ABSTRACT
Cognitive radio (CR) has been suggested as the solution to spectrum scarcity due to the fixed allocation employed worldwide by regulatory bodies.A secondary usercan opportunistically access the licensed frequency bands without causing harmful interference to the licensed user. In order to avoid interference to a primary user signal,the CR has to be aware about the spectrum usage inthe geographic area in which it wants to operate. The process of spectrumsensing is a fundamental task for obtaining this awareness and the result of this process determines the successful operation of cognitive radio. Energy detection is one of the methods of spectrum sensing with the lowest computational complexity but with low performance at low signal to noise ratio. Exploring energy detection has led to the application of many techniques one of which is the use of time-frequency analysis. This method employs distribution techniques for analyzing the energy spectral density of an observed signal with a view to setting a sensing threshold. However, the distribution techniques that were used in literature suffered from the problem of cross-terms which affect the analysis of the resulting distribution thereby leading to poor sensing performance at low signal-to-noise ratio. Smoothed pseudo Wigner-Ville distribution (SPWVD) of the time-frequency analysis has been employed in this work to reduce the effect of cross-terms and a better sensing threshold was gotten validated through comparison with the existing work which employed pseudo Wigner-Ville Distribution (PWVD) with an average reduction of 2.7% and 3% for additive white Gaussian noise (AWGN) channel, 4.1% and 4.7% for Rician channel, 6.4% and 8% for Rayleigh channel in the probabilities of missed detection and false alarm respectively. These results showed that significant reduction was achieved using SPWVD to set threshold. This work was carried out using the MATLAB R2013b time-frequency tool box.
TABLE OF CONTENTS
TITLE PAGE i
DECLARATION II
CERTIFICATION III
DEDICATION iv
ACKNOWLEDGEMENT v
ABSTRACT VII
LIST OF FIGURES xi
LIST OF TABLES xii
LIST OF ABBREVIATION xiii
CHAPTER ONE: INTRODUCTION
1.1 Background of Study 1
1.2Problem Statement 4
1.3Aim and Objectives 4
1.4Methodology 5
1.5Significance of the Research5
1.6Organization of the Dissertation 6
CHAPTER TWO: LITERATURE REVIEW
2.1 INTRODUCTION 7
2.2 Review of Fundamental Concepts 7
2.2.1 Cognitive Radio Concepts 7
2.2.2 Secondary User Spectrum Measurement 9
2.2.3 Spectrum Sensing 12
2.2.4 Spectrum Sensing Techniques 12
2.2.5 Time-Frequency Analysis (TFA) for Energy Detection 15
2.2.6 Cross-Terms (Interference Component) 15
IX
2.2.7 Kernels for Suppresing Cross-Terms 16
2.2.8 Analytic Associate 17
2.2.9 Ambiguity Domain 17
2.2.10 Time-Frequency Distribution Techniques 17
2.2.10.1 Wigner-Ville Distribution 18
2.2.10.1 Pseudo Wigner-Ville Distribution 19
2.2.10.1 Choi Williams Distribution 19
2.2.10.1 Cove Shaped Distribution 20
2.2.10.1 Smoothed Pseudo Wigner-Ville Distribution 21
2.2.11 Wireless Channel Model 21
2.2.12Comparison Metrics 25
2.3 REVIEW OF SIMILAR WORKS 25
CHAPTER THREE: MATERIALS AND METHODS
3.1 Introduction 34
3.2 System Model 34
3.3 Signal Simulation Process 35
3.3.1 AWGN Channel 37
3.3.2 Rayleigh Channel 37
3.3.3 Rician Channel 37
3.4 Hilbert Transforming the Simulated Signal 38
3.5 Smoothening and Calculating ESD using SPWDV41
3.6 Threshold Determination 43
3.7 GLRT on the Set Threshold 43
3.8Performance Metrics 44
3.8.1 Probability of False Alarm 44
X
3.8.2 Probability of Missed Detection 45
3.8.3 Signal to Noise Ratio 46
3.8.4 Relationship between PFA, PMD and SNR 46
CHAPTER FOUR: RESULT AND DISCUSSION
4.1 Introduction 48
4.2 Results of Comparisons 48
CHAPTER FIVE: CONCLUSION AND RECOMMENDATION
5.1 Summary 57
5.2 Conclusion 57
5.3 Significant Contribution 58
5.4 Recommendations 58
REFERENCES 59
XI
CHAPTER ONE
INTRODUCTION 1.1 Background of Study Electromagnetic spectrum is one of the most important resources required for radio communications. Spectrum utilization is regulated throughout the world so that essential servicescan be provided and also protected from harmful interference. Spectrum governanceacross the world traditionally tended toward static long-term exclusive use of spectrum, assigning license to a specific operator on a certain frequency band, thereby, giving them the exclusive right to operate on the licensed band (Biglieri et al., 2012). This static spectrum allocation strategy has led to many successful applications like broadcasting and mobile communication and it has also led to almostthe entire prime available spectrum being assigned for various applications (Biglieri et al., 2012). It may thus seem that there is little or no spectrum available for emerging wirelessproducts and services.
