Compressive sensing for wireless networks /

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Han, Zhu, 1974-
Otros Autores: Li, Husheng, 1975-, Yin, Wotao
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge : Cambridge University Press, [2013]
Temas:
Coding theory.
Data compression (Telecommunication)
Signal processing > Digital techniques.
Sampling (Statistics)
Données > Compression (Télécommunications)
Traitement du signal > Techniques numériques.
Échantillonnage (Statistique)
COMPUTERS > Data Transmission Systems > General.
Coding theory
Signal processing > Digital techniques
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 1. Introduction
  • 1.1. Motivation and objectives
  • 1.2. Outline
  • 2. Overview of wireless networks
  • 2.1. Wireless channel models
  • 2.1.1. Radio propagation
  • 2.1.2. Interference channel
  • 2.2. Categorization of wireless networks
  • 2.2.1.3G cellular networks and beyond
  • 2.2.2. WiMAX networks
  • 2.2.3. WiFi networks 19
  • 2.2.4. Wireless personal area networks
  • 2.2.5. Wireless ad hoc networks
  • 2.2.6. Wireless sensor networks
  • 2.3. Advanced wireless technology
  • 2.3.1. OFDM technology
  • 2.3.2. Multiple antenna system
  • 2.3.3. Cognitive radios
  • 2.3.4. Scheduling and multiple access
  • 2.3.5. Wireless positioning and localization
  • pt. I Compressive Sensing Technique
  • 3.Compressive sensing framework
  • 3.1. Background
  • 3.2. Traditional sensing versus compressive sensing
  • 3.3. Sparse representation
  • 3.3.1. Extensions of sparse models
  • 3.4. CS encoding and decoding
  • 3.5. Examples
  • 4. Sparse optimization algorithms.
  • 4.1.A brief introduction to optimization
  • 4.2. Sparse optimization models
  • 4.3. Classic solvers
  • 4.4. Shrinkage operation
  • 4.4.1. Generalizations of shrinkage
  • 4.5. Prox-linear algorithms
  • 4.5.1. Forward-backward operator splitting
  • 4.5.2. Examples
  • 4.5.3. Convergence rates
  • 4.6. Dual algorithms
  • 4.6.1. Dual formulations
  • 4.6.2. The augmented Lagrangian method
  • 4.6.3. Bregman method
  • 4.6.4. Bregman iterations and denoising
  • 4.6.5. Linearized Bregman and augmented models
  • 4.6.6. Handling complex data and variables
  • 4.7. Alternating direction method of multipliers
  • 4.7.1. Framework
  • 4.7.2. Applications of ADM in sparse optimization
  • 4.7.3. Applications in distributed optimization
  • 4.7.4. Applications in decentralized optimization
  • 4.7.5. Convergence rates
  • 4.8.(Block) coordinate minimization and gradient descent
  • 4.9. Homotopy algorithms and parametric quadratic programming
  • 4.10. Continuation, varying step sizes, and line search.
  • 4.11. Non-convex approaches for sparse optimization
  • 4.12. Greedy algorithms
  • 4.12.1. Greedy pursuit algorithms
  • 4.12.2. Iterative support detection
  • 4.12.3. Hard thresholding
  • 4.13. Algorithms for low-rank matrices
  • 4.14. How to choose an algorithm
  • 5. CS analog-to-digital converter
  • 5.1. Traditional ADC basics
  • 5.1.1. Sampling theorem
  • 5.1.2. Quantization
  • 5.1.3. Practical implementation
  • 5.2. Random demodulator ADC
  • 5.2.1. Signal model
  • 5.2.2. Architecture
  • 5.3. Modulated wideband converter ADC
  • 5.3.1. Architecture
  • 5.3.2.Comparison with random demodulator
  • 5.4. Xampling
  • 5.4.1. Union of subspaces
  • 5.4.2. Architecture
  • 5.4.3.X-ADC and hardware implementation
  • 5.4.4.X-DSP and subspace algorithms
  • 5.5. Other architecture
  • 5.5.1. Random sampling
  • 5.5.2. Random filtering
  • 5.5.3. Random delay line
  • 5.5.4. Miscellaneous literature
  • 5.6. Summary
  • pt. II CS-Based Wireless Communication
  • 6.Compressed channel estimation.
