Two-stage LASSO ADMM signal detection algorithm for large scale MIMO

Anis Elgabli; Ali Elghariani; Abubakr O. Al-Abbasi; Mark Bell

doi:10.1109/ACSSC.2017.8335641

Two-stage LASSO ADMM signal detection algorithm for large scale MIMO

Anis Elgabli
, Ali Elghariani
, Abubakr O. Al-Abbasi
, Mark Bell

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

18 Scopus citations

Abstract

This paper explores the benefit of using some of the machine learning techniques and Big data optimization tools in approximating maximum likelihood (ML) detection of Large Scale MIMO systems. First, large scale MIMO detection problem is formulated as a LASSO (Least Absolute Shrinkage and Selection Operator) optimization problem. Then, Alternating Direction Method of Multipliers (ADMM) is considered in solving this problem. The choice of ADMM is motivated by its ability of solving convex optimization problems by breaking them into smaller sub-problems, each of which are then easier to handle. Further improvement is obtained using two stages of LASSO with interference cancellation from the first stage. The proposed algorithm is investigated at various modulation techniques with different number of antennas. It is also compared with widely used algorithms in this field. Simulation results demonstrate the efficacy of the proposed algorithm for both uncoded and coded cases.

Original language	English
Title of host publication	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Editors	Michael B. Matthews
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1660-1664
Number of pages	5
ISBN (Electronic)	9781538618233
DOIs	https://doi.org/10.1109/ACSSC.2017.8335641
State	Published - 2 Jul 2017
Externally published	Yes

Publication series

Name	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume	2017-October

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

Control and Optimization
Computer Networks and Communications
Hardware and Architecture
Signal Processing
Biomedical Engineering
Instrumentation

Access to Document

10.1109/ACSSC.2017.8335641

Cite this

Elgabli, A., Elghariani, A., Al-Abbasi, A. O., & Bell, M. (2017). Two-stage LASSO ADMM signal detection algorithm for large scale MIMO. In M. B. Matthews (Ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 (pp. 1660-1664). (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACSSC.2017.8335641

Elgabli, Anis ; Elghariani, Ali ; Al-Abbasi, Abubakr O. et al. / Two-stage LASSO ADMM signal detection algorithm for large scale MIMO. Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. editor / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1660-1664 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017).

@inproceedings{41feef2487e54c3099ff83daf3d2c769,

title = "Two-stage LASSO ADMM signal detection algorithm for large scale MIMO",

abstract = "This paper explores the benefit of using some of the machine learning techniques and Big data optimization tools in approximating maximum likelihood (ML) detection of Large Scale MIMO systems. First, large scale MIMO detection problem is formulated as a LASSO (Least Absolute Shrinkage and Selection Operator) optimization problem. Then, Alternating Direction Method of Multipliers (ADMM) is considered in solving this problem. The choice of ADMM is motivated by its ability of solving convex optimization problems by breaking them into smaller sub-problems, each of which are then easier to handle. Further improvement is obtained using two stages of LASSO with interference cancellation from the first stage. The proposed algorithm is investigated at various modulation techniques with different number of antennas. It is also compared with widely used algorithms in this field. Simulation results demonstrate the efficacy of the proposed algorithm for both uncoded and coded cases.",

author = "Anis Elgabli and Ali Elghariani and Al-Abbasi, \{Abubakr O.\} and Mark Bell",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.",

year = "2017",

month = jul,

day = "2",

doi = "10.1109/ACSSC.2017.8335641",

language = "English",

series = "Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017",

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

pages = "1660--1664",

editor = "Matthews, \{Michael B.\}",

booktitle = "Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017",

address = "United States",

}

Elgabli, A, Elghariani, A, Al-Abbasi, AO & Bell, M 2017, Two-stage LASSO ADMM signal detection algorithm for large scale MIMO. in MB Matthews (ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, vol. 2017-October, Institute of Electrical and Electronics Engineers Inc., pp. 1660-1664. https://doi.org/10.1109/ACSSC.2017.8335641

Two-stage LASSO ADMM signal detection algorithm for large scale MIMO. / Elgabli, Anis; Elghariani, Ali; Al-Abbasi, Abubakr O. et al.
Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. ed. / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1660-1664 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October).

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

TY - GEN

T1 - Two-stage LASSO ADMM signal detection algorithm for large scale MIMO

AU - Elgabli, Anis

AU - Elghariani, Ali

AU - Al-Abbasi, Abubakr O.

AU - Bell, Mark

PY - 2017/7/2

Y1 - 2017/7/2

N2 - This paper explores the benefit of using some of the machine learning techniques and Big data optimization tools in approximating maximum likelihood (ML) detection of Large Scale MIMO systems. First, large scale MIMO detection problem is formulated as a LASSO (Least Absolute Shrinkage and Selection Operator) optimization problem. Then, Alternating Direction Method of Multipliers (ADMM) is considered in solving this problem. The choice of ADMM is motivated by its ability of solving convex optimization problems by breaking them into smaller sub-problems, each of which are then easier to handle. Further improvement is obtained using two stages of LASSO with interference cancellation from the first stage. The proposed algorithm is investigated at various modulation techniques with different number of antennas. It is also compared with widely used algorithms in this field. Simulation results demonstrate the efficacy of the proposed algorithm for both uncoded and coded cases.

AB - This paper explores the benefit of using some of the machine learning techniques and Big data optimization tools in approximating maximum likelihood (ML) detection of Large Scale MIMO systems. First, large scale MIMO detection problem is formulated as a LASSO (Least Absolute Shrinkage and Selection Operator) optimization problem. Then, Alternating Direction Method of Multipliers (ADMM) is considered in solving this problem. The choice of ADMM is motivated by its ability of solving convex optimization problems by breaking them into smaller sub-problems, each of which are then easier to handle. Further improvement is obtained using two stages of LASSO with interference cancellation from the first stage. The proposed algorithm is investigated at various modulation techniques with different number of antennas. It is also compared with widely used algorithms in this field. Simulation results demonstrate the efficacy of the proposed algorithm for both uncoded and coded cases.

UR - https://www.scopus.com/pages/publications/85050986914

U2 - 10.1109/ACSSC.2017.8335641

DO - 10.1109/ACSSC.2017.8335641

M3 - Conference contribution

AN - SCOPUS:85050986914

T3 - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017

SP - 1660

EP - 1664

BT - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017

A2 - Matthews, Michael B.

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Elgabli A, Elghariani A, Al-Abbasi AO, Bell M. Two-stage LASSO ADMM signal detection algorithm for large scale MIMO. In Matthews MB, editor, Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1660-1664. (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017). doi: 10.1109/ACSSC.2017.8335641

Two-stage LASSO ADMM signal detection algorithm for large scale MIMO

Abstract

Publication series

Bibliographical note

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this