Compressed Sensing In Li Fi And Wi Fi Networks
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Compressed Sensing In Li Fi And Wi Fi Networks
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Author : Malek Benslama
language : en
Publisher: Elsevier
Release Date : 2017-11-20
Compressed Sensing In Li Fi And Wi Fi Networks written by Malek Benslama and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-20 with Technology & Engineering categories.
Compressed Sensing in Li-Fi and Wi-Fi Networks features coverage of the first applications in optical telecommunications and wireless. After extensive development of basic theory, many techniques are presented, such as non-asymptotic analysis of random matrices, adaptive detection, greedy algorithms, and the use of graphical models. The book can be used as a comprehensive manual for teaching and research in courses covering advanced signal processing, efficient data processing algorithms, and telecommunications. After a thorough review of the basic theory of compressed sensing, many mathematical techniques are presented, including advanced signal modeling, Nyquist sub-sampling of analog signals, the non-asymptotic analysis of random matrices, adaptive detection, greedy algorithms, and the use of graphical models. Offers extensive development of basic theory behind telecommunications and wireless networks Contains broad coverage of treat compressed sensing, including electromagnetism signals Provides insights into the two key areas of telecommunications, WIFI and LIFI Includes information on advanced signal modeling, Nyquist sub-sampling of analog signals, the non-asymptotic analysis of random matrices, adaptive detection, greedy algorithms, and more
Compressive Sensing For Wireless Networks
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Author : Zhu Han
language : en
Publisher: Cambridge University Press
Release Date : 2013-06-06
Compressive Sensing For Wireless Networks written by Zhu Han and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-06 with Computers categories.
This comprehensive reference delivers the understanding and skills needed to take advantage of compressive sensing in wireless networks.
Compressed Sensing With Applications In Wireless Networks
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Author : Markus Leinonen
language : en
Publisher:
Release Date : 2019-11-29
Compressed Sensing With Applications In Wireless Networks written by Markus Leinonen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-29 with Technology & Engineering categories.
This monograph reviews several recent compressed sensing advancements in wireless networks with an aim to improve the quality of signal reconstruction or detection while reducing the use of energy, radio, and computation resources.
Data Driven Wireless Networks
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Author : Yue Gao
language : en
Publisher: Springer
Release Date : 2018-10-19
Data Driven Wireless Networks written by Yue Gao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-19 with Technology & Engineering categories.
This SpringerBrief discusses the applications of spare representation in wireless communications, with a particular focus on the most recent developed compressive sensing (CS) enabled approaches. With the help of sparsity property, sub-Nyquist sampling can be achieved in wideband cognitive radio networks by adopting compressive sensing, which is illustrated in this brief, and it starts with a comprehensive overview of compressive sensing principles. Subsequently, the authors present a complete framework for data-driven compressive spectrum sensing in cognitive radio networks, which guarantees robustness, low-complexity, and security. Particularly, robust compressive spectrum sensing, low-complexity compressive spectrum sensing, and secure compressive sensing based malicious user detection are proposed to address the various issues in wideband cognitive radio networks. Correspondingly, the real-world signals and data collected by experiments carried out during TV white space pilot trial enables data-driven compressive spectrum sensing. The collected data are analysed and used to verify our designs and provide significant insights on the potential of applying compressive sensing to wideband spectrum sensing. This SpringerBrief provides readers a clear picture on how to exploit the compressive sensing to process wireless signals in wideband cognitive radio networks. Students, professors, researchers, scientists, practitioners, and engineers working in the fields of compressive sensing in wireless communications will find this SpringerBrief very useful as a short reference or study guide book. Industry managers, and government research agency employees also working in the fields of compressive sensing in wireless communications will find this SpringerBrief useful as well.
Enhanced Data Transmission Using Li Fi In Visible Light Communication Vlc Technology
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Author : Dr.M.Vijayalakshmi
language : en
Publisher: Archers & Elevators Publishing House
Release Date :
Enhanced Data Transmission Using Li Fi In Visible Light Communication Vlc Technology written by Dr.M.Vijayalakshmi and has been published by Archers & Elevators Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on with Antiques & Collectibles categories.
Compressive Sensing For Wireless Networks
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Author : Zhu Han
language : en
Publisher: Cambridge University Press
Release Date : 2013-06-06
Compressive Sensing For Wireless Networks written by Zhu Han and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-06 with Technology & Engineering categories.
Compressive sensing is a new signal processing paradigm that aims to encode sparse signals by using far lower sampling rates than those in the traditional Nyquist approach. It helps acquire, store, fuse and process large data sets efficiently and accurately. This method, which links data acquisition, compression, dimensionality reduction and optimization, has attracted significant attention from researchers and engineers in various areas. This comprehensive reference develops a unified view on how to incorporate efficiently the idea of compressive sensing over assorted wireless network scenarios, interweaving concepts from signal processing, optimization, information theory, communications and networking to address the issues in question from an engineering perspective. It enables students, researchers and communications engineers to develop a working knowledge of compressive sensing, including background on the basics of compressive sensing theory, an understanding of its benefits and limitations, and the skills needed to take advantage of compressive sensing in wireless networks.
Robust Network Compressive Sensing
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Author : Guangtao Xue
language : en
Publisher: Springer Nature
Release Date : 2022-10-22
Robust Network Compressive Sensing written by Guangtao Xue and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-22 with Computers categories.
