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. Introduction - p. xxi 2. 1: Shannon’s Theorem in Classic Data Processing - p. 1 3. 2: Shannon’s Theorem in Quantum Data - p. 9 4. 3: Sparse Signals and Compressed Sensing - p. 17 5. 4: Compressed Sensing and the Fourier Transform - p. 23 6. 5: Compressed Sensing and Entanglement - p. 33 7. 6: [WITHDRAWN] Compressed Sensing and Intelligent LiFi Systems - p. 39 8. 7: Compressed Sensing in LiFi Systems in Mobile Communications and Cryptography - p. 69 9. 8: Compressed Sensing in WiFi Systems - p. 99 10. 9: Compressed Sensing in Interconnections Covering WiMAX, UMTS and MANET Satellite Networks - p. 137 11. 10: Compressed Sensing in Radar Interferometry - p. 163 12. 11: Compressed Sensing in Radars - p. 183 13. 12: Compressed Sensing in Electromagnetism - p. 211 14. Conclusion - p. 229 15. Bibliography - p. 231 16. Index - p. 257
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. Introduction - p. xxi 2. 1: Shannon’s Theorem in Classic Data Processing - p. 1 3. 2: Shannon’s Theorem in Quantum Data - p. 9 4. 3: Sparse Signals and Compressed Sensing - p. 17 5. 4: Compressed Sensing and the Fourier Transform - p. 23 6. 5: Compressed Sensing and Entanglement - p. 33 7. 6: [WITHDRAWN] Compressed Sensing and Intelligent LiFi Systems - p. 39 8. 7: Compressed Sensing in LiFi Systems in Mobile Communications and Cryptography - p. 69 9. 8: Compressed Sensing in WiFi Systems - p. 99 10. 9: Compressed Sensing in Interconnections Covering WiMAX, UMTS and MANET Satellite Networks - p. 137 11. 10: Compressed Sensing in Radar Interferometry - p. 163 12. 11: Compressed Sensing in Radars - p. 183 13. 12: Compressed Sensing in Electromagnetism - p. 211 14. Conclusion - p. 229 15. Bibliography - p. 231 16. Index - p. 257
