{"title":"Efficient Equalization and Carrier Frequency Offset Compensation for Underwater Wireless Communication Systems","authors":"Khaled Ramadan, Mohamed S. Elbakry","doi":"10.1007/s40745-022-00449-x","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Data Science","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s40745-022-00449-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Decision Sciences","Score":null,"Total":0}
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.
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.