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.
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
Hatem Mokhtari was formerly Lecturer at the University of Constantine 1 in Algeria. He currently provides consulting services on an international scale for telecommunications and holds a doctorate from Paul Verlaine University – Metz in France.
- 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
Date de parution : 11-2017
Ouvrage de 284 p.
15x22.8 cm