFDM

KW - Q measurement

KW - Signal to noise ratio

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BT - 2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings

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T2 - Information Theory and Applications Workshop, ITA 2015

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Compressive sensing based pilot reduction technique for massive MIMO systems

Abstract

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Keywords

ASJC Scopus subject areas

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