Joint data recovery and NBI mitigation in OFDM systems using CS framework

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

doi:10.1109/ICUWB.2016.7790530

Joint data recovery and NBI mitigation in OFDM systems using CS framework

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, a novel approach is proposed to jointly recover the transmitted data and mitigate narrow-band interference (NBI) in OFDM systems using compressive sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. The main idea behind our approach is to represent the transmitted data and the NBI signal as a sparse vector and then solve a joint optimization problem. Therefore, the modulated signal using popular modulation schemes such as BPSK, QPSK, and M-PAM is represented by binary representation using some dictionaries. NBI is a sparse signal in the frequency domain, however, frequency-grid-mismatch destroys the sparsity of NBI at the receiver. We propose a structured-dictionary-mismatch formulation to estimate the frequency-grid-mismatch and recover the sparsity of the NBI in the frequency domain. The optimization problem is formulated as a combined re-weighted ℓ₁ and ℓ_2,1 norms. The solution aims to recover the transmitted data and NBI jointly.

Original language	English
Title of host publication	2016 IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509013173
DOIs	https://doi.org/10.1109/ICUWB.2016.7790530
Publication status	Published - Dec 16 2016
Event	16th IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016 - Nanjing, China Duration: Oct 16 2016 → Oct 19 2016

Other

Other	16th IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016
Country/Territory	China
City	Nanjing
Period	10/16/16 → 10/19/16

Keywords

compressive sensing
NBI
OFDM
re-weighted

ASJC Scopus subject areas

Computer Networks and Communications
Instrumentation

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10.1109/ICUWB.2016.7790530

Cite this

Joint data recovery and NBI mitigation in OFDM systems using CS framework. / Al-Tous, Hanan; Barhumi, Imad ; Kalbat, Abdulrahman et al.
2016 IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7790530.

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

Al-Tous, H, Barhumi, I , Kalbat, A & Al-Dhahir, N 2016, Joint data recovery and NBI mitigation in OFDM systems using CS framework. in 2016 IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016., 7790530, Institute of Electrical and Electronics Engineers Inc., 16th IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016, Nanjing, China, 10/16/16. https://doi.org/10.1109/ICUWB.2016.7790530

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title = "Joint data recovery and NBI mitigation in OFDM systems using CS framework",

abstract = "In this paper, a novel approach is proposed to jointly recover the transmitted data and mitigate narrow-band interference (NBI) in OFDM systems using compressive sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. The main idea behind our approach is to represent the transmitted data and the NBI signal as a sparse vector and then solve a joint optimization problem. Therefore, the modulated signal using popular modulation schemes such as BPSK, QPSK, and M-PAM is represented by binary representation using some dictionaries. NBI is a sparse signal in the frequency domain, however, frequency-grid-mismatch destroys the sparsity of NBI at the receiver. We propose a structured-dictionary-mismatch formulation to estimate the frequency-grid-mismatch and recover the sparsity of the NBI in the frequency domain. The optimization problem is formulated as a combined re-weighted ℓ1 and ℓ2,1 norms. The solution aims to recover the transmitted data and NBI jointly.",

keywords = "compressive sensing, NBI, OFDM, re-weighted",

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

year = "2016",

month = dec,

day = "16",

doi = "10.1109/ICUWB.2016.7790530",

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booktitle = "2016 IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016",

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AU - Al-Tous, Hanan

AU - Barhumi, Imad

AU - Kalbat, Abdulrahman

AU - Al-Dhahir, Naofal

PY - 2016/12/16

Y1 - 2016/12/16

N2 - In this paper, a novel approach is proposed to jointly recover the transmitted data and mitigate narrow-band interference (NBI) in OFDM systems using compressive sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. The main idea behind our approach is to represent the transmitted data and the NBI signal as a sparse vector and then solve a joint optimization problem. Therefore, the modulated signal using popular modulation schemes such as BPSK, QPSK, and M-PAM is represented by binary representation using some dictionaries. NBI is a sparse signal in the frequency domain, however, frequency-grid-mismatch destroys the sparsity of NBI at the receiver. We propose a structured-dictionary-mismatch formulation to estimate the frequency-grid-mismatch and recover the sparsity of the NBI in the frequency domain. The optimization problem is formulated as a combined re-weighted ℓ1 and ℓ2,1 norms. The solution aims to recover the transmitted data and NBI jointly.

AB - In this paper, a novel approach is proposed to jointly recover the transmitted data and mitigate narrow-band interference (NBI) in OFDM systems using compressive sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. The main idea behind our approach is to represent the transmitted data and the NBI signal as a sparse vector and then solve a joint optimization problem. Therefore, the modulated signal using popular modulation schemes such as BPSK, QPSK, and M-PAM is represented by binary representation using some dictionaries. NBI is a sparse signal in the frequency domain, however, frequency-grid-mismatch destroys the sparsity of NBI at the receiver. We propose a structured-dictionary-mismatch formulation to estimate the frequency-grid-mismatch and recover the sparsity of the NBI in the frequency domain. The optimization problem is formulated as a combined re-weighted ℓ1 and ℓ2,1 norms. The solution aims to recover the transmitted data and NBI jointly.

KW - compressive sensing

KW - NBI

KW - OFDM

KW - re-weighted

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BT - 2016 IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016

Y2 - 16 October 2016 through 19 October 2016

ER -

Joint data recovery and NBI mitigation in OFDM systems using CS framework

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