Compressed channel feedback for correlateci massive MIMO systems

Min Soo Sim, Chan Byoung Chae

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. As a sparsifying basis, prior work has applied compressive sensing (CS) techniques with the two-dimensional discrete cosine transform (2D-DCT) and the instantaneous Karhunen-Loeve transform (KLT). 2D-DCT fails, however, to reflect the spatial correlation and channel conditions. Instantaneous KLT requires perfect CSI, which means it is not feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel and a new reconstruction algorithm for CS. We also suggest that dimensionality reduction is more proper to compress, and compare performance with the conventional method. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single user) and point-to-multi-point (multiuser) scenarios.

Original languageEnglish
Title of host publication2014 IEEE Globecom Workshops, GC Wkshps 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages327-332
Number of pages6
ISBN (Electronic)9781479974702
DOIs
Publication statusPublished - 2014 Mar 18
Event2014 IEEE Globecom Workshops, GC Wkshps 2014 - Austin, United States
Duration: 2014 Dec 82014 Dec 12

Publication series

Name2014 IEEE Globecom Workshops, GC Wkshps 2014

Other

Other2014 IEEE Globecom Workshops, GC Wkshps 2014
CountryUnited States
CityAustin
Period14/12/814/12/12

Fingerprint

Channel state information
Feedback
available information
performance
Cellular radio systems
reconstruction
Discrete cosine transforms
scenario
energy
efficiency
Energy efficiency
communication
Transmitters
Antennas

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Communication

Cite this

Sim, M. S., & Chae, C. B. (2014). Compressed channel feedback for correlateci massive MIMO systems. In 2014 IEEE Globecom Workshops, GC Wkshps 2014 (pp. 327-332). [7063452] (2014 IEEE Globecom Workshops, GC Wkshps 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOCOMW.2014.7063452
Sim, Min Soo ; Chae, Chan Byoung. / Compressed channel feedback for correlateci massive MIMO systems. 2014 IEEE Globecom Workshops, GC Wkshps 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 327-332 (2014 IEEE Globecom Workshops, GC Wkshps 2014).
@inproceedings{84ca621f6d07400bb2dddd83886e7f53,
title = "Compressed channel feedback for correlateci massive MIMO systems",
abstract = "Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. As a sparsifying basis, prior work has applied compressive sensing (CS) techniques with the two-dimensional discrete cosine transform (2D-DCT) and the instantaneous Karhunen-Loeve transform (KLT). 2D-DCT fails, however, to reflect the spatial correlation and channel conditions. Instantaneous KLT requires perfect CSI, which means it is not feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel and a new reconstruction algorithm for CS. We also suggest that dimensionality reduction is more proper to compress, and compare performance with the conventional method. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single user) and point-to-multi-point (multiuser) scenarios.",
author = "Sim, {Min Soo} and Chae, {Chan Byoung}",
year = "2014",
month = "3",
day = "18",
doi = "10.1109/GLOCOMW.2014.7063452",
language = "English",
series = "2014 IEEE Globecom Workshops, GC Wkshps 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "327--332",
booktitle = "2014 IEEE Globecom Workshops, GC Wkshps 2014",
address = "United States",

}

Sim, MS & Chae, CB 2014, Compressed channel feedback for correlateci massive MIMO systems. in 2014 IEEE Globecom Workshops, GC Wkshps 2014., 7063452, 2014 IEEE Globecom Workshops, GC Wkshps 2014, Institute of Electrical and Electronics Engineers Inc., pp. 327-332, 2014 IEEE Globecom Workshops, GC Wkshps 2014, Austin, United States, 14/12/8. https://doi.org/10.1109/GLOCOMW.2014.7063452

Compressed channel feedback for correlateci massive MIMO systems. / Sim, Min Soo; Chae, Chan Byoung.

2014 IEEE Globecom Workshops, GC Wkshps 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 327-332 7063452 (2014 IEEE Globecom Workshops, GC Wkshps 2014).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Compressed channel feedback for correlateci massive MIMO systems

AU - Sim, Min Soo

AU - Chae, Chan Byoung

PY - 2014/3/18

Y1 - 2014/3/18

N2 - Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. As a sparsifying basis, prior work has applied compressive sensing (CS) techniques with the two-dimensional discrete cosine transform (2D-DCT) and the instantaneous Karhunen-Loeve transform (KLT). 2D-DCT fails, however, to reflect the spatial correlation and channel conditions. Instantaneous KLT requires perfect CSI, which means it is not feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel and a new reconstruction algorithm for CS. We also suggest that dimensionality reduction is more proper to compress, and compare performance with the conventional method. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single user) and point-to-multi-point (multiuser) scenarios.

AB - Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. As a sparsifying basis, prior work has applied compressive sensing (CS) techniques with the two-dimensional discrete cosine transform (2D-DCT) and the instantaneous Karhunen-Loeve transform (KLT). 2D-DCT fails, however, to reflect the spatial correlation and channel conditions. Instantaneous KLT requires perfect CSI, which means it is not feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel and a new reconstruction algorithm for CS. We also suggest that dimensionality reduction is more proper to compress, and compare performance with the conventional method. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single user) and point-to-multi-point (multiuser) scenarios.

UR - http://www.scopus.com/inward/record.url?scp=84946686198&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84946686198&partnerID=8YFLogxK

U2 - 10.1109/GLOCOMW.2014.7063452

DO - 10.1109/GLOCOMW.2014.7063452

M3 - Conference contribution

AN - SCOPUS:84946686198

T3 - 2014 IEEE Globecom Workshops, GC Wkshps 2014

SP - 327

EP - 332

BT - 2014 IEEE Globecom Workshops, GC Wkshps 2014

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Sim MS, Chae CB. Compressed channel feedback for correlateci massive MIMO systems. In 2014 IEEE Globecom Workshops, GC Wkshps 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 327-332. 7063452. (2014 IEEE Globecom Workshops, GC Wkshps 2014). https://doi.org/10.1109/GLOCOMW.2014.7063452