Estimation de canal multi-trajets dans un contexte de modulation OFDM

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Table des matières

Introduction
r.1 Contexte et modèle
r.1.1 Le canal de transmission multi-trajet
r.1.2 Transmission d’un signal OFDM
r.2 Techniques d’estimation : état de l’art
r.2.1 Les pilotes
r.2.2 Les critères LS et MMSE
r.2.3 Techniques d’interpolation
r.2.4 Autres méthodes d’estimation de canal
r.3 Estimation ACA-LMMSE
r.3.1 Principe du ACA-LMMSE
r.3.2 Complexité de ACA-LMMSE
r.3.3 Choix des paramètres de G
r.3.4 Résultats de simulations
r.3.5 Conclusion et perspectives
r.4 Estimation conjointe du RSB et du canal
r.4.1 Présentation de l’algorithme
r.4.2 Convergence de l’algorithme
r.4.3 Résultats de simulations
r.4.4 Conclusion et perspectives
r.5 Étude des interpolations sur les performances de l’estimation d’un canal de Rayleigh
r.5.1 Modèle
r.5.2 Statistique des erreurs d’interpolation
r.5.3 Considérations géométriques
r.5.4 Résultats de simulations
r.5.5 Conclusion et perspectives
r.6 Application de la diversité de délai cyclique à un SFN
r.6.1 Modèle
r.6.2 Diversité de délai cyclique
r.6.3 Résultat de simulation
Conclusion
Abstract
Introduction
1 System, Models, Basic Elements
1.1 Introduction
1.2 The Transmission Channel
1.2.1 The Multipath Channel
1.2.2 Channel Models
1.2.3 Channel Statistics
1.3 The OFDM Signal and the Transmission Chain
1.3.1 History
1.3.2 Modelisation of the OFDM Signal
1.3.3 Transmission of the OFDM Signal
1.3.4 Discrete Model of the OFDM Transmission
1.3.5 Frequency Covariance and Correlation Matrix
1.4 Simulation of the Transmission Channel
1.5 Conclusion
2 Channel Estimation Methods
2.1 Introduction
2.1.1 Time or Frequency Domain Estimation
2.1.2 Blind Estimation
2.1.3 Transmission Methods with a Known Channel State Information
2.1.4 Semi-blind Estimation
2.2 The Pilots in the OFDM Frame
2.3 LS and MMSE Criteria
2.3.1 Principle of LS Estimation
2.3.2 Principle of Linear-MMSE Estimation
2.4 Pilot-Aided Estimation Methods
2.4.1 Methods with Knowledge of Some Properties of the Channel
2.4.2 Methods without Knowledge of the Channel Properties
2.4.3 Iterative and Recursive Channel Estimation
2.5 Conclusion
3 Artificial Channel Aided-LMMSE Channel Estimation
3.1 Introduction
3.2 Description of the Method
3.2.1 Principle of the Method
3.2.2 ACA-LMMSE channel Estimation
3.2.3 Characteristics of ACA-LMMSE
3.2.4 Complexity Comparison with Standard LMMSE
3.3 Choice of Filter G Parameters
3.3.1 Discussion on the Choice of the Parameters
3.3.2 Discussion on the Choice of the Maximum Delay τ(G) max
3.3.3 Discussion on the Choice of the Number of Paths of the Artificial Channel
3.3.4 Discussion on the Choice of the Multipath Intensity Profile
3.4 Simulations Results
3.4.1 Mean Square Error of ACA-LMMSE
3.4.2 Comparison with other methods
3.4.3 Suitability of ACA-LMMSE in general WSSUS Channel Models
3.4.4 Reduction of Implementation Complexity
3.5 Application to Intersymbol Interference Cancellation
3.5.1 Model of ISI Channel
3.5.2 RISIC Algorithm
3.5.3 ACA-LMMSE with RISIC Algorithm
3.5.4 Simulations Results for RISIC combined with ACA-LMMSE
3.6 Conclusion
4 MMSE-based Joint Iterative SNR and Channel Estimation
4.1 Introduction
4.2 SNR Estimation : State of the Art
4.3 First Approach of the Method in a Simple Context
4.3.1 System Model
4.3.2 Proposed Algorithm – Theoretical Case
4.3.3 Simulations Results – Theoretical Approach
4.4 Realistic Approach of the Joint estimation
4.4.1 Proposed Algorithm – Realistic Case
4.4.2 Convergence of the Algorithm
4.4.3 Simulations Results – Realistic Approach
4.5 Application of the Algorithm to Spectrum Sensing
4.5.1 Spectrum Sensing
4.5.2 Proposed Detector
4.5.3 Analytical Expressions of the Detection and False Alarm Probabilities
4.5.4 Simulations Results
4.6 Conclusion
5 Study of the Interpolation on the Rayleigh Channel Estimation Performance
5.1 Introduction
5.2 System Model
5.3 Statistics of the Interpolation Errors
5.3.1 Nearest Neighbor Interpolation
5.3.2 Linear Interpolation
5.3.3 Statistics of the Interpolated Noise
5.4 Mean Square Error of the Estimations Performed with Interpolation
5.5 Geometrical Considerations
5.5.1 System Model
5.5.2 BPSK Constellation
5.5.3 4-QAM Constellation
5.5.4 Analytical Expression of the BER Floor
5.6 Simulation Results
5.6.1 Simulations Parameters
5.6.2 Analytical BER Floor
5.7 Conclusion
6 Application of Cyclic Delay Diversity to a Single Frequency Network
6.1 Introduction
6.2 Different Kinds of Diversity
6.2.1 Time Diversity
6.2.2 Spatial Diversity
6.2.3 Polarization Diversity
6.2.4 Frequency Diversity
6.3 Application of the Cyclic Delay Diversity in a SFN
6.3.1 Model Description
6.3.2 Simulation Parameters
6.4 Cyclic Delay Diversity
6.4.1 Principle of CDD
6.4.2 Generalization to a Multitransmitter Network
6.5 Simulations Results
6.5.1 Realistic DRM+ Cell
6.5.2 Measurement of the Fading
6.5.3 Bit Error Rate Performance
6.6 Conclusion
General Conclusion
A Appendix of the Chapter 1
A.1 Expression of the Channel Covariance
A.2 Proof of the Diagonalization of a Circulant Matrix in the Fourier Basis
B Appendix of the Chapter 4
B.1 Proof of the Convergence to Zero of the Algorithm when Using the Matrix LSH
B.2 Proof of the Convergence to Zero of the Algorithm under the Hypothesis H0
C Appendix of the Chapter 5
C.1 Error of the Linear Interpolation
List of Figures
List of Tables
List of Algorithms

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