Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks: = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen
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| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
Erlangen
FAU University Press
2023
|
| Series: | FAU Studien aus der Elektrotechnik
Band 21 |
| Subjects: | |
| Online Access: | Volltext Volltext Volltext Volltext Inhaltsverzeichnis |
| Physical Description: | xx, 132 Seiten Illustrationen, Diagramme 24 cm x 17 cm, 390 g |
| ISBN: | 9783961476572 3961476578 |
| DOI: | 10.25593/978-3-96147-658-9 |
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MARC
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| 020 | |a 3961476578 |9 3-96147-657-8 | ||
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| 100 | 1 | |a Jüschke, Patrick |0 (DE-588)1297534352 |4 aut | |
| 245 | 1 | 0 | |a Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks |b = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen |c Patrick Jüschke |
| 246 | 1 | 1 | |a Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen |
| 264 | 1 | |a Erlangen |b FAU University Press |c 2023 | |
| 300 | |a xx, 132 Seiten |b Illustrationen, Diagramme |c 24 cm x 17 cm, 390 g | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a FAU Studien aus der Elektrotechnik |v Band 21 | |
| 502 | |b Dissertation |c Friedrich-Alexander-Universität Erlangen-Nürnberg |d 2022 | ||
| 650 | 0 | 7 | |a Digitale Vorverzerrung |0 (DE-588)1275789080 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Transceiver |0 (DE-588)4507910-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Kompensation |0 (DE-588)4031980-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Hochfrequenztechnik |0 (DE-588)4025202-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Radiofrequenzbereich |0 (DE-588)4176824-3 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Leistungsverstärker |0 (DE-588)4129735-0 |2 gnd |9 rswk-swf |
| 653 | |a Leistungsverstärker | ||
| 653 | |a Hochfrequenztechnik | ||
| 653 | |a Maschinelles Lernen | ||
| 653 | |a Künstliche Intelligenz | ||
| 653 | |a Digitale Vorverzerrung | ||
| 653 | |a RF Power Amplifiers | ||
| 653 | |a Digital Predistortion | ||
| 653 | |a Artificial Neural Networks | ||
| 653 | |a FPGA Neurocomputing | ||
| 653 | |a PA Modelling | ||
| 653 | |a Memory Effects | ||
| 653 | |a Machine Learning | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Leistungsverstärker |0 (DE-588)4129735-0 |D s |
| 689 | 0 | 1 | |a Hochfrequenztechnik |0 (DE-588)4025202-4 |D s |
| 689 | 0 | 2 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 3 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |D s |
| 689 | 0 | 4 | |a Digitale Vorverzerrung |0 (DE-588)1275789080 |D s |
| 689 | 0 | 5 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Transceiver |0 (DE-588)4507910-9 |D s |
| 689 | 1 | 1 | |a Leistungsverstärker |0 (DE-588)4129735-0 |D s |
| 689 | 1 | 2 | |a Radiofrequenzbereich |0 (DE-588)4176824-3 |D s |
| 689 | 1 | 3 | |a Digitale Vorverzerrung |0 (DE-588)1275789080 |D s |
| 689 | 1 | 4 | |a Kompensation |0 (DE-588)4031980-5 |D s |
| 689 | 1 | 5 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 1 | 6 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
| 689 | 1 | |5 DE-604 | |
| 710 | 2 | |a FAU University Press ein Imprint der Universität Erlangen-Nürnberg |b Universitätsbibliothek |0 (DE-588)1068111240 |4 pbl | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |o 10.25593/978-3-96147-658-9 |o urn:nbn:de:bvb:29-opus4-235898 |z 978-3-96147-658-9 |
| 830 | 0 | |a FAU Studien aus der Elektrotechnik |v Band 21 |w (DE-604)BV042335665 |9 21 | |
| 856 | 4 | 1 | |u https://open.fau.de/handle/openfau/23589 |x Verlag |z kostenfrei |3 Volltext |
| 856 | 4 | 1 | |u https://doi.org/10.25593/978-3-96147-658-9 |x Resolving-System |z kostenfrei |3 Volltext |
| 856 | 4 | 1 | |u https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-235898 |x Resolving-System |z kostenfrei |3 Volltext |
| 856 | 4 | 1 | |u https://d-nb.info/129602931X/34 |x Langzeitarchivierung Nationalbibliothek |z kostenfrei |3 Volltext |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034332398&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-034332398 | ||
| 883 | 1 | |8 1\p |a vlb |d 20230712 |q DE-101 |u https://d-nb.info/provenance/plan#vlb | |
Record in the Search Index
| _version_ | 1804185384349859840 |
|---|---|
| adam_text | CONTENTS
AUTHOR
S
STATEMENT
............................................................................................
