Importance sampling: applications in communications and detection
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2002
|
| Schriftenreihe: | Engineering online library
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references (p. [235]-240) and index |
| Beschreibung: | xiv, 242 p. ill. : 25 cm |
| ISBN: | 3540434208 |
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Datensatz im Suchindex
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| adam_text | RAJAN SRINIVASAN IMPORTANCE SAMPLING APPLICATIONS IN COMMUNICATIONS AND
DETECTION WITH 114FIGURES PF SPRINGER TABLE OF CONTENTS PREFACE VN 1.
ELEMENTS OF IMPORTANCE SAMPLING 1 1.1 RARE EVENTS AND SIMULATION 1 1.2
FAST SIMULATION 2 1.2.1 RANDOM FUNCTIONS 4 1.3 OPTIMAL BIASING 4
EXAMPLE: AN OPTIMAL BIASING DENSITY 6 1.3.1 CONDITIONS ON /* 7 1.4 THE
SIMULATION GAIN 8 2. METHODS OF IMPORTANCE SAMPLING 9 2.1 CONVENTIONAL
BIASING METHODS 10 2.1.1 SCALING 10 EXAMPLE: SCALING THE WEIBULL DENSITY
11 EXAMPLE: ESTIMATING A PROBABILITY CLOSE TO UNITY 13 TRANSFORMATIONS
14 2.1.2 TRANSLATION 16 EXAMPLE: OPTIMAL TRANSLATED DENSITY 17 EXAMPLE:
TRANSLATING THE GAUSSIAN DENSITY 18 EXAMPLE: TRANSLATING THE WEIBULL
DENSITY 19 AN EQUIVALENCE 20 2.1.3 EXPONENTIAL TWISTING 21 EXAR, PLE:
TWISTING THE GAUSSIAN DENSITY 24 EXAMPLE: TWISTING THE GAMMA DENSITY 24
2.2 ADAPTIVE IS - OPTIMIZED BIASING 25 2.2.1 VARIANCE ESTIMATION AND
MINIMIZATION 25 ADAPTIVE OPTIMIZATION 27 EXAMPLE: OPTIMUM TRANSLATION
FOR RAYLEIGH DENSITY... 27 2.2.2 ESTIMATION BY IS 29 EXAMPLE: ESTIMATING
VARIANCE USING IS 33 2.3 COMBINED SCALING AND TRANSLATION 34 EXAMPLE:
TWO-DIMENSIONAL BIASING FOR A WEIBULL DENSITY 35 2.4 OTHER BIASING
METHODS 36 XII TABLE OF CONTENTS 2.4.1 EXTREME ORDER DENSITY FUNCTIONS
38 BIASING WITH / RAL 40 BIASING WITH / N _ N 42 APPENDIX A 44 APPENDIX
B 45 3. SUMS OF RANDOM VARIABLES 47 3.1 TAIL PROBABILITY OF AN I.I.D.
SUM 48 3.2 THE 5-METHOD 49 3.2.1 ADVANTAGE OVER USUAL IS 51 EXAMPLE:
SCALING FOR WEIBULL I.I.D. SUMS 52 EXAMPLE: TRANSLATION FOR I.I.D. SUMS
55 3.3 THE INVERSE IS PROBLEM 55 EXAMPLE: ADAPTIVE DETERMINATION OF T 56
3.3.1 SYSTEM PARAMETER OPTIMIZATION 56 3.4 APPROXIMATIONS FOR TAIL
PROBABILITY 57 3.4.1 THE G-REPRESENTATION 59 3.5 ASYMPTOTIC IS 61 3.5.1
THE VARIANCE RATE 62 MINIMIZING THE RATE 64 3.5.2 ASYMPTOTIC BEHAVIOUR
OF THE G-METHOD 65 EXAMPLE: LAPLACE DENSITY 65 EXAMPLE: LAPLACE DENSITY
66 3.5.3 CONSTANT PROBABILITY ESTIMATION 67 THE WEIGHTING FUNCTION 71
3.6 DENSITY ESTIMATION FOR SUMS 71 EXAMPLE: DENSITY ESTIMATION BY
TRANSLATION 73 3.6.1 AN APPROXIMATION: THE SRINIVASAN DENSITY 74
CONVERGENCE OF THE SRINIVASAN DENSITY 76 DENSITY AND DISTRIBUTION PAIRS
77 EXAMPLE: DENSITY APPROXIMATION OF RAYLEIGH SUMS .... 77 3.6.2
EXPONENTIAL TWISTING 79 APPENDIX C 83 4. DETECTION THEORY 85 4.1 THE
NEYMAN-PEARSON LEMMA 85 4.1.1 PROPERTIES OF LIKELIHOOD RATIO TESTS 86
4.2 APPROXIMATIONS FOR THE ERROR PROBABILITIES 88 4.2.1 FALSE ALARM