  • 6.1. Introduction and motivation
  • 6.2. Multipath channel estimation
  • 6.2.1. Channel model and training-based method
  • 6.2.2.Compressed channel sensing
  • 6.3. OFDM channel estimation
  • 6.3.1. System model
  • 6.3.2.Compressive sensing OFDM channel estimator
  • 6.3.3. Numerical algorithm
  • 6.3.4. Numerical simulations
  • 6.4. Underwater acoustic channel estimation
  • 6.4.1. Channel model
  • 6.4.2.Compressive sensing algorithms
  • 6.5. Random field estimation
  • 6.5.1. Random field model
  • 6.5.2. Matrix completion algorithm
  • 6.5.3. Simulation results
  • 6.6. Other channel estimation methods
  • 6.6.1. Blind channel estimation
  • 6.6.2. Adaptive algorithm
  • 6.6.3. Group sparsity method
  • 6.7. Summary
  • 7. Ultra-wideband systems
  • 7.1.A brief introduction to UWB
  • 7.1.1. History and applications
  • 7.1.2. Characteristics of UWB
  • 7.1.3. Mathematical model of UWB
  • 7.2.Compression of UWB
  • 7.2.1. Transmitter side compression
  • 7.2.2. Receiver side compression.
  • 7.3. Reconstruction of UWB
  • 7.3.1. Block reconstruction
  • 7.3.2. Bayesian reconstruction
  • 7.3.3.Computational issue
  • 7.4. Direct demodulation in UWB communications
  • 7.4.1. Transceiver structures
  • 7.4.2. Demodulation
  • 7.5. Conclusions
  • 8. Positioning
  • 8.1. Introduction to positioning
  • 8.2. Direct application of compressive sensing
  • 8.2.1. General principle
  • 8.2.2. Positioning in WLAN
  • 8.2.3. Positioning in cognitive radio
  • 8.2.4. Dynamic compressive sensing
  • 8.3. Indirect application of compressive sensing
  • 8.3.1. UWB positioning system
  • 8.3.2. Space-time compressive sensing
  • 8.3.3. Joint compressive sensing and TDOA
  • 8.4. Conclusions
  • 9. Multiple access
  • 9.1. Introduction
  • 9.2. Introduction to multiuser detection
  • 9.2.1. System model for CDMA
  • 9.2.2.Comparison between multiuser detection and compressive sensing
  • 9.2.3. Various algorithms of multiuser detection
  • 9.2.4. Optimal multiuser detector.
  • 9.3. Multiple access in cellular systems
  • 9.3.1. Uplink
  • 9.3.2. Downlink
  • 9.4. Multiple access in sensor networks
  • 9.4.1. Single hop
  • 9.4.2. Multiple hops
  • 9.5. Conclusions
  • 10. Cognitive radio networks
  • 10.1. Introduction
  • 10.2. Literature review
  • 10.3.Compressive sensing-based collaborative spectrum sensing
  • 10.3.1. System model
  • 10.3.2. CSS matrix completion algorithm
  • 10.3.3. CSS joint sparsity recovery algorithm
  • 10.3.4. Discussion
  • 10.3.5. Simulations
  • 10.4. Dynamic approach
  • 10.4.1. System model
  • 10.4.2. Dynamic recovery algorithm
  • 10.4.3. Simulations
  • 10.5. Joint consideration with localization
  • 10.5.1. System model
  • 10.5.2. Joint spectrum sensing and localization algorithm
  • 10.5.3. Simulations
  • 10.6. Summary.

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