III
LIST
OF
SYMBOLS
AND
ABBREVIATIONS
..............................................................
XV
I
INTRODUCTION
....................................................................................
I
2
TOOLS
FOR
PERFORMANCE
ASSESSMENT
..............................................
5
2.1
SIMULATION
ENVIRONMENT
......................................................................
5
2.1.1
CIRCUIT
ENVELOPE
SIMULATION
.................................................
6
2.2
HARDWARE
TEST
PLATFORM
......................................................................
8
2.2.1
CLASS-AB
POWER
AMPLIFIER
.....................................................
10
2.2.2
CLASS-ABJ
POWER
AMPLIFIER
.................................................
10
3
POWER
AMPLIFIER
MODELING
.............................................................
13
3.1
STATIC
NONLINEARITIES
............................................................................
14
3.1.1
POLYNOMIAL
MODEL
...................................................................
16
3.1.2
SALEH
S
MODEL
.........................................................................
16
3.2
MEMORY
EFFECTS
...................................................................................
17
3.2.1
SHORT
TERM
MEMORY
EFFECTS
..................................................
17
3.2.2
LONG
TERM
MEMORY
EFFECTS
..................................................
21
3.3
THERMAL
MEMORY
................................................................................
22
3.3.1
PERFORMANCE
OF
TEMPERATURE
SENSORS
.................................
23
3.3.2
THERMAL
TIME
CONSTANT
EVALUATION
....................................
24
3.3.3
CHARACTERIZATION
OF
THERMAL
MEMORY
.................................
24
3.3.4
MODELING
THERMAL
MEMORY
ON
MEASUREMENT
DATA
....
25
3.4
CHARGE
TRAPPING
IN
GAN
DEVICES
.........................................................
28
3.4.1
MEASURING
INTRINSIC
VOLTAGES
...............................................
29
3.4.2
PEAK
VOLTAGE
DETECTOR
............................................................
29
4
PREDISTORTION
TECHNIQUES
...............................................................
33
4.1
FEEDBACK
LINEARIZATION
......................................................................
34
4.2
FEEDFORWARD
LINEARIZATION
...................................................................
34
4.3
PREDISTORTION
..........................................................................................
35
4.3.1
DIRECT
AND
INDIRECT
LEARNING
...............................................
38
4.4
DIGITAL
PREDISTORTION
(DPD)
................................................................
38
4.4.1
MEMORYLESS
DPD
...................................................................
39
4.4.2
MITIGATION
OF
MEMORY
EFFECTS
..............................................
39
XI
5
ARTIFICIAL
NEURAL
NETWORKS
FOR
PREDISTORTION
OF
RF
POWER
AMPLI
FIERS
......................................................................................................
41
5.1
NEURON
MODEL
......................................................................................
41
5.1.1
INITIALIZATION
OF
NEURON
WEIGHTS
........................................
42
5.1.2
TRANSFER
FUNCTIONS
...............................................................
43
5.2
NETWORK
ARCHITECTURES
..........................................................................
