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Compressive Sensing for Wireless Networks [Hardback]

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(University of Tennessee, Knoxville), (Rice University, Houston), (University of Houston)
  • Formāts: Hardback, 304 pages, height x width x depth: 253x180x20 mm, weight: 790 g, Worked examples or Exercises; 7 Tables, black and white; 100 Line drawings, unspecified
  • Izdošanas datums: 06-Jun-2013
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 1107018838
  • ISBN-13: 9781107018839
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  • Cena: 115,83 €
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Compressive Sensing for Wireless NetworksPalielināt
  • Formāts: Hardback, 304 pages, height x width x depth: 253x180x20 mm, weight: 790 g, Worked examples or Exercises; 7 Tables, black and white; 100 Line drawings, unspecified
  • Izdošanas datums: 06-Jun-2013
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 1107018838
  • ISBN-13: 9781107018839
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781107018839.html
Keywords:
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.

This comprehensive reference presents a unified view on incorporating compressive sensing over assorted wireless network scenarios. It enables students, researchers and communications engineers to develop an understanding of compressive sensing, including its benefits and limitations, as well as the skills needed to take advantage of compressive sensing in wireless networks.

Papildus informācija

This comprehensive reference delivers the understanding and skills needed to take advantage of compressive sensing in wireless networks.
Preface xiii
1 Introduction
1(5)
1.1 Motivation and objectives
1(1)
1.2 Outline
2(4)
2 Overview of wireless networks
6(45)
2.1 Wireless channel models
6(7)
2.1.1 Radio propagation
6(5)
2.1.2 Interference channel
11(2)
2.2 Categorization of wireless networks
13(23)
2.2.1 3G cellular networks and beyond
13(4)
2.2.2 WiMAX networks
17(5)
2.2.3 WiFi networks 19
2.2.4 Wireless personal area networks
22(6)
2.2.5 Wireless ad hoc networks
28(4)
2.2.6 Wireless sensor networks
32(4)
2.3 Advanced wireless technology
36(15)
2.3.1 OFDM technology
36(3)
2.3.2 Multiple antenna system
39(2)
2.3.3 Cognitive radios
41(2)
2.3.4 Scheduling and multiple access
43(2)
2.3.5 Wireless positioning and localization
45(6)
Part I Compressive Sensing Technique
3 Compressive sensing framework
51(18)
3.1 Background
51(5)
3.2 Traditional sensing versus compressive sensing
56(1)
3.3 Sparse representation
57(3)
3.3.1 Extensions of sparse models
59(1)
3.4 CS encoding and decoding
60(7)
3.5 Examples
67(2)
4 Sparse optimization algorithms
69(49)
4.1 A brief introduction to optimization
70(3)
4.2 Sparse optimization models
73(1)
4.3 Classic solvers
74(2)
4.4 Shrinkage operation
76(3)
4.4.1 Generalizations of shrinkage
78(1)
4.5 Prox-linear algorithms
79(4)
4.5.1 Forward-backward operator splitting
80(1)
4.5.2 Examples
81(2)
4.5.3 Convergence rates
83(1)
4.6 Dual algorithms
83(10)
4.6.1 Dual formulations
84(1)
4.6.2 The augmented Lagrangian method
85(1)
4.6.3 Bregman method
86(2)
4.6.4 Bregman iterations and denoising
88(2)
4.6.5 Linearized Bregman and augmented models
90(2)
4.6.6 Handling complex data and variables
92(1)
4.7 Alternating direction method of multipliers
93(10)
4.7.1 Framework
94(2)
4.7.2 Applications of ADM in sparse optimization
96(4)
4.7.3 Applications in distributed optimization
100(2)
4.7.4 Applications in decentralized optimization
102(1)
4.7.5 Convergence rates
102(1)
4.8 (Block) coordinate minimization and gradient descent
103(2)
4.9 Homotopy algorithms and parametric quadratic programming
105(2)
4.10 Continuation, varying step sizes, and line search
107(2)
4.11 Non-convex approaches for sparse optimization
109(1)
4.12 Greedy algorithms
110(4)
4.12.1 Greedy pursuit algorithms
110(2)
4.12.2 Iterative support detection
112(1)
4.12.3 Hard thresholding
113(1)
4.13 Algorithms for low-rank matrices
114(1)
4.14 How to choose an algorithm
115(3)
5 CS analog-to-digital converter
118(23)
5.1 Traditional ADC basics
118(7)
5.1.1 Sampling theorem
118(2)
5.1.2 Quantization
120(1)
5.1.3 Practical implementation
121(4)
5.2 Random demodulator ADC
125(2)
5.2.1 Signal model
125(1)
5.2.2 Architecture
125(2)
5.3 Modulated wideband converter ADC
127(2)
5.3.1 Architecture
127(2)
5.3.2 Comparison with random demodulator
129(1)
5.4 Xampling
