Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio

313 Pages·2016·1.57 MB·English

by  Mitola Joseph

Cognitive Radio
An Integrated Agent Architecture for Software Defined Radio
Dissertation
Doctor of Technology
Joseph Mitola III
Royal Institute of Technology (KTH)
Teleinformatics
Electrum 204
SE-164 40 Kista
Sweden
TRITA-IT AVH 00:01
ISSN 1403-5286
ISRN KTH/IT/AVH—00/01--SE
This dissertation is submitted in partial fulfillment of the degree of Doctor of Technology.
8 May, 2000
Copyright © 2000 Joseph Mitola III,
All Rights Reserved
Abstract
This dissertation addresses the intersection of personal wireless technology and
computational intelligence. The primary research issue addressed is the organization of radio
domain knowledge into data structures processable in real-time that integrate machine learning
and natural language processing technology into software radio. The thesis defines and develops
the cognitive radio architecture. The features needed in the architecture are derived from
cognitive radio use cases. These include inferring user communications context, shaping access-
network demand, and realizing a protocol for real-time radio spectrum rental. Mathematical
foundations for the knowledge-representation architecture are derived by applying point-set
topology to the requirements of the use cases. This results in the set-theoretic ontology of radio
knowledge defined in the Radio Knowledge Representation Language (RKRL). The
mathematical analysis also demonstrates that isochronous radio software is not Turing-
computable. Instead, it is constrained to a bounded-recursive subset of the total functions. A
rapid-prototype cognitive radio, CR1, was developed to apply these mathematical foundations in
a simulated environment. CR1 demonstrated the principles of cognitive radio and focused the
research issues. This led to an important contribution of this dissertation, the cognitive radio
architecture. This is an open architecture framework for integrating agent-based control, natural
language processing, and machine learning technology into software-defined radio platforms.
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Dedication
For Lynne’
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Acknowledgement
First, I would like to acknowledge the support and advice offered by Professor G. Q.
(“Chip”) Maguire of KTH without whose initial enthusiasm the research would not have been
undertaken. Without his tireless and patient mentoring, it might not have been completed. Dr.
Erland Wikoburg of my advisory committee kept me thinking about the physics and real-world
implications of my research. Professor Jens Zander threw down the gauntlett “its too long” a
couple of times. I still take too many words to say things, but now I’m more aware of the value
of brevity. Dr. Svante Signall, my Licentiate Opponent, wondered if the doctoral research could
be completed “in such a brief amount of time” – seems like forever and only yesterday. Theo
Kanter tackled the equally important network side of things with his work in active memory.
Working in the same laboratory as Theo has been stimulating. In addition, special thanks are due
to Professor Dr. re. nat. Friedrich Jondral for graciously agreeing to serve as opponent for the
dissertation defense at the time that he was organizing and conducting his own software radio
workshop at Karlsruhe. It has been a distinct pleasure and enlightening experience to interact
with Chip, Theo, and other professors, graduate students and researchers at KTH who are blazing
trails for the future of wireless technology.
The US Navy sponsored the modeling and simulation aspects of this research under
MITRE contract number DAAB07-99-C-C-201. Special thanks to Rosemary, Allan, and Bob.
In addition, I would like to acknowledge the climate of interest and support at MITRE created by
Dr. Bob Mikelskas. Bob supported my participation in MITRE’s accelerated graduate degree
program, which also supported part of this research.
Finally, this work is dedicated to my wife, Lynne’ in recognition of her initial enthusiasm
for the undertaking and of her unending love, patience, understanding and support during the two
a half years this work was in the making.
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Brief Glossary
Antecedent The pre-condition of an If-Then logic structure
3G Third Generation mobile cellular systems, e.g. based on CDMA
CIR Carrier to Interference Ratio, a measure of radio signal strength
CORBA Common Object Request Broker
ETSI European Telecommunications Standards Institute
HTML Hypertext Markup Language
IDL CORBA Interface Definition Language
ITU International Telecommunications Union
Method A procedure attached to a software object
Ontology The branch of metaphysics that studies the nature of existence or being
OOT Object-Oriented (OO) Technology
ORB Object Request Broker, software that dispatches remote procedure calls, etc.
PDA Personal Digital Assistant
PDANode An object that contains an srModel and related slots and methods.
Reinforcement Assigning an increased degree of belief or relevance to a stimulus, plan, etc.
RKRL Radio Knowledge Representation Language
SDL The Specification and Description Language (ITU Recommendation Z.100)