43
5.2.1
TIME
DELAY
LINES
FOR
MEMORY
EFFECT
MITIGATION
.................
46
5.3
TRAINING
.................................................................................................
47
5.3.1
LEARNING
RULES
......................................................................
48
5.3.2
ADVANCED
TRAINING
ALGORITHMS
...........................................
49
5.3.3
ASSESSMENT
ON
TRAINING
PERFORMANCE
.................................
50
5.4
IQ
IMBALANCE
MITIGATION
......................................................................
52
5.4.1
NEURAL
NETWORK
DESIGN
.........................................................
52
5.4.2
SIMULATION
RESULTS
...............................................................
52
5.4.3
MEASUREMENT
RESULTS
............................................................
53
5.5
POWER
AMPLIFIER
LINEARIZATION
.............................................................
56
5.5.1
NEURAL
NETWORK
DESIGN
.........................................................
56
5.5.2
LINEARIZATION
OF
SALEH
MODEL
...............................................
57
5.5.3
LINEARIZATION
OF
POLYNOMIAL
MODEL
.....................................
57
5.5.4
LINEARIZATION
OF
MEMORY
POLYNOMIAL
MODEL
....................
62
5.5.5
MEASUREMENT
RESULTS
............................................................
63
5.6
IQ
IMBALANCE
MITIGATION
AND
PA
LINEARIZATION
...............................
66
5.6.1
NEURAL
NETWORK
DESIGN
.........................................................
66
5.6.2
SIMULATION
RESULTS
...............................................................
67
5.6.3
MEASUREMENT
RESULTS
............................................................
69
5.7
LINEARIZATION
OF
THERMAL
MEMORY
......................................................
71
5.7.1
NEURAL
NETWORK
DESIGN
.........................................................
71
5.7.2
RESULTS
ON
LINEARIZATION
OF
THERMAL
MEMORY
.....................
71
5.7.3
LINEARIZATION
PERFORMANCE
FOR
TDD
.....................................
76
5.8
LINEARIZATION
OF
THERMAL
MEMORY
AND
IQ
IMBALANCE
.....................
78
5.8.1
NEURAL
NETWORK
DESIGN
.........................................................
78
5.8.2
RESULTS
ON
THERMAL
MEMORY
AND
IQ
IMBALANCE
.................
79
5.9
LINEARIZATION
OF
CHARGE
TRAPPING
MEMORY
.........................................
81
5.9.1
NEURAL
NETWORK
DESIGN
.........................................................
81
5.9.2
RESULTS
ON
LINEARIZATION
OF
CHARGE
TRAPPING
.....................
82
5.10
COMPARISON
OF
RESULTS
..........................................................................
86
5.10.1
SIMULATION
RESULTS
...............................................................
86
5.10.2
MEASUREMENT
RESULTS
............................................................
87
5.10.3
COMPARISON
WITH
VOLTERRA
DPD
............................................
89
5.10.4
COMPARISON
WITH
MEMORY
POLYNOMIAL
DPD
........................
89
XII
6
FPGA
NEUROCOMPUTING
..................................................................
91
6.1
SYSTEM
DESIGN
......................................................................................
91
6.2
IMPLEMENTATION
OF
TRANSFER
FUNCTIONS
...............................................
92
6.2.1
PIECEWISE
LINEAR
APPROXIMATION
OF
SIGMOID
FUNCTION
...
93
6.2.2
ASSESSMENT
ON
APPROXIMATION
ERROR
..................................
94
6.3
HARDWARE
NEURAL
NETWORK
...................................................................
94
6.3.1
ABSOLUTE
VALUE
CALCULATION
..................................................
96
6.3.2
HWNN
LAYERS
.........................................................................
96
6.3.3
DIGITAL
DESIGN
OF
NEURONS
......................................................
97
6.3.4
PARALLEL
COMPUTATION
............................................................
97
6.4
CO-PROCESSING
......................................................................................