129(6)
5.4.1 Union of subspaces
130(1)
5.4.2 Architecture
130(1)
5.4.3 X-ADC and hardware implementation
131(1)
5.4.4 X-DSP and subspace algorithms
132(3)
5.5 Other architecture
135(3)
5.5.1 Random sampling
135(1)
5.5.2 Random filtering
136(1)
5.5.3 Random delay line
136(1)
5.5.4 Miscellaneous literature
136(2)
5.6 Summary
138(3)
Part II CS-Based Wireless Communication
6 Compressed channel estimation
141(32)
6.1 Introduction and motivation
141(2)
6.2 Multipath channel estimation
143(3)
6.2.1 Channel model and training-based method
143(1)
6.2.2 Compressed channel sensing
143(3)
6.3 OFDM channel estimation
146(13)
6.3.1 System model
147(1)
6.3.2 Compressive sensing OFDM channel estimator
148(3)
6.3.3 Numerical algorithm
151(3)
6.3.4 Numerical simulations
154(5)
6.4 Underwater acoustic channel estimation
159(3)
6.4.1 Channel model
159(1)
6.4.2 Compressive sensing algorithms
160(2)
6.5 Random field estimation
162(9)
6.5.1 Random field model
163(3)
6.5.2 Matrix completion algorithm
166(2)
6.5.3 Simulation results
168(3)
6.6 Other channel estimation methods
171(1)
6.6.1 Blind channel estimation
171(1)
6.6.2 Adaptive algorithm
171(1)
6.6.3 Group sparsity method
172(1)
6.7 Summary
172(1)
7 Ultra-wideband systems
173(20)
7.1 A brief introduction to UWB
173(2)
7.1.1 History and applications
173(1)
7.1.2 Characteristics of UWB
174(1)
7.1.3 Mathematical model of UWB
174(1)
7.2 Compression of UWB
175(5)
7.2.1 Transmitter side compression
175(2)
7.2.2 Receiver side compression
177(3)
7.3 Reconstruction of UWB
180(9)
7.3.1 Block reconstruction
180(4)
7.3.2 Bayesian reconstruction
184(2)
7.3.3 Computational issue
186(3)
7.4 Direct demodulation in UWB communications
189(3)
7.4.1 Transceiver structures
189(1)
7.4.2 Demodulation
190(2)
7.5 Conclusions
192(1)
8 Positioning
193(21)
8.1 Introduction to positioning
193(1)
8.2 Direct application of compressive sensing
194(11)
8.2.1 General principle
194(1)
8.2.2 Positioning in WLAN
195(3)
8.2.3 Positioning in cognitive radio
198(5)
8.2.4 Dynamic compressive sensing
203(2)
8.3 Indirect application of compressive sensing
205(7)
8.3.1 UWB positioning system
205(2)
8.3.2 Space-time compressive sensing
207(3)
8.3.3 Joint compressive sensing and TDOA
210(2)
8.4 Conclusions
212(2)
9 Multiple access
214(18)
9.1 Introduction
214(1)
9.2 Introduction to multiuser detection
215(6)
9.2.1 System model for CDMA
216(1)
9.2.2 Comparison between multiuser detection and compressive sensing
216(1)
9.2.3 Various algorithms of multiuser detection
217(1)
9.2.4 Optimal multiuser detector
217(4)
9.3 Multiple access in cellular systems
221(6)
9.3.1 Uplink
221(5)
9.3.2 Downlink
226(1)
9.4 Multiple access in sensor networks
227(4)
9.4.1 Single hop
227(2)
9.4.2 Multiple hops
229(2)
9.5 Conclusions
231(1)
10 Cognitive radio networks
232(36)
10.1 Introduction
232(2)
10.2 Literature review
234(2)
10.3 Compressive sensing-based collaborative spectrum sensing
236(15)
10.3.1 System model
236(1)
10.3.2 CSS matrix completion algorithm
237(3)
10.3.3 CSS joint sparsity recovery algorithm
240(3)
10.3.4 Discussion
243(1)
10.3.5 Simulations
244(7)
10.4 Dynamic approach
251(8)
10.4.1 System model
252(1)
10.4.2 Dynamic recovery algorithm
253(2)
10.4.3 Simulations
255(4)
10.5 Joint consideration with localization
259(8)
10.5.1 System model
259(2)
10.5.2 Joint spectrum sensing and localization algorithm
261(3)
10.5.3 Simulations
264(3)
10.6 Summary
267(1)
References 268(23)
Index 291
Zhu Han is an Associate Professor in the Electrical and Computer Engineering Department at the University of Houston, Texas. He received an NSF CAREER award in 2010 and the IEEE Fred W. Ellersick Prize in 2011. He co-authored papers that won the best paper award at the IEEE International Conference on Communications 2009, the 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt09), and the IEEE Wireless Communication and Networking Conference, 2012. Husheng Li is an Assistant Professor in the Electrical and Computer Engineering Department at the University of Tennessee. He received the Best Paper Award of the EURASIP Journal on Wireless Communications and Networking in 2005 (together with his PhD advisor, Professor H. V. Poor), the Best Demo Award of IEEE Globecom in 2010, and the Best Paper Award at IEEE ICC in 2011. Wotao Yin is an Associate Professor at the Department of Computational and Applied Mathematics at Rice University. He won an NSF CAREER award in 2008 and an Alfred P. Sloan Research Fellowship in 2009.