Spectrum occupancy and efficiency: A South African per-spective

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INTRODUCTION

Given predictions about exponential increases in the demand for spectrum [1], coupled with the fact that it is a limited natural resource, future technologies will need to balance spectrum efficiency with interference-free communication so as to ensure that the spectrum needs of future wireless networks can be met. Various approaches to achieving this balance have been proposed in the literature [9, 11], some of which will be explored in this chapter. The chapter begins with a discussion on spectral efficiency, the electromagnetic spectrum and a selection of technologies that have been earmarked for improving spectral efficiency in future wireless networks.
A brief overview on spectrum usage studies both in South Africa and around the world, together with a brief look at the South African spectrum regulatory environment, is also presented. One of the technologies earmarked for improving spectral efficiency, known as cognitive radio [8, 12], is then discussed in greater detail with a strong focus on spectrum sensing. Which is a critical process whereby information is gathered about the radio environment and the behaviour of other users of the spectrum.
The focus on spectrum sensing then continues as an overview of the cooperative spectrum sensing (CSS) concept, aimed at improving spectrum sensing accuracy through cooperation and sharing amongst cognitive radios (CR), is presented. The chapter is concluded by a discussion on how the behaviour of other users of the spectrum can be modelled and predicted so as to facilitate proactive decision making within cognitive radio networks.

SPECTRAL EFFICIENCY

The mobile traffic forecast illustrated in Figure 1.1, means that demand for spectrum in certain bands is rapidly increasing and will continue to do so into the future. A segment of the electromagnetic spectrum is presented in Figure 2.1. For convenience, the spectrum has been organised into general divisions roughly based on application and general propagation characteristics [13]. The divisions shown range from the bands used for long range radio through the visible spectrum and right up to X and Gamma-rays. The regions that are currently of interest for broadband wireless communication, the bands where the demand for spectrum is rapidly growing, are shaded in grey.

TABLE OF CONTENTS :

• CHAPTER 1 Introduction
  • 1.1 Background and motivation
  • 1.2 Objectives
  • 1.3 Contribution and outputs
  • 1.4 Thesis outline
• CHAPTER 2 Background: Improving spectral efficiency
  • 1.3.1 Research contribution
  • 1.3.2 Publications
• 2.1 Introduction
• 2.2 Spectral efficiency
• 2.3 Cognitive radio
• 2.4 Cooperative spectrum sensing
• 2.4.4 Cooperative sensing gains and costs
  • 2.3.1 Definition
  • 2.3.2 Primary functions
  • 2.3.3 Spectrum sensing
• 2.5 White space prediction
  • 2.4.1 Objectives and essential elements
  • 2.4.2 Approach
  • 2.4.3 Decision making
• 2.6 Conclusion

• CHAPTER 3 Spectrum occupancy and efficiency: A South African per-spective
  • 2.5.1 Artificial intelligence
  • 2.5.2 Linear models
  • 2.5.3 Statistical and moving average approaches
  • 2.5.4 Cooperative prediction
• 3.1 Introduction
• 3.2 Calculating spectrum occupancy
• 3.3 Spectrum measurement system
• 3.4 Measurement campaigns
  • 3.2.1 Maximum normal fit method
  • 3.2.2 Threshold estimation
  • 3.2.3 Validation
  • 3.2.4 Occupancy calculation
• 3.5 Spectral opportunities in the television broadcast bands
  • 3.3.1 Measurement setup
  • 3.3.2 System calibration and sensitivity
• 3.6 Characterisation of spectral activity in the mobile cellular bands
  • 3.4.1 Measurement description
  • 3.4.2 Measurement schedules
  • 3.4.3 Measurement sites
• 3.7 Comparative analysis
• 3.8 Conclusion
  • 3.6.1 Mobile 900 MHz bands
  • 3.6.2 Mobile 1800 MHz bands
  • 3.6.3 Mobile 2100 MHz bands
• CHAPTER 4 Primary user traffic prediction
  • 4.1 Introduction
  • 4.2 Traffic classification
  • 4.3 Prediction modelling
  • 4.4 Simulation results
    • 4.2.1 Periodicity
    • 4.2.2 Randomness
    • 4.2.3 Traffic density
  • 4.4.4 Prediction performance: Complexity
    • 4.3.1 Primary user traffic prediction
    • 4.3.2 Occupancy window approach
    • 4.3.3 Occupancy window example
  • 4.5 Conclusion
    • 4.4.1 Simulation environment
    • 4.4.2 Prediction performance: Deterministic and stochastic traffic
    • 4.4.3 Prediction performance: Traffic density
• CHAPTER 5 Cooperative prediction in cognitive radio networks
  • 5.1 Introduction
  • 5.2 System model for cooperative prediction
  • 5.2.1 Prediction scenario
  • 5.2.2 Radio environment
  • 5.3 Cooperative prediction
  • 5.4 Optimal forecasting
  • 5.4.1 Problem formulation
  • 5.4.2 Cooperative forecasting algorithm
  • 5.5 Simulation results
• CHAPTER 6 Forecasting for energy efficient spectrum sensing
  • 6.1 Introduction
  • 6.2 Optimal sensor node activation
  • 6.2.1 Problem formulation
  • 6.2.2 Implicit enumeration
  • 6.2.3 λ-Greedy algorithm
  • 6.3 Forecasting spectral opportunities
  • 6.4 Energy efficiency simulation
  • 6.4.1 Parameters
  • 6.4.2 Simulation results
  • 6.5 Conclusion
• CHAPTER 7 Conclusions and future research
  • 7.1 Summary of work
  • 7.2 Future research
  • APPENDIX A Derivation of the maximum normal fit method
  • A.1 Introduction
  • A.2 Noise threshold calculation
  • APPENDIX B Tshwane metropolitan area television channel assignments
  • B.1 Introduction
  • B.2 Very-high frequency channel assignments
  • B.3 Ultra-high frequency channel assignments

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