98
6.4.1
SOFTWARE
NEURAL
NETWORK
......................................................
98
6.4.2
TRAINING
ALGORITHM
...............................................................
99
6.4.3
PERFORMANCE
MONITOR
............................................................
99
6.4.4
PARAMETER
UPDATE
LOGIC
.........................................................
99
7
CONCLUSION
.........................................................................................
101
BIBLIOGRAPHY
............................................................................................
105
APPENDIX
............................................................................................................
111
A
LIST
OF
PUBLICATIONS
BY
THE
AUTHOR
.....................................................
111
B
LEVENBERG-MARQUARDT
TRAINING
.........................................................
113
C
PIECEWISE
CUBIC
INTERPOLATION
ALGORITHM
........................................
119
D
ERROR
VECTOR
MAGNITUDE
ESTIMATION
..................................................
120
E
ACP
CALCULATIONS
...............................................................................
121
F
CIRCUIT
ENVELOPE
SIMULATION
SUBCIRCUITS
...........................................
122
G
CHARACTERIZATION
RESULTS
OF
CLASS-AB
PA
...........................................
124
H
CHARACTERIZATION
RESULTS
OF
CLASS-ABJ
PA
........................................
126
I
MEASUREMENT
RESULTS
WITH
3G
UMTS
SIGNAL
.....................................
127
LIST
OF
FIGURES
..................................................................................................
128
LIST
OF
TABLES
.....................................................................................................
132
XIII
|
| adam_txt |
CONTENTS
AUTHOR
'
S
STATEMENT
.
III
LIST
OF
SYMBOLS
AND
ABBREVIATIONS
.
XV
I
INTRODUCTION
.
I
2
TOOLS
FOR
PERFORMANCE
ASSESSMENT
.
5
2.1
SIMULATION
ENVIRONMENT
.
5
2.1.1
CIRCUIT
ENVELOPE
SIMULATION
.
6
2.2
HARDWARE
TEST
PLATFORM
.
8
2.2.1
CLASS-AB
POWER
AMPLIFIER
.
10
2.2.2
CLASS-ABJ
POWER
AMPLIFIER
.
10
3
POWER
AMPLIFIER
MODELING
.
13
3.1
STATIC
NONLINEARITIES
.
14
3.1.1
POLYNOMIAL
MODEL
.
16
3.1.2
SALEH
'
S
MODEL
.
16
3.2
MEMORY
EFFECTS
.
17
3.2.1
SHORT
TERM
MEMORY
EFFECTS
.
17
3.2.2
LONG
TERM
MEMORY
EFFECTS
.
21
3.3
THERMAL
MEMORY
.
22
3.3.1
PERFORMANCE
OF
TEMPERATURE
SENSORS
.
23
3.3.2
THERMAL
TIME
CONSTANT
EVALUATION
.
24
3.3.3
CHARACTERIZATION
OF
THERMAL
MEMORY
.
24
3.3.4
MODELING
THERMAL
MEMORY
ON
MEASUREMENT
DATA
.
25
3.4
CHARGE
TRAPPING
IN
GAN
DEVICES
.
28
3.4.1
MEASURING
INTRINSIC
VOLTAGES
.
29
3.4.2
PEAK
VOLTAGE
DETECTOR
.
29
4
PREDISTORTION
TECHNIQUES
.
33
4.1
FEEDBACK
LINEARIZATION
.
34
4.2
FEEDFORWARD
LINEARIZATION
.
34
4.3
PREDISTORTION
.
35
4.3.1
DIRECT
AND
INDIRECT
LEARNING
.
38
4.4
DIGITAL
PREDISTORTION
(DPD)
.
38
4.4.1
MEMORYLESS
DPD
.
39
4.4.2
MITIGATION
OF
MEMORY
EFFECTS
.
39
XI
5
ARTIFICIAL
NEURAL
NETWORKS
FOR
PREDISTORTION
OF
RF
POWER
AMPLI
FIERS
.
