BANERJEE-DISSERTATION-2015

174 Pages·2015·4.49 MB·English

INTELLIGENT REAL-TIME ENVIRONMENT AND
PROCESS ADAPTIVE RADIO FREQUENCY
FRONT-ENDS FOR ULTRA LOW POWER
APPLICATIONS
A Thesis
Presented to
The Academic Faculty
by
Debashis Banerjee
In Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy in the
School of Electrical and Computer Engineering
Georgia Institute of Technology
Aug 2015
Copyright (cid:13)c 2015 by Debashis Banerjee
INTELLIGENT REAL-TIME ENVIRONMENT AND
PROCESS ADAPTIVE RADIO FREQUENCY
FRONT-ENDS FOR ULTRA LOW POWER
APPLICATIONS
Approved by:
Professor Abhijit Chatterjee, Advisor Professor Linda Milor
School of Electrical and Computer School of Electrical and Computer
Engineering Engineering
Georgia Institute of Technology Georgia Institute of Technology
Professor Hua Wang Professor Satish Kumar
School of Electrical and Computer The George W. Woodruff School of
Engineering Mechanical Engineering
Georgia Institute of Technology Georgia Institute of Technology
Professor John D. Cressler Date Approved: 23 April 2015
School of Electrical and Computer
Engineering
Georgia Institute of Technology
To my parents, my brother and my wife.
For always being there for me.
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ACKNOWLEDGEMENTS
A PhD is a journey, a long and hard one, but yet so enjoyable and fulfilling. Such
a journey is worth embarking on not only for the academic enrichment and self-
development it brings to ones life but also for the guidance, friendship and company
of teachers and friends. I have been extremely fortunate to have met many such
individuals over the course of this journey. First and foremost I would like to thank
my advisor Prof. Abhijit Chatterjee who has over the past few years guided, taught
and helped me. His technical expertise and constant drive and excitement about the
cutting edge of research inspired and will continue to inspire me. Additionally he has
been a role model and a friend to his students and I hope I can imbibe some of his
generosity, understanding, kindness in my personal life.
I would also like to thank my committee members Prof. Hua Wang, Prof. John
D. Cressler, Prof. Linda Milor and Prof. Satish Kumar. Georgia Tech would not be
the place it is without the many excellent faculty and I would like to thank all the
faculty members whom I have been lucky to learn from.
I would like to mention all my friends at Georgia Tech and Atlanta who made
these years such fun. A special thanks to Shreyas Sen & Shyam Devarakond for help-
ing me immensely in my research. I owe a lot to the countless hours of discussion
we had together. I am indebted to Debesh Bhatta for teaching me how to drive. A
big thanks to all the members of my lab including Sehun Kook, Jayaram Natara-
jan, Aritra Banerjee, Sabyasachi Deyati, Suvadeep Banerjee, Nicholas Zhou, Thomas
Moon, Xian Wang, Barry Muldrey & Joshua Wells for their help and co-operation.
Life would have been a lot less fun without my friends in Atlanta: Ayan Chakraborty,
Payel Chakraborty, Akash, Ananda Barua, Arindam Khan, Abhishek Banerjee and
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Anshuman Goswami. An acknowledgement would be incomplete without mentioning
the immense contribution of my family: my parents Ranu and Gautam Banerjee, my
brother Debayan and my wife Nairwita. My parents sacrificed a lot to provide me
a cushion from the harsh difficulties of life. Through my successes they relive the
opportunities they were not given. I owe everything to them. My brother has been a
friend to me and remains someone I can always talk freely if I need to. And last but
not the least I am grateful to my wife Nairwita for being the perfect partner. When
the going gets tough I know I can count on you. From technical discussions to actual
drafting of this thesis and my publications, she has helped me in every possible way.
I am proud to have you by my side.