Slot A data structure that is part of a software object
srModel Stimulus-Response Model
Taxonomy Science dealing with description, identification, naming, and classification
UML Unified Modeling Language, an object-oriented design language
W-CDMA Wideband CDMA, the emerging international 3G standard
XML The eXtensible Markup Language
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Table of Contents
1 Introduction 1
1.1 Overview 1
1.2 Organization of this Thesis 3
2 Background 5
2.1 Air Interface- and Protocol-Definition Processes 5
2.2 The Software Radio, PDR and SDR 8
2.3 Environment-Aware Computing 13
2.4 Machine Learning 14
2.5 Natural Language Processing 28
2.6 Why Is This Research Important? 31
3 Previous Work 32
3.1 Computational Intelligence In Telecommunications 32
3.2 General Knowledge Representation 34
3.3 Knowledge Query Manipulation Languages 35
3.4 Control Knowledge 36
3.5 Uncertainty 38
3.6 Natural Language Processing 39
3.7 Radio Knowledge 41
3.8 Formal Methods 42
4 Cognitive Radio 45
4.1 Making Radio Self-Aware 45
4.2 The Cognition Cycle 47
4.3 Organization of Cognition Tasks 49
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4.4 Structuring Knowledge for Cognition Tasks 54
4.5 Potential Impact Areas 57
5 Cognitive Radio Use Cases 59
5.1 Radio Resource Management: Spectrum Pooling 59
5.2 Network Management Protocols Use Case 73
5.3 Services Delivery Use Case 76
5.4 Implications – Representing the Services Parameter Space 84
5.5 Type-Certified Downloads 87
5.6 Summary of Use Case Analyses 90
6 The Radio Knowledge Representation Language 91
6.1 Topological Analysis of Radio Knowledge Representation 91
6.2 RKRL Language Overview 102
6.3 Syntax 114
6.4 General Logical Sorts and Axioms 116
1.5 Micro-worlds of Space, Time, Spectrum, and Users 123
1.6 The Spatial Micro-worlds 124
1.7 The Generic Radio Micro-worlds 132
1.8 The Software Micro-worlds 135
1.9 The Wireless Function Micro-worlds 137
1.10 The Protocol Micro-worlds 139
1.11 The Network Micro-worlds 139
1.12 Mechanisms for Extending RKRL 140
2 The CR1 Prototype 142
2.1 Illustrative Scenarios 142
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2.2 The CR1 Java Environment 158
2.3 The Wake Cycle 175
2.4 The Sleep Cycle 180
2.5 The Prayer Cycle 182
2.6 Performance Metrics 183
2.7 Observations and Research Issues 186
3 The Cognitive Radio Architecture 194
3.1 Primary Cognitive Radio Functions 194
3.2 Behaviors 195
3.3 Cognitive Radio Components 196
3.4 A-Priori Knowledge Taxonomy 198
3.5 Observe-Phase Data Structures 199
3.6 Radio Procedure Knowledge Encapsulation 200
3.7 Orient-phase Components 201
3.8 Plan Phase Components 201
3.9 Decide-Phase Components 202
3.10 Act-Phase Knowledge Representation 202
3.11 Design Rules 202
4 Conclusions 205
4.1 Summary 205
4.2 Research Issues 206
5 References 207
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1 Introduction
This dissertation defines and develops cognitive radio, the integration of model-based
reasoning with software radio [1] technologies. It analyzes the architecture and performance of a
rapid-prototype cognitive radio, CR1 in a simulated environment. This architecture is based on
the set-theoretic ontology of radio knowledge defined in the Radio Knowledge Representation
Language (RKRL) [2]. CR1 incorporates machine-learning techniques to embrace the open-
domain framework of RKRL. These machine learning techniques make the software-radio
trainable in a broad sense, instead of just programmable. Although somewhat primitive, CR1’s
level of computational intelligence provides useful insights into the research issues surrounding
cognitive radio. CR1 integrates aspects of digital signal processing, speech processing, theory of
computing, rule-based expert systems, natural language processing, and machine learning into
the software radio domain. The inter-disciplinary nature of cognitive radio raises interesting
questions for future research and development.
1.1 Overview
This dissertation addresses the intersection of personal wireless technology and
computational intelligence. The term cognitive radio identifies the point at which wireless
personal digital assistants (PDAs) and the related networks are sufficiently computationally
intelligent about radio resources and related computer-to-computer communications to:
(a) detect user communications needs as a function of use context, and
(b) to provide radio resources and wireless services most appropriate to those needs.
The results are derived in part from the development of a rapid-prototype cognitive
wireless PDA, CR1, that uses structured computational models of services and radio etiquettes to
control the delivery of next-generation wireless services in a simulated environment. The
computational models underlying cognitive radio include RKRL, reinforced hierarchical
sequences, and the cognition cycle [3]. RKRL 0.3 consists of forty micro-worlds described in
the Extensible Markup Language (XML) and summarized in this thesis. Reinforced hierarchical
sequences organize CR1’s internal representation of itself, its user, and its environment,
including the radio networks accessed through its software-radio host platform. The architecture
1