41
5.1
NEURON
MODEL
.
41
5.1.1
INITIALIZATION
OF
NEURON
WEIGHTS
.
42
5.1.2
TRANSFER
FUNCTIONS
.
43
5.2
NETWORK
ARCHITECTURES
.
43
5.2.1
TIME
DELAY
LINES
FOR
MEMORY
EFFECT
MITIGATION
.
46
5.3
TRAINING
.
47
5.3.1
LEARNING
RULES
.
48
5.3.2
ADVANCED
TRAINING
ALGORITHMS
.
49
5.3.3
ASSESSMENT
ON
TRAINING
PERFORMANCE
.
50
5.4
IQ
IMBALANCE
MITIGATION
.
52
5.4.1
NEURAL
NETWORK
DESIGN
.
52
5.4.2
SIMULATION
RESULTS
.
52
5.4.3
MEASUREMENT
RESULTS
.
53
5.5
POWER
AMPLIFIER
LINEARIZATION
.
56
5.5.1
NEURAL
NETWORK
DESIGN
.
56
5.5.2
LINEARIZATION
OF
SALEH
MODEL
.
57
5.5.3
LINEARIZATION
OF
POLYNOMIAL
MODEL
.
57
5.5.4
LINEARIZATION
OF
MEMORY
POLYNOMIAL
MODEL
.
62
5.5.5
MEASUREMENT
RESULTS
.
63
5.6
IQ
IMBALANCE
MITIGATION
AND
PA
LINEARIZATION
.
66
5.6.1
NEURAL
NETWORK
DESIGN
.
66
5.6.2
SIMULATION
RESULTS
.
67
5.6.3
MEASUREMENT
RESULTS
.
69
5.7
LINEARIZATION
OF
THERMAL
MEMORY
.
71
5.7.1
NEURAL
NETWORK
DESIGN
.
71
5.7.2
RESULTS
ON
LINEARIZATION
OF
THERMAL
MEMORY
.
71
5.7.3
LINEARIZATION
PERFORMANCE
FOR
TDD
.
76
5.8
LINEARIZATION
OF
THERMAL
MEMORY
AND
IQ
IMBALANCE
.
78
5.8.1
NEURAL
NETWORK
DESIGN
.
78
5.8.2
RESULTS
ON
THERMAL
MEMORY
AND
IQ
IMBALANCE
.
79
5.9
LINEARIZATION
OF
CHARGE
TRAPPING
MEMORY
.
81
5.9.1
NEURAL
NETWORK
DESIGN
.
81
5.9.2
RESULTS
ON
LINEARIZATION
OF
CHARGE
TRAPPING
.
82
5.10
COMPARISON
OF
RESULTS
.
86
5.10.1
SIMULATION
RESULTS
.
86
5.10.2
MEASUREMENT
RESULTS
.
87
5.10.3
COMPARISON
WITH
VOLTERRA
DPD
.
89
5.10.4
COMPARISON
WITH
MEMORY
POLYNOMIAL
DPD
.
89
XII
6
FPGA
NEUROCOMPUTING
.
91
6.1
SYSTEM
DESIGN
.
91
6.2
IMPLEMENTATION
OF
TRANSFER
FUNCTIONS
.
92
6.2.1
PIECEWISE
LINEAR
APPROXIMATION
OF
SIGMOID
FUNCTION
.
93
6.2.2
ASSESSMENT
ON
APPROXIMATION
ERROR
.
94
6.3
HARDWARE
NEURAL
NETWORK
.
94
6.3.1
ABSOLUTE
VALUE
CALCULATION
.
96
6.3.2
HWNN
LAYERS
.
96
6.3.3
DIGITAL
DESIGN
OF
NEURONS
.
97
6.3.4
PARALLEL
COMPUTATION
.
97
6.4
CO-PROCESSING
.