PROBABILITY 90 4.2.2 MISS PROBABILITY 90 4.3 ASYMPTOTICALLY CONSTANT
ERROR PROBABILITIES 91 4.3.1 DETECTION PROBABILITY 92 4.3.2 FALSE ALARM
PROBABILITY 93 4.4 DENSITIES FOR THE LOG-LIKELIHOOD RATIO 93 APPENDIX D
94 TABLE OF CONTENTS XIII 5. CFAR DETECTION 97 5.1 CONSTANT FALSE ALARM
RATE DETECTION 97 5.2 IS FOR CFAR ALGORITHMS 99 5.2.1 INPUT BIASING 100
5.3 MULTIPLIER DETERMINATION-ADAPTIVE OPTIMIZATION 101 5.4 EXPONENTIAL
TWISTING FOR CA-CFAR 101 5.4.1 CONVENTIONAL IS ESTIMATOR 102 5.4.2
G-METHOD ESTIMATOR 103 A CLASSIC EXAMPLE 104 5.5 APPROXIMATIONS FOR
CA-CFAR 105 5.5.1 USING DENSITY APPROXIMATIONS 105 THE ASYMPTOTIC FORM
106 5.5.2 USING EXPONENTIAL TWISTING 106 5.6 THE GM-CFAR DETECTOR 107
5.6.1 APPROXIMATIONS FOR FAP 108 5.7 POINT OF APPLICATION OF BIASING 111
EXAMPLE: SO-CA-CFAR DETECTOR 111 5.8 FAP DECOMPOSITION FOR SO DETECTORS:
CA AND GM 113 5.8.1 FAST ESTIMATION 114 5.8.2 VARIANCE AND IS GAIN 115
5.8.3 SO- AND GO-GM DETECTORS 117 APPROXIMATIONS FOR FAP 119 5.9
EXAMPLES IN CFAR DETECTION 121 EXAMPLE: CA-CFAR DETECTOR IN RAYLEIGH
CLUTTER 121 EXAMPLE: CENSORED OS CA-CFAR IN WEIBULL CLUTTER .. 126
EXAMPLE: ML-CFAR DETECTOR IN WEIBULL CLUTTER 130 5.10 STAP DETECTION 132
6. ENSEMBLE CFAR DETECTION 137 6.1 ENSEMBLE PROCESSING 137 6.2 THE
E-CFAR DETECTOR 139 6.2.1 NORMALIZATION 140 6.2.2 FAP ESTIMATION AND
BIAS OPTIMIZATION 143 6.2.3 DETERMINING ENSEMBLE THRESHOLDS 144 6.3
PERFORMANCE IN NONHOMOGENEOUS CLUTTER 145 6.4 RESULTS FOR SOME ENSEMBLES
146 GEOMETRIE MEAN DETECTORS 147 6.4.1 COMMENTS 153 6.5 RANDOMIZED
ENSEMBLES 153 6.5.1 FAP DECOMPOSITIONS 154 6.5.2 CHOICE OF FUNETIONS:
FURTHER DECOMPOSITIONS 155 RE1 156 RES 2, 3, 5 158 RE4 158 RE6 160 XIV
TABLE OF CONTENTS 6.6 TUNING THE MULTIPLIERS: HOMOGENEOUS OPERATING
POINTS 161 7. BLIND SIMULATION 167 7.1 BLIND BIASING 167 7.1.1 THE
WEIGHTING FUNCTION 168 7.2 TAIL PROBABILITY ESTIMATION 169 7.2.1 THE
/I-FUNCTION 170 7.2.2 THE ASYMPTOTIC RATE 171 7.2.3 BLIND SIMULATION
GAIN 172 PARTIALLY BLIND CASE 172 COMPLETELY BLIND CASE 174 7.3 CFAR
DETECTION 174 7.3.1 ESTIMATOR IMPLEMENTATION 176 OPTIMUM TWIST 176 7.3.2
THE BLIND SIMULATION GAIN 176 7.3.3 AN APPLICATION 178 THRESHOLD
ADAPTATION AND A T CONTROL 178 7.3.4 COMMENTS 180 8. DIGITAL
COMMUNICATIONS 185 8.1 ADAPTIVE SIMULATION 185 8.2 DPSK IN AWGN 187 8.3
PARAMETER OPTIMIZATION 190 8.3.1 NONCOHERENT OOK IN AWGN: THRESHOLD
OPTIMIZATION . 192 8.4 SUM DENSITY OF RANDOMLY PHASED SINUSOIDS 194 8.5
M-ARY PSK IN CO-CHANNEL INTERFERENCE 196 8.5.1 INTERFERENCE DOMINATED
ENVIRONMENT 198 COHERENT BPSK 199 M-ARY PSK 201 8.5.2 INTERFERENCE AND
AWGN 204 COHERENT BPSK 204 GAUSSIAN APPROXIMATION 208 M-ARY PSK 209 8.6
CROSSTALK IN WDM NETWORKS 211 8.6.1 GAUSSIAN APPROXIMATION 215 8.6.2
CHERNOFF BOUND 216 8.6.3 IS ESTIMATION 217 THRESHOLD OPTIMIZATION 219
8.7 MULTIUSER DETECTION 223 8.8 CAPACITY OF MULTI-ANTENNA SYSTEMS 230
REFERENCES 235 INDEX 241
|
