Signal Processing for Cognitive Radios E-Book

Sudharman K. Jayaweera

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1304

Veröffentlichungsjahr: 2014

Beschreibung

This book examines signal processing techniques for cognitive radios. The book is divided into three parts: Part I, is an introduction to cognitive radios and presents a history of the cognitive radio (CR), and introduce their architecture, functionalities, ideal aspects, hardware platforms, and state-of-the-art developments. Dr. Jayaweera also introduces the specific type of CR that has gained the most research attention in recent years: the CR for Dynamic Spectrum Access (DSA). Part II of the book, Theoretical Foundations, guides the reader from classical to modern theories on statistical signal processing and inference. The author addresses detection and estimation theory, power spectrum estimation, classification, adaptive algorithms (machine learning), and inference and decision processes. Applications to the signal processing, inference and learning problems encountered in cognitive radios are interspersed throughout with concrete and accessible examples. Part III of the book, Signal Processing in Radios, identifies the key signal processing, inference, and learning tasks to be performed by wideband autonomous cognitive radios. The author provides signal processing solutions to each task by relating the tasks to materials covered in Part II. Specialized chapters then discuss specific signal processing algorithms required for DSA and DSS cognitive radios.

Leseprobe

CONTENTS

COVER

TITLE PAGE

COPYRIGHT PAGE

DEDICATION PAGE

PREFACE

PART I: INTRODUCTION TO COGNITIVE RADIOS

1 INTRODUCTION

1.1 INTRODUCTION

1.2 SIGNAL PROCESSING AND COGNITIVE RADIOS

1.3 SOFTWARE-DEFINED RADIOS

1.4 FROM SOFTWARE-DEFINED RADIOS TO COGNITIVE RADIOS

1.5 WHAT THIS BOOK IS ABOUT

1.6 SUMMARY

2 THE COGNITIVE RADIO

2.1 INTRODUCTION

2.2 A FUNCTIONAL MODEL OF A COGNITIVE RADIO

2.3 THE COGNITIVE RADIO ARCHITECTURE

2.4 THE IDEAL COGNITIVE RADIO

2.5 SIGNAL PROCESSING CHALLENGES IN COGNITIVE RADIOS

2.6 SUMMARY

3 COGNITIVE RADIOS AND DYNAMIC SPECTRUM SHARING

3.1 INTRODUCTION

3.2 INTERFERENCE AND SPECTRUM OPPORTUNITIES

3.3 DYNAMIC SPECTRUM ACCESS

3.4 DYNAMIC SPECTRUM LEASING

3.5 CHALLENGES IN DSS COGNITIVE RADIOS

3.6 COGNITIVE RADIOS AND FUTURE OF WIRELESS COMMUNICATIONS

3.7 SUMMARY

PART II: THEORETICAL FOUNDATIONS

4 INTRODUCTION TO DETECTION THEORY

4.1 INTRODUCTION

4.2 OPTIMALITY CRITERIA: BAYESIAN VERSUS NON-BAYESIAN

4.3 PARAMETRIC SIGNAL DETECTION THEORY

4.4 NONPARAMETRIC SIGNAL DETECTION THEORY

4.5 SUMMARY

5 INTRODUCTION TO ESTIMATION THEORY

5.1 INTRODUCTION

5.2 RANDOM PARAMETER ESTIMATION: BAYESIAN ESTIMATION

5.3 NONRANDOM PARAMETER ESTIMATION

5.4 SUMMARY

6 POWER SPECTRUM ESTIMATION

6.1 INTRODUCTION

6.2 PSD ESTIMATION OF A STATIONARY DISCRETE-TIME SIGNAL

6.3 BLACKMAN–TUKEY ESTIMATOR OF THE POWER SPECTRUM

6.4 OTHER PSD ESTIMATORS BASED ON MODIFIED PERIODOGRAMS

6.5 PSD ESTIMATION OF NONSTATIONARY DISCRETE-TIME SIGNALS

6.6 SPECTRAL CORRELATION OF CYCLOSTATIONARY SIGNALS

6.7 SUMMARY

7 MARKOV DECISION PROCESSES

7.1 INTRODUCTION

7.2

MARKOV DECISION PROCESSES

7.3 FINITE-HORIZON MDPs

7.4 INFINITE-HORIZON MDPs

7.5 PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES

7.6 SUMMARY

8 BAYESIAN NONPARAMETRIC CLASSIFICATION

8.1 INTRODUCTION

8.2

K

-MEANS CLASSIFICATION ALGORITHM

8.3

X

