Compressed Sensing in Li-Fi and Wi-Fi Networks

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.

Autorentext

Malek Benslama is currently Professor at the University of Constantine 1 in Algeria. He is also Doctor of Science with the INP Toulouse in France and a member of the scientific council of the Algerian Space Agency.

Klappentext

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.

Inhalt

1. Shannon's Theorem in Classic Data Processing
   
   2. Shannon's Theorem in Quantum Data
   3. Sparse Signals and Compressed Sensing
   4. Compressed Sensing and the Fourier Transform
   5. Compressed Sensing and Entanglement
   6. Compressed Sensing and Intelligent LiFi Systems
   7. Compressed Sensing in LiFi Systems in Mobile Communications and Cryptography
   8. Compressed Sensing in WiFi Systems
   9. Compressed Sensing in Interconnections Covering WiMAX, UMTS and MANET Satellite Networks
   10. Compressed Sensing in Radar Interferometry
   11. Compressed Sensing in Radars
   12. Compressed Sensing in Electromagnetism