Efficient Equalization and Carrier Frequency Offset Compensation for Underwater Wireless Communication Systems

Q1 Decision Sciences
Khaled Ramadan, Mohamed S. Elbakry
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引用次数: 1

Abstract

Underwater Acoustic (UWA) wireless communication systems are plagued by a slew of flaws that restrict their performance. This includes factors such as high attenuation in seawater, sediment type, acidity concentration, water temperature, and sound speed propagation. One of the available solutions is Orthogonal Frequency Division Multiplexing (OFDM). Unfortunately, the OFDM systems suffer from the Carrier Frequency Offset (CFO) phenomenon that causes Inter-Carrier-Interference. One of the means to overcome this problem is joint equalization and CFO compensation. In this paper, the conventional OFDM system is adapted for Multiple-Input-Multiple Output (MIMO)-OFDM communication utilizing Discrete Wavelet Transform (DWT) rather than Discrete Fourier Transform (DFT). The DWT-based OFDM system has certain benefits over the comparable DFT. The trade-off, on the other hand, is the necessity for an extra DFT/IDFT to complete the Frequency-Domain equalization procedure, which increases the total computational complexity. In addition, we present a Joint Low Regularized Linear Zero Forcing equalizer for MIMO-OFDM based on DWT that employs the banded-matrix approximation approach. The suggested approach avoids signal-to-noise ratio estimation. Simulation results show that the proposed scheme outperforms different schemes at the same UWA channel conditions spatially in the case of estimation errors.

水下无线通信系统的有效均衡和载波频偏补偿
水下声学(UWA)无线通信系统受到一系列限制其性能的缺陷的困扰。这包括海水中的高衰减、沉积物类型、酸度浓度、水温和声速传播等因素。可用的解决方案之一是正交频分复用(OFDM)。不幸的是,OFDM系统受到载波频率偏移(CFO)现象的影响,该现象导致载波间干扰。克服这一问题的手段之一是联合均衡和首席财务官薪酬。在本文中,传统的OFDM系统适用于利用离散小波变换(DWT)而不是离散傅立叶变换(DFT)的多输入多输出(MIMO)-OFDM通信。与可比较的DFT相比,基于DWT的OFDM系统具有一定的优点。另一方面,代价是需要额外的DFT/IDFT来完成频域均衡过程,这增加了总的计算复杂性。此外,我们还提出了一种用于MIMO-OFDM的基于DWT的联合低正则化线性迫零均衡器,该均衡器采用了带状矩阵近似方法。所提出的方法避免了信噪比估计。仿真结果表明,在估计误差的情况下,该方案在空间上优于相同UWA信道条件下的不同方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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