-MEANS CLASSIFICATION ALGORITHM

8.4 DIRICHLET PROCESS MIXTURE MODEL

8.5 BAYESIAN NONPARAMETRIC CLASSIFICATION BASED ON THE DPMM AND THE GIBBS SAMPLING

8.6 SUMMARY

PART III: SIGNAL PROCESSING IN COGNITIVE RADIOS

9 WIDEBAND SPECTRUM SENSING

9.1 INTRODUCTION

9.2 WIDEBAND SPECTRUM SENSING PROBLEM

9.3 WIDEBAND SPECTRUM SCANNING PROBLEM

9.4 SPECTRUM SEGMENTATION AND SUBBANDING

9.5 WIDEBAND SPECTRUM SENSING RECEIVER

9.6 SUBBAND SELECTION PROBLEM IN WIDEBAND SPECTRUM SENSING

9.7 A REDUCED COMPLEXITY OPTIMAL SUBBAND SELECTION FRAMEWORK WITH AN ALTERNATIVE REWARD FUNCTION

9.8 MACHINE-LEARNING AIDED SUBBAND SELECTION POLICIES

9.9 SUMMARY

10 SPECTRAL ACTIVITY DETECTION IN WIDEBAND COGNITIVE RADIOS

10.1 INTRODUCTION

10.2 OPTIMAL WIDEBAND SPECTRAL ACTIVITY DETECTION

10.3 WIDEBAND SPECTRAL ACTIVITY DETECTION

10.4 WAVELET TRANSFORM-BASED WIDEBAND SPECTRAL ACTIVITY DETECTION

10.5 WIDEBAND SPECTRAL ACTIVITY DETECTION IN NON-GAUSSIAN NOISE

10.6 WIDEBAND SPECTRAL ACTIVITY DETECTION WITH COMPRESSIVE SAMPLING

10.7 SUMMARY

11 SIGNAL CLASSIFICATION IN WIDEBAND COGNITIVE RADIOS

11.1 INTRODUCTION

11.2 SIGNAL CLASSIFICATION PROBLEM IN A WIDEBAND COGNITIVE RADIO

11.3 FEATURE EXTRACTION FOR SIGNAL CLASSIFICATION

11.4 A SIGNAL CLASSIFICATION ARCHITECTURE FOR A WIDEBAND COGNITIVE RADIO

11.5 BAYESIAN NONPARAMETRIC SIGNAL CLASSIFICATION

11.6 SEQUENTIAL BAYESIAN NONPARAMETRIC SIGNAL CLASSIFICATION

11.7 SUMMARY

12 PRIMARY SIGNAL DETECTION IN DSA COGNITIVE NETWORKS

12.1 INTRODUCTION

12.2 SPECTRUM SENSING PROBLEM IN DYNAMIC SPECTRUM SHARING CR NETWORKS

12.3 AUTONOMOUS SPECTRUM SENSING FOR DYNAMIC SPECTRUM SHARING

12.4 LIMITATIONS OF AUTONOMOUS SPECTRUM SENSING

12.5 COOPERATIVE SPECTRUM SENSING FOR DYNAMIC SPECTRUM SHARING

12.6 COOPERATIVE CHANNEL-STATE DETECTION

12.7 SUMMARY

13 SPECTRUM DECISION-MAKING IN DSA COGNITIVE NETWORKS

13.1 INTRODUCTION

13.2 PRIMARY CHANNEL DYNAMIC MODEL

13.3 SENSING DECISIONS IN DSS NETWORKS WITH AUTONOMOUS COGNITIVE RADIOS

13.4 SENSING DECISIONS IN COOPERATIVE DSS NETWORKS

13.5 SUMMARY

14 DYNAMIC SPECTRUM LEASING IN COGNITIVE RADIO NETWORKS

14.1 INTRODUCTION

14.2 DSL WITH DIRECT REWARDS TO PRIMARY USERS

14.3 DSL BASED ON ASYMMETRIC COOPERATION WITH PRIMARY USERS

14.4 SUMMARY

15 COOPERATIVE COGNITIVE COMMUNICATIONS

15.1 INTRODUCTION

15.2 COOPERATIVE SPECTRUM SENSING

15.3 COOPERATIVE SPECTRUM SENSING AND CHANNEL-ACCESS DECISIONS

15.4 COOPERATIVE COMMUNICATIONS STRATEGIES IN COGNITIVE RADIO NETWORKS

15.5 ASYMMETRIC COOPERATIVE RELAYING IN DSA COGNITIVE RADIOS

15.6 SUMMARY

16 MACHINE LEARNING IN COGNITIVE RADIOS

16.1 INTRODUCTION

16.2 ARTIFICIAL NEURAL NETWORKS

16.3 SUPPORT VECTOR MACHINES

16.4 REINFORCEMENT LEARNING

16.5 MULTIAGENT LEARNING

16.6 SUMMARY

APPENDIX A: NYQUIST SAMPLING THEOREM

APPENDIX B: A COLLECTION OF USEFUL PROBABILITY DISTRIBUTIONS

B.1 UNIVARIATE DISTRIBUTIONS

B.2 MULTIVARIATE DISTRIBUTIONS

APPENDIX C: CONJUGATE PRIORS

REFERENCES

INDEX

END USER LICENSE AGREEMENT

List of Tables

Chapter 06

TABLE 6.1 Commonly used window functions

Chapter 11

TABLE 11.1 Sequential Bayesian nonparametric classification of feature vectors that are drawn from a Gaussian and lognormal mixture using Algorithm 11.2

Appendix C

TABLE C.1 A collection of Conjugate Priors for Commonly Encountered Likelihoods [119]

List of Illustrations

Chapter 01

FIGURE 1.1 RF signals and the antenna are the sensations and sensory organ of a cognitive radio.