This book investigates compressive sensing techniques to provide a robust and general framework for network data analytics. The goal is to introduce a compressive sensing framework for missing data interpolation, anomaly detection, data segmentation and activity recognition, and to demonstrate its benefits. Chapter 1 introduces compressive sensing, including its definition, limitation, and how it supports different network analysis applications. Chapter 2 demonstrates the feasibility of compressive sensing in network analytics, the authors we apply it to detect anomalies in the customer care call dataset from a Tier 1 ISP in the United States. A regression-based model is applied to find the relationship between calls and events. The authors illustrate that compressive sensing is effective in identifying important factors and can leverage the low-rank structure and temporal stability to improve the detection accuracy. Chapter 3 discusses that there are several challenges in applying compressive sensing to real-world data. Understanding the reasons behind the challenges is important for designing methods and mitigating their impact. The authors analyze a wide range of real-world traces. The analysis demonstrates that there are different factors that contribute to the violation of the low-rank property in real data. In particular, the authors find that (1) noise, errors, and anomalies, and (2) asynchrony in the time and frequency domains lead to network-induced ambiguity and can easily cause low-rank matrices to become higher-ranked. To address the problem of noise, errors and anomalies in Chap. 4, the authors propose a robust compressive sensing technique. It explicitly accounts for anomalies by decomposing real-world data represented in matrix form into a low-rank matrix, a sparse anomaly matrix, an error term and a small noise matrix. Chapter 5 addresses the problem of lack of synchronization, and the authors propose a data-driven synchronization algorithm. It can eliminate misalignment while taking into account the heterogeneity of real-world data in both time and frequency domains. The data-driven synchronization can be applied to any compressive sensing technique and is general to any real-world data. The authors illustrates that the combination of the two techniques can reduce the ranks of real-world data, improve the effectiveness of compressive sensing and have a wide range of applications. The networks are constantly generating a wealth of rich and diverse information. This information creates exciting opportunities for network analysis and provides insight into the complex interactions between network entities. However, network analysis often faces the problems of (1) under-constrained, where there is too little data due to feasibility and cost issues in collecting data, or (2) over-constrained, where there is too much data, so the analysis becomes unscalable. Compressive sensing is an effective technique to solve both problems. It utilizes the underlying data structure for analysis. Specifically, to solve the under-constrained problem, compressive sensing technologies can be applied to reconstruct the missing elements or predict the future data. Also, to solve the over-constraint problem, compressive sensing technologies can be applied to identify significant elements To support compressive sensing in network data analysis, a robust and general framework is needed to support diverse applications. Yet this can be challenging for real-world data where noise, anomalies and lack of synchronization are common. First, the number of unknowns for network analysis can be much larger than the number of measurements. For example, traffic engineering requires knowing the complete traffic matrix between all source and destination pairs, in order to properly configure traffic and avoid congestion. However, measuring the flow between all source and destination pairs is very expensive or even infeasible. Reconstructing data from a small number of measurements is an underconstrained problem. In addition, real-world data is complex and heterogeneous, and often violate the low-level assumptions required by existing compressive sensing techniques. These violations significantly reduce the applicability and effectiveness of existing compressive sensing methods. Third, synchronization of network data reduces the data ranks and increases spatial locality. However, periodic time series exhibit not only misalignment but also different frequencies, which makes it difficult to synchronize data in the time and frequency domains. The primary audience for this book is data engineers, analysts and researchers, who need to deal with big data with missing anomalous and synchronization problems. Advanced level students focused on compressive sensing techniques will also benefit from this book as a reference.
Wireless Algorithms Systems And Applications
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Author : Gopal Pandurangan
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-30
Wireless Algorithms Systems And Applications written by Gopal Pandurangan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-30 with Computers categories.
This book constitutes the refereed proceedings of the 5th Annual International Conference on Wireless Algorithms, Systems, and Applications, WASA 2010, held in Beijing, China, in August 2010. The 19 revised full papers and 10 revised short papers presented together with 18 papers from 4 workshops were carefully reviewed and selected from numerous submissions. The papers are organized in topica sections on topology control and coverage, theoretical foundations, energy-aware algorithms and protocol design, wireless sensor networks and applications, applications and experimentation, scheduling and channel assignment, coding, information theory and security, security of wireless and ad-hoc networks, data management and network control in wireless networks, radar and sonar sensor networks, as well as compressive sensing for communications and networking.
Imaging Sensors And Technologies
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Author : Gonzalo Pajares Martinsanz
language : en
Publisher: MDPI
Release Date : 2018-07-06
Imaging Sensors And Technologies written by Gonzalo Pajares Martinsanz and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-06 with Science (General) categories.
This book is a printed edition of the Special Issue "Imaging: Sensors and Technologies" that was published in Sensors
Foundation Of Cognitive Radio Systems
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Author : Samuel Cheng
language : en
Publisher: BoD – Books on Demand
Release Date : 2012-03-16
Foundation Of Cognitive Radio Systems written by Samuel Cheng and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-16 with Technology & Engineering categories.
The fast user growth in wireless communications has created significant demands for new wireless services in both the licensed and unlicensed frequency spectra. Since many spectra are not fully utilized most of the time, cognitive radio, as a form of spectrum reuse, can be an effective means to significantly boost communications resources. Since its introduction in late last century, cognitive radio has attracted wide attention from academics to industry. Despite the efforts from the research community, there are still many issues of applying it in practice. This books is an attempt to cover some of the open issues across the area and introduce some insight to many of the problems. It contains thirteen chapters written by experts across the globe covering topics including spectrum sensing fundamental, cooperative sensing, spectrum management, and interaction among users.