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| indexdate | 2024-07-09T19:02:54Z |
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| isbn | 3540434208 |
| language | English |
| lccn | 2002021691 |
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| physical | xiv, 242 p. ill. : 25 cm |
| publishDate | 2002 |
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| publisher | Springer |
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| spelling | Srinivasan, Rajan Verfasser aut Importance sampling applications in communications and detection Rajan Srinivasan Berlin [u.a.] Springer 2002 xiv, 242 p. ill. : 25 cm txt rdacontent n rdamedia nc rdacarrier Engineering online library Includes bibliographical references (p. [235]-240) and index Monte-Carlo, Méthode de Steekproeven gtt Waarschijnlijkheid (statistiek) gtt Échantillonnage (Statistique) Monte Carlo method Sampling (Statistics) Stichprobennahme (DE-588)4183250-4 gnd rswk-swf Seltenes Ereignis (DE-588)4425827-6 gnd rswk-swf Stichprobennahme (DE-588)4183250-4 s Seltenes Ereignis (DE-588)4425827-6 s DE-604 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009875108&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Srinivasan, Rajan Importance sampling applications in communications and detection Monte-Carlo, Méthode de Steekproeven gtt Waarschijnlijkheid (statistiek) gtt Échantillonnage (Statistique) Monte Carlo method Sampling (Statistics) Stichprobennahme (DE-588)4183250-4 gnd Seltenes Ereignis (DE-588)4425827-6 gnd |
| subject_GND | (DE-588)4183250-4 (DE-588)4425827-6 |
| title | Importance sampling applications in communications and detection |
| title_auth | Importance sampling applications in communications and detection |
| title_exact_search | Importance sampling applications in communications and detection |
| title_full | Importance sampling applications in communications and detection Rajan Srinivasan |
| title_fullStr | Importance sampling applications in communications and detection Rajan Srinivasan |
| title_full_unstemmed | Importance sampling applications in communications and detection Rajan Srinivasan |
| title_short | Importance sampling |
| title_sort | importance sampling applications in communications and detection |
| title_sub | applications in communications and detection |
| topic | Monte-Carlo, Méthode de Steekproeven gtt Waarschijnlijkheid (statistiek) gtt Échantillonnage (Statistique) Monte Carlo method Sampling (Statistics) Stichprobennahme (DE-588)4183250-4 gnd Seltenes Ereignis (DE-588)4425827-6 gnd |
| topic_facet | Monte-Carlo, Méthode de Steekproeven Waarschijnlijkheid (statistiek) Échantillonnage (Statistique) Monte Carlo method Sampling (Statistics) Stichprobennahme Seltenes Ereignis |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009875108&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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