FIGURE 1.2 Signal processing in a receiver.

FIGURE 1.3 Signal processing in a transmitter.

FIGURE 1.4 Signals involved in the process of analog-to-digital conversion. (a) An analog signal. (b) A sampled discrete-time signal. (c) A digital signal.

FIGURE 1.5 The ideal software-defined radio concept.

FIGURE 1.6 A feasible software-defined radio architecture.

FIGURE 1.7 A realistic software-defined radio architecture.

FIGURE 1.8 A realistic software-defined radio (SDR) receiver (homodyne) with baseband I/Q channels.

FIGURE 1.9 The evolution of wireless devices from conventional to SDR to cognitive radio. (a) A conventional radio. (b) A software-defined radio. (c) A cognitive radio.

Chapter 02

FIGURE 2.1 Cognition and intelligence have certain overlap. But they each also possess certain attributes that are unique to each one of them.

FIGURE 2.2 Attributes of intelligence.

FIGURE 2.3 As abstract functional model of a congnitive radio.

FIGURE 2.4 Spectrum knowledge acquisition consists of a planning stage and a processing stage.

FIGURE 2.5 An ideal cognitive radio architecture.

FIGURE 2.6 Basic architecture of a cognitive radio.

Chapter 03

FIGURE 3.1 Different spectrum allocation models.

FIGURE 3.2 Spectrum coexistence under dynamic spectrum sharing, (a) Spectrum underlay, (b) Spectrum interweave (spectrum overlay).

FIGURE 3.3 Dynamic spectrum sharing as an interference channel.

FIGURE 3.4 Possible spectrum opportunities, (a) In a spectrum interweave-based DSS system, spectrum opportunities are considered along the time and frequency dimensions, (b) In a spectrum underlay-based DSS system, spectrum opportunities are considered along the space and frequency dimensions.

FIGURE 3.5 Interference regions and spectrum opportunities in a spectrum underlay system, (a) No spectrum opportunity because of significant interference to the primary receiver from the secondary transmitter, (b) A spectrum opportunity because both receivers are outside of the interference regions of the opposing transmitter.

FIGURE 3.6 Allowed secondary transmitter and receiver locations for spectrum opportunities with respect to a fixed primary (transmitter, receiver) location.

FIGURE 3.7 Dynamic spectrum access is almost entirely managed by the secondary cognitive radios.

FIGURE 3.8 Dynamic spectrum leasing provides the primary users an opportunity to be proactive in managing the spectrum sharing process in order to maximize their rewards.

FIGURE 3.9 Simultaneous sensing and communications with self-interference cancelation.

FIGURE 3.10 DSS with designated sensing time slots for simultaneous sensing and transmission, (a) One sensing slot per time frame, (b) One sensing slot per TDMA time slot.

Chapter 04

FIGURE 4.1 A submarine radar needs to detect when a signal reflected from a target is present in its received signal. (a) When there is no target, the received signal is comprised of noise and reflections from the ocean floor and surface. (b) When there is a target, the received signal contains a reflected signal from the target in addition to noise and reflections from the ocean floor and surface.

FIGURE 4.2 The structure of the Bayesian minimum probability of error optimal detector in the case of a scalar observation

y

.

FIGURE 4.3 Minimum achievable average probability of error in simple binary hypothesis testing in the presence of zero-mean additive Gaussian noise.