TY - GEN
T1 - Joint data recovery and NBI mitigation in OFDM systems using CS framework
AU - Al-Tous, Hanan
AU - Barhumi, Imad
AU - Kalbat, Abdulrahman
AU - Al-Dhahir, Naofal
N1 - Publisher Copyright:
© 2016 IEEE.
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 - NBI
KW - OFDM
KW - compressive sensing
KW - re-weighted
UR - http://www.scopus.com/inward/record.url?scp=85011068526&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011068526&partnerID=8YFLogxK
U2 - 10.1109/ICUWB.2016.7790530
DO - 10.1109/ICUWB.2016.7790530
M3 - Conference contribution
AN - SCOPUS:85011068526
T3 - 2016 IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016
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 -