CS-PSO algorithm for off-grid narrow-band interference mitigation in OFDM systems

Hanan Al-Tous; Imad Barhumi; Abdulrahman Kalbat; Naofal Al-Dhahir

doi:10.1109/WCNC.2017.7925684

CS-PSO algorithm for off-grid narrow-band interference mitigation in OFDM systems

Hanan Al-Tous, Imad Barhumi, Abdulrahman Kalbat, Naofal Al-Dhahir

Department of Electrical and Communication Engineering

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

1 Citation (Scopus)

Abstract

In this paper, we propose a novel approach to recover off-grid narrow-band-interference (NBI) in orthogonal-frequency-division-multiplexing (OFDM) systems using a compressive-sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. NBI is a sparse signal in the frequency-domain (FD). However, frequency-grid mismatch destroys the sparsity of NBI in the FD. Therefore, the received off-grid NBI is characterized by a nonlinear model which is parametrized by two vectors; the first vector represents the frequency-grid-mismatch and the second vector represents the FD sparse NBI. A CS-based particle-swarm-optimization (CS-PSO) evolutionary algorithm is proposed based on a weighted sum of ℓ₀ and ℓ₂ norms fitness function to jointly recover the support of the FD sparse vector and the frequenc ygrid- mismatch vector. Simulation results demonstrate the merits of the proposed approach.

Original language	English
Title of host publication	2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509041831
DOIs	https://doi.org/10.1109/WCNC.2017.7925684
Publication status	Published - May 10 2017
Event	2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - San Francisco, United States Duration: Mar 19 2017 → Mar 22 2017

Other

Other	2017 IEEE Wireless Communications and Networking Conference, WCNC 2017
Country/Territory	United States
City	San Francisco
Period	3/19/17 → 3/22/17

Keywords

Compressive sensing
OFDM
Off-grid NBI
PSO
ℓ norm

ASJC Scopus subject areas

Engineering(all)

Access to Document

10.1109/WCNC.2017.7925684

Cite this

CS-PSO algorithm for off-grid narrow-band interference mitigation in OFDM systems. / Al-Tous, Hanan; Barhumi, Imad ; Kalbat, Abdulrahman et al.
2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 7925684.

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

Al-Tous, H, Barhumi, I , Kalbat, A & Al-Dhahir, N 2017, CS-PSO algorithm for off-grid narrow-band interference mitigation in OFDM systems. in 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings., 7925684, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017, San Francisco, United States, 3/19/17. https://doi.org/10.1109/WCNC.2017.7925684

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title = "CS-PSO algorithm for off-grid narrow-band interference mitigation in OFDM systems",

abstract = "In this paper, we propose a novel approach to recover off-grid narrow-band-interference (NBI) in orthogonal-frequency-division-multiplexing (OFDM) systems using a compressive-sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. NBI is a sparse signal in the frequency-domain (FD). However, frequency-grid mismatch destroys the sparsity of NBI in the FD. Therefore, the received off-grid NBI is characterized by a nonlinear model which is parametrized by two vectors; the first vector represents the frequency-grid-mismatch and the second vector represents the FD sparse NBI. A CS-based particle-swarm-optimization (CS-PSO) evolutionary algorithm is proposed based on a weighted sum of ℓ0 and ℓ2 norms fitness function to jointly recover the support of the FD sparse vector and the frequenc ygrid- mismatch vector. Simulation results demonstrate the merits of the proposed approach.",

keywords = "Compressive sensing, OFDM, Off-grid NBI, PSO, ℓ norm",

author = "Hanan Al-Tous and Imad Barhumi and Abdulrahman Kalbat and Naofal Al-Dhahir",

year = "2017",

month = may,

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doi = "10.1109/WCNC.2017.7925684",

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AU - Barhumi, Imad

AU - Kalbat, Abdulrahman

AU - Al-Dhahir, Naofal

PY - 2017/5/10

Y1 - 2017/5/10

N2 - In this paper, we propose a novel approach to recover off-grid narrow-band-interference (NBI) in orthogonal-frequency-division-multiplexing (OFDM) systems using a compressive-sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. NBI is a sparse signal in the frequency-domain (FD). However, frequency-grid mismatch destroys the sparsity of NBI in the FD. Therefore, the received off-grid NBI is characterized by a nonlinear model which is parametrized by two vectors; the first vector represents the frequency-grid-mismatch and the second vector represents the FD sparse NBI. A CS-based particle-swarm-optimization (CS-PSO) evolutionary algorithm is proposed based on a weighted sum of ℓ0 and ℓ2 norms fitness function to jointly recover the support of the FD sparse vector and the frequenc ygrid- mismatch vector. Simulation results demonstrate the merits of the proposed approach.

AB - In this paper, we propose a novel approach to recover off-grid narrow-band-interference (NBI) in orthogonal-frequency-division-multiplexing (OFDM) systems using a compressive-sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. NBI is a sparse signal in the frequency-domain (FD). However, frequency-grid mismatch destroys the sparsity of NBI in the FD. Therefore, the received off-grid NBI is characterized by a nonlinear model which is parametrized by two vectors; the first vector represents the frequency-grid-mismatch and the second vector represents the FD sparse NBI. A CS-based particle-swarm-optimization (CS-PSO) evolutionary algorithm is proposed based on a weighted sum of ℓ0 and ℓ2 norms fitness function to jointly recover the support of the FD sparse vector and the frequenc ygrid- mismatch vector. Simulation results demonstrate the merits of the proposed approach.

KW - Compressive sensing

KW - OFDM

KW - Off-grid NBI

KW - PSO

KW - ℓ norm

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BT - 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings

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Y2 - 19 March 2017 through 22 March 2017

ER -

CS-PSO algorithm for off-grid narrow-band interference mitigation in OFDM systems

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