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Joint low‐complexity equalization and carrier frequency offset compensation for underwater acoustic OFDM communication systems with banded‐matrix approximation at different channel conditions
Author(s) -
Ramadan Khaled,
Dessouky Moawad I.,
Elkordy Mohamad,
Elagooz Salah,
Abd ElSamie Fathi E.
Publication year - 2018
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3779
Subject(s) - computer science , carrier frequency offset , underwater acoustic communication , orthogonal frequency division multiplexing , equalization (audio) , adaptive equalizer , bit error rate , minimum mean square error , control theory (sociology) , channel (broadcasting) , algorithm , underwater , frequency offset , telecommunications , mathematics , estimator , oceanography , statistics , control (management) , artificial intelligence , geology
Summary Underwater acoustic wireless communication is considered as one of the most challenging communication technologies due to low propagation speed, water salinity, water depth, PH degree, and water temperature. Equalization is one of the means to improve the system performance. Matched filter (MF), zero forcing (ZF), and minimum mean square error (MMSE) equalizers can be used for channel equalization. Unfortunately, the performance of the matched filter is destroyed in the Multiple‐Input Multiple‐Output (MIMO) configurations. In addition, the ZF equalizer suffers from noise enhancement and high complexity because of the direct matrix inversion. On the other hand, the MMSE equalizer requires estimating the operating signal‐to‐noise ratio (SNR) to work properly. In this paper, we present a joint low‐complexity regularized ZF equalizer and carrier frequency offset (CFO) compensation scheme to deal with these problems. The proposed equalization algorithm mitigates the noise enhancement problem using a constant regularization parameter, and it can be implemented with low complexity using the banded‐matrix approximation. Also, we test the performance of the proposed system at different channel conditions. Simulation results show that the proposed equalizer has the ability to enhance the system performance with lower complexity than those of the other equalization algorithms at different channel conditions.