98
6.4.1
SOFTWARE
NEURAL
NETWORK
.
98
6.4.2
TRAINING
ALGORITHM
.
99
6.4.3
PERFORMANCE
MONITOR
.
99
6.4.4
PARAMETER
UPDATE
LOGIC
.
99
7
CONCLUSION
.
101
BIBLIOGRAPHY
.
105
APPENDIX
.
111
A
LIST
OF
PUBLICATIONS
BY
THE
AUTHOR
.
111
B
LEVENBERG-MARQUARDT
TRAINING
.
113
C
PIECEWISE
CUBIC
INTERPOLATION
ALGORITHM
.
119
D
ERROR
VECTOR
MAGNITUDE
ESTIMATION
.
120
E
ACP
CALCULATIONS
.
121
F
CIRCUIT
ENVELOPE
SIMULATION
SUBCIRCUITS
.
122
G
CHARACTERIZATION
RESULTS
OF
CLASS-AB
PA
.
124
H
CHARACTERIZATION
RESULTS
OF
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| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Jüschke, Patrick |
| author_GND | (DE-588)1297534352 |
| author_facet | Jüschke, Patrick |
| author_role | aut |
| author_sort | Jüschke, Patrick |
| author_variant | p j pj |
| building | Verbundindex |
| bvnumber | BV049070429 |
| collection | ebook |
| ctrlnum | (OCoLC)1390742350 (DE-599)DNB1295955601 |
| doi_str_mv | 10.25593/978-3-96147-658-9 |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV049070429 |
| illustrated | Illustrated |
| index_date | 2024-07-03T22:26:57Z |
| indexdate | 2024-07-10T09:54:23Z |
| institution | BVB |
| institution_GND | (DE-588)1068111240 |
| isbn | 9783961476572 3961476578 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034332398 |
| oclc_num | 1390742350 |
| open_access_boolean | 1 |
| owner | DE-384 DE-473 DE-BY-UBG DE-703 DE-1051 DE-824 DE-29 DE-12 DE-91 DE-BY-TUM DE-19 DE-BY-UBM DE-1049 DE-92 DE-739 DE-898 DE-BY-UBR DE-355 DE-BY-UBR DE-706 DE-20 DE-1102 DE-860 DE-2174 DE-29T |
| owner_facet | DE-384 DE-473 DE-BY-UBG DE-703 DE-1051 DE-824 DE-29 DE-12 DE-91 DE-BY-TUM DE-19 DE-BY-UBM DE-1049 DE-92 DE-739 DE-898 DE-BY-UBR DE-355 DE-BY-UBR DE-706 DE-20 DE-1102 DE-860 DE-2174 DE-29T |
| physical | xx, 132 Seiten Illustrationen, Diagramme 24 cm x 17 cm, 390 g |
| psigel | ebook |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | FAU University Press |
| record_format | marc |
| series | FAU Studien aus der Elektrotechnik |
| series2 | FAU Studien aus der Elektrotechnik |
| spelling | Jüschke, Patrick (DE-588)1297534352 aut Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen Patrick Jüschke Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen Erlangen FAU University Press 2023 xx, 132 Seiten Illustrationen, Diagramme 24 cm x 17 cm, 390 g txt rdacontent n rdamedia nc rdacarrier FAU Studien aus der Elektrotechnik Band 21 Dissertation Friedrich-Alexander-Universität Erlangen-Nürnberg 2022 Digitale Vorverzerrung (DE-588)1275789080 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Transceiver (DE-588)4507910-9 gnd rswk-swf Kompensation (DE-588)4031980-5 gnd rswk-swf Hochfrequenztechnik (DE-588)4025202-4 gnd rswk-swf Radiofrequenzbereich (DE-588)4176824-3 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Leistungsverstärker (DE-588)4129735-0 gnd rswk-swf Leistungsverstärker Hochfrequenztechnik Maschinelles Lernen Künstliche Intelligenz Digitale Vorverzerrung RF Power Amplifiers Digital Predistortion Artificial Neural Networks FPGA Neurocomputing PA Modelling Memory Effects Machine Learning (DE-588)4113937-9 Hochschulschrift gnd-content Leistungsverstärker (DE-588)4129735-0 s Hochfrequenztechnik (DE-588)4025202-4 s Maschinelles Lernen (DE-588)4193754-5 s Künstliche Intelligenz (DE-588)4033447-8 s Digitale Vorverzerrung (DE-588)1275789080 s Neuronales Netz (DE-588)4226127-2 s DE-604 Transceiver (DE-588)4507910-9 s Radiofrequenzbereich (DE-588)4176824-3 s Kompensation (DE-588)4031980-5 s FAU University Press ein Imprint der Universität Erlangen-Nürnberg Universitätsbibliothek (DE-588)1068111240 pbl Erscheint auch als Online-Ausgabe 10.25593/978-3-96147-658-9 urn:nbn:de:bvb:29-opus4-235898 978-3-96147-658-9 FAU Studien aus der Elektrotechnik Band 21 (DE-604)BV042335665 21 https://open.fau.de/handle/openfau/23589 Verlag kostenfrei Volltext https://doi.org/10.25593/978-3-96147-658-9 Resolving-System kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-235898 Resolving-System kostenfrei Volltext https://d-nb.info/129602931X/34 Langzeitarchivierung Nationalbibliothek kostenfrei Volltext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034332398&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20230712 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Jüschke, Patrick Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen FAU Studien aus der Elektrotechnik Digitale Vorverzerrung (DE-588)1275789080 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Transceiver (DE-588)4507910-9 gnd Kompensation (DE-588)4031980-5 gnd Hochfrequenztechnik (DE-588)4025202-4 gnd Radiofrequenzbereich (DE-588)4176824-3 gnd Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Leistungsverstärker (DE-588)4129735-0 gnd |
| subject_GND | (DE-588)1275789080 (DE-588)4033447-8 (DE-588)4507910-9 (DE-588)4031980-5 (DE-588)4025202-4 (DE-588)4176824-3 (DE-588)4226127-2 (DE-588)4193754-5 (DE-588)4129735-0 (DE-588)4113937-9 |
| title | Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen |
| title_alt | Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen |
| title_auth | Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen |
| title_exact_search | Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen |
| title_exact_search_txtP | Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen |
| title_full | Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen Patrick Jüschke |
| title_fullStr | Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen Patrick Jüschke |
| title_full_unstemmed | Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen Patrick Jüschke |
| title_short | Physically inspired predistortion of RF Power Amplifiers with Artificial Neural Networks |
| title_sort | physically inspired predistortion of rf power amplifiers with artificial neural networks physikalisch inspirierte vorverzerrung von hochfrequenzleistungsverstarkern mit kunstlichen neuronalen netzen |
| title_sub | = Physikalisch inspirierte Vorverzerrung von Hochfrequenzleistungsverstärkern mit künstlichen neuronalen Netzen |
| topic | Digitale Vorverzerrung (DE-588)1275789080 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Transceiver (DE-588)4507910-9 gnd Kompensation (DE-588)4031980-5 gnd Hochfrequenztechnik (DE-588)4025202-4 gnd Radiofrequenzbereich (DE-588)4176824-3 gnd Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Leistungsverstärker (DE-588)4129735-0 gnd |
| topic_facet | Digitale Vorverzerrung Künstliche Intelligenz Transceiver Kompensation Hochfrequenztechnik Radiofrequenzbereich Neuronales Netz Maschinelles Lernen Leistungsverstärker Hochschulschrift |
| url | https://open.fau.de/handle/openfau/23589 https://doi.org/10.25593/978-3-96147-658-9 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-235898 https://d-nb.info/129602931X/34 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034332398&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV042335665 |
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