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TABLE OF CONTENTS
DEDICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . iv
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi
1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Origin and History of the problem . . . . . . . . . . . . . . . . . . . 2
1.2 Traditional static RF front-end design . . . . . . . . . . . . . . . . . 3
1.3 Adaptive RF front-end design . . . . . . . . . . . . . . . . . . . . . 5
1.4 Need for use-aware adaptive MIMO front-end design . . . . . . . . . 7
1.5 Need for process-resilient self-learning front-end design . . . . . . . . 8
1.6 Alternate control strategies for adaptive front-end design . . . . . . 9
1.7 Prior Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.7.1 Traditional low-power RF front-end design techniques . . . . 10
1.7.2 Low-powermultipleinputmultipleoutput(MIMO)transceiver
systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.7.3 Channel-adaptive wireless system design . . . . . . . . . . . . 15
1.7.4 Process tolerant channel-adaptive wireless systems . . . . . . 17
1.8 Key Contributions of this work . . . . . . . . . . . . . . . . . . . . . 19
2 FUZZYLOGICANDANALYTICALEXPRESSIONBASEDNOISE-
DISTORTION CONTROL TECHNIQUES . . . . . . . . . . . . . . 22
2.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2 Adaptation Metric and Reconfiguration Strategy . . . . . . . . . . . 23
2.2.1 EVM based adaptation . . . . . . . . . . . . . . . . . . . . . 23
2.2.2 Reconfiguration strategy . . . . . . . . . . . . . . . . . . . . 23
2.3 Fuzzy Control Formulation . . . . . . . . . . . . . . . . . . . . . . . 26
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2.4 System Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.4.1 Signal and Interferer Strength Estimation . . . . . . . . . . . 29
2.4.2 Orthogonally tunable LNA . . . . . . . . . . . . . . . . . . . 30
2.4.3 System Characteristics . . . . . . . . . . . . . . . . . . . . . 31
2.5 Fuzzy Controller Design . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.7 Hardware Implementation . . . . . . . . . . . . . . . . . . . . . . . . 38
2.8 Analytical deduction of tuning knob values . . . . . . . . . . . . . . 39
2.8.1 Low signal strength and low interferer strength . . . . . . . . 40
2.8.2 Adequate signal strength and high interferer strength . . . . 41
2.8.3 IIP3 calculation . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.8.4 NF calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.9 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.10 Proposed methodology . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3 REAL-TIMEUSE-AWAREADAPTIVERFTRANSCEIVERSYS-
TEMSFORENERGYEFFICIENCYUNDERBERCONSTRAINTS 48
3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.3 MIMO modes and performance margins . . . . . . . . . . . . . . . . 53
3.4 Adaptation methodology . . . . . . . . . . . . . . . . . . . . . . . . 56
3.4.1 Adaptation Metric . . . . . . . . . . . . . . . . . . . . . . . . 56
3.4.2 Optimum tuning knob selection . . . . . . . . . . . . . . . . 57
3.5 Transceiver Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.5.1 Receiver adaptation . . . . . . . . . . . . . . . . . . . . . . . 60
3.5.2 Transmitter adaptation . . . . . . . . . . . . . . . . . . . . . 70
3.5.3 Effect of Coding Rate . . . . . . . . . . . . . . . . . . . . . . 73
3.6 Hardware Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.6.1 Receiver adaptation . . . . . . . . . . . . . . . . . . . . . . . 77
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3.6.2 Transmitter adaptation . . . . . . . . . . . . . . . . . . . . . 81
3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4 ADAPTIVE RF FRONT-END DESIGN VIA SELF-DISCOVERY:
USINGREAL-TIMEDATATOOPTIMIZEADAPTATIONCON-
TROL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.2.1 Stochastic Optimisation:Simulated Annealing(SA) . . . . . . 94
4.2.2 Heuristics used in Simulated Annealing . . . . . . . . . . . . 95
4.3 System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.3.1 Receiver Architecture . . . . . . . . . . . . . . . . . . . . . . 96
4.3.2 Channel Model and Channel Quality Estimation . . . . . . . 98
4.3.3 Current Consumption Estimation . . . . . . . . . . . . . . . 99
4.3.4 Resource overhead . . . . . . . . . . . . . . . . . . . . . . . . 99
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.4.1 Nominal Table . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.4.2 Real-time evolution of the table . . . . . . . . . . . . . . . . 100
4.5 Hardware Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5 SELF-LEARNINGRFRECEIVERSYSTEMS:PROCESSAWARE
REAL-TIME ADAPTATION TO CHANNEL CONDITIONS FOR
LOW POWER OPERATION . . . . . . . . . . . . . . . . . . . . . . 107
5.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.2 Learning based process resiliency . . . . . . . . . . . . . . . . . . . . 109
5.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.4 Overview of the approach . . . . . . . . . . . . . . . . . . . . . . . . 112
5.5 Detailed discussion of adaptation states/phases . . . . . . . . . . . . 118
5.5.1 Exploration State (EXPLORE) . . . . . . . . . . . . . . . . 118
5.5.2 Map Phase (MAP) . . . . . . . . . . . . . . . . . . . . . . . 119
5.5.3 Optimization Phase (OPT): . . . . . . . . . . . . . . . . . . 121
viii
5.5.4 Update Phase (UPDATE): . . . . . . . . . . . . . . . . . . . 122
5.6 Cluster formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.7 Overhead and protocol level implications . . . . . . . . . . . . . . . 125
5.8 Simulation setup and results . . . . . . . . . . . . . . . . . . . . . . 129
5.8.1 MIMO Front-end . . . . . . . . . . . . . . . . . . . . . . . . 129
5.8.2 SISO Front-end . . . . . . . . . . . . . . . . . . . . . . . . . 137
5.9 Artificial neural net training . . . . . . . . . . . . . . . . . . . . . . 140
5.10 Hardware validation . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
5.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
6 CONCLUSION AND FUTURE WORK . . . . . . . . . . . . . . . 145
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
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LIST OF TABLES
1 Heuristics for Noise-Distortion Modulation . . . . . . . . . . . . . . . 26
2 Possible choices of mode, modulation and code-rate . . . . . . . . . . 77
3 Relative Throughput For Different Mode, Modulation And Code Rate 78
4 Adaptive Transmitter Hardware Setup . . . . . . . . . . . . . . . . . 83
5 Code-Rate Used For Different Channel Condition And Modes . . . . 86
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