ISBN-10:
1848216971
ISBN-13:
9781848216976
Pub. Date:
10/20/2014
Publisher:
Wiley
MMSE-Based Algorithm for Joint Signal Detection, Channel and Noise Variance Estimation for OFDM Systems / Edition 1

MMSE-Based Algorithm for Joint Signal Detection, Channel and Noise Variance Estimation for OFDM Systems / Edition 1

by Vincent Savaux, Yves Louet
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Product Details

ISBN-13: 9781848216976
Publisher: Wiley
Publication date: 10/20/2014
Series: ISTE Series
Pages: 132
Product dimensions: 6.10(w) x 9.30(h) x 0.20(d)

About the Author

Vincent Savaux is a post doctorate at Supélec in Rennes, France. He has worked in the SCEE (Signal, Communications and Embedded Electronics) team since the beginning of 2014.

Yves Louët is Professor at Supélec (SCEE team) in Rennes, France. His research activities concern the physical layer of multicarrier communication systems applied to intelligent and green radio.

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Table of Contents

INTRODUCTION  ix

CHAPTER 1. BACKGROUND AND SYSTEM MODEL 1

1.1. Channel model 1

1.1.1. The multipath channel 1

1.1.2. Statistics of the channe l2

1.2. Transmission of an OFDM signal 7

1.2.1. Continuous representation 7

1.2.2. Discrete representation 9

1.2.3. Discrete representation under synchronization mismatch 12

1.3. Pilot symbol aided channel and noise estimation 12

1.3.1. The pilot arrangements 12

1.3.2. Channel estimation 15

1.3.3. Noise variance estimation 19

1.4. Work motivations 22

CHAPTER 2. JOINT CHANNEL AND NOISE VARIANCE ESTIMATION IN THE PRESENCE OF THE OFDM SIGNAL  25

2.1. Presentation of the algorithm in an ideal approach 25

2.1.1. Channel covariance matrix 25

2.1.2. MMSE noise variance estimation 27

2.1.3. Proposed algorithm: ideal approach 27

2.1.4. Simulation results: ideal approach 41

2.2. Algorithm in a practical approach 48

2.2.1. Proposed algorithm: realistic approach 48

2.2.2. Convergence of the algorithm 51

2.2.3. Simulations results: realistic approach 60

2.3. Summary  65

CHAPTER 3. APPLICATION OF THE ALGORITHM AS A DETECTOR FOR COGNITIVE RADIO SYSTEMS 67

3.1. Spectrum sensing 67

3.1.1. Non-cooperative methods 69

3.1.2. Cooperative methods 71

3.2. Proposed detector 73

3.2.1. Detection hypothesis 73

3.2.2. Convergence of the MMSE-based algorithm under the hypothesis H0 74

3.2.3. Decision rule for the proposed detector 79

3.3. Analytical expressions of the detection and false alarm probabilities 82

3.3.1. Probability density function ofMunder H1 82

3.3.2. Probability density function ofMunder H0 85

3.3.3. Analytical expressions of Pd and Pfa 86

3.4. Simulations results 88

3.4.1. Choice of the threshold ς 88

3.4.2. Effect of the choice of eσ on the detector performance 89

3.4.3. Detector performance under non-WSS channel model and synchronization mismatch 92

3.4.4. Receiver operating characteristic of the detector  94

3.5. Summary  98

CONCLUSION  99

APPENDICES  101

BIBLIOGRAPHY  109

INDEX 119

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