KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/563425
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AbstractOrthogonal Frequency Division Multiplexing (OFDM) is a modulation scheme that is widely used in wired and wireless communication systems. While OFDM is ideally suited to deal with frequency selective channels and AWGN, its performance may be dramatically impacted by the presence of impulse noise. In fact, very strong noise impulses in the time domain might result in the erasure of whole OFDM blocks of symbols at the receiver. Impulse noise can be mitigated by considering it as a sparse signal in time, and using recently developed algorithms for sparse signal reconstruction. We propose an algorithm that utilizes the guard band subcarriers for the impulse noise estimation and cancellation. Instead of relying on ℓ1 minimization as done in some popular general-purpose compressive sensing schemes, the proposed method jointly exploits the specific structure of this problem and the available a priori information for sparse signal recovery. The computational complexity of the proposed algorithm is very competitive with respect to sparse signal reconstruction schemes based on ℓ1 minimization. The proposed method is compared with respect to other state-of-the-art methods in terms of achievable rates for an OFDM system with impulse noise and AWGN. © 2014 IEEE.
CitationAl-Naffouri, T. Y., Quadeer, A. A., & Caire, G. (2014). Impulse Noise Estimation and Removal for OFDM Systems. IEEE Transactions on Communications, 62(3), 976–989. doi:10.1109/tcomm.2014.012414.130244
SponsorsThe work of T. Y. Al-Naffouri and A. A. Quadeer was supported by SABIC through an internally funded project from DSR, KFUPM (Project No. SB101006). The work of T. Y. Al-Naffouri was also partially supported by the Fulbright Scholar Program. The work of G. Caire was partially supported by NSF Grant CCF 0729162. Part of this work has been presented in the IEEE International Symposium on Information Theory, Russia, 2011 .