There had been several studies and reports over the years thatshowed that the static allocated spectrum was in fact vastly underutilized. A report presenting statistics regardingspectrum utilization showed that even during the high demand period of a politicalconvention such as the one held in 2004 in New York City, only about 13% of the spectrum opportunities were utilized (McHenry & McCloskey, 2004). Further, measurement on radio frequency bands from 30 MHz to 910 MHz was done in Mexico City of San Luis Potosi and showed 11.83% (Aguilar-Gonzalez et al., 2013), Kwara State of Nigeria at 48.5 MHz to 880 MHz showed 12.02% usage in the urban areas (Babalola et al., 2015), and also at 2.4GHz to 2.7GHz showed 22.56% usage in the urban areas (Ayeni et al., 2016), thus, all showing that spectrum was in fact underutilized. These findings also suggest that devices using advanced radio and signal processing technologyshould
2
be able to exploit underutilized spectrum. Much of the early motivationfor cognitive radio technology was indeed to accomplish such opportunistic spectrumuse and to also alleviate the artificial scarcity of prime spectrum. Thistechnology could revolutionize the way spectrum is allocated worldwide(Biglieri et al., 2012). Cognitive radio (CR) is the key enabling technology forthe implementation of dynamic spectrum access (DSA) which has been suggested as one of the most potent remedial measures for the fixed spectrum allocation which has led to spectrum scarcity (Weiss et al., 2012). A CR is an evolved softwaredefined radio (SDR) that has the ability to analyze its surrounding radioenvironment and decide how best to re-configure itself to suit operations without causing harmful interference to the licensed user (Javed & Mahmood, 2010). The CR has the ability to identify any opportunities that exist inthe spectrum band of interest and utilize them without causingany interference to the primary users (PUs). These opportunities exist in theform of spectrum holes or white spaces. A spectrum hole is the part of thespectrum that is devoid of the primary licensed user. The major functions of CR are: spectrum sensing which is the process of identifying thepresence of licensed users and unused frequency bands,that is, white spaces in those licensed bands;spectrum management which is identifying how long the secondary users can use those white spaces; spectrum sharing which is the decisions process of how to share the white spaces(spectrum hole) fairly among the secondary users; and spectrum mobility which is maintainingseamless communication during thetransition from one spectrum band to another when a primary user is sensed on the current band of transmission (Mounika et al., 2013).
In order to avoid interference to a primary user signal,the CR terminal has to be aware about the spectrum usage inthe geographic area in which it wants to operate. Spectrumsensing is a
3
fundamental task for obtaining this awareness(Angrisani et al., 2014). The challenging task is carrying out reliable spectrum sensing at low signal-to-noise ratio (SNR) as the successful operation of CR depends on the result of spectrum sensing. This means that a secondary user needs to reliably detect a primary user that is transmitting at very low power or that is locatedfar from the detection point. The selection of themost appropriate spectrum sensing technique should take intoaccount the trade-off between the performance and the computationalburden(Angrisani et al., 2014). Spectrum sensing can be done for detecting spectrum holes opportunity in which the cognitive radio can only transmit when the primary user is not transmitting or interference temperature detection in which case the cognitive radio is allowed to coexist with the primary user but transmitting at very low power in order not to cause interference to the primary user (Mounika et al., 2013). Various spectrum sensing techniques have been researched for detecting spectrum holes opportunity among which are: energy detection, cyclostationary feature detection and matched filtering (Vaidehi et al., 2015). All these techniques perform well at high signal to noise ratio. Energy detection has the lowest computational burden but also has low detection performance compared to others, especially at very low signal to noise ratios (Arthy & Periyasamy, 2015). Its low computational complexity is what attracts attention from researchers and that it does not require information of the primary user transmission which is a limitation in other detection techniques.
Earlier works on energy detection presented different ways of achieving a good spectrum sensing performance under varying signal to noise ratios and users, one of which is the method of time-frequency analysis using Wigner-Ville distribution. This work will focus on improving the
4
sensing threshold of energy detection using a different distribution technique of time-frequency analysis. This technique will improve on the weakness of the earlier distributions techniques that have been employed. 1.2 Problem Statement The successful operation of cognitive radios depends largely on the results of spectrum sensing. Energy detection being the simplest to implement in terms of computational complexity has attracted a great deal of attention from researchers in order to improve on it, as its performance is weak when the SNR is low. Researches had proposed ways of performing energy detection and the use of time-frequency analysis show a great potential. However, the distribution techniques used so far suffers from cross-terms which affects the readability of the resulting distribution, leading to no improvement on the sensing threshold. Therefore, there is the need to employ a different distribution technique that can improve on the weakness of the techniques present in literature so as to improve on the sensing threshold, especially at low signal to noise ratio. 1.3 Aim and Objectives The aim of this work is to determine an improved spectrum sensing threshold for energy detection through the use of time-frequency analysis by employing smoothed pseudo Wigner-Ville distribution to analyze the energy spectral content of the primary user signal. The research work had the following objectives:
1. To simulate signals of an orthogonal frequency division multiple access (OFDMA) user with varying signal-to-noise ratios.
5
2. To apply the smoothed pseudo Wigner-Ville distribution technique on the generated signals to determine the energy spectral content and determining a better sensing threshold.
3. To compare the result obtained with the work of Monfared et al., (2013) in order to establish the validity and improvement made of the technique used in this work.
1.4 Methodology The following steps will be taken to achieve the set objectives:
1. Simulation of an OFDMA signal in MATLAB R2013b environment to get a primary user signal.
2. Performing the Hilbert transform of the simulated signal to get the analytic associate of the signal.
3. Calculating the energy density of the signal in terms of the time and frequency using the smoothed pseudo Wigner-Ville distribution technique in the MATLAB R2013b time frequency toolbox.
4. Steps 1, 2 and 3were repeated for a hundred simulations and the mean energy densitywas taken in order to set a threshold.
5. Taking generalized likelihood ratio test to detect the presence or absence of a primary user based on the set threshold.
6. Comparing the performance of this method with the work of Monfared et al., (2013) to establish the validity as well as show improvement achieved.
1.5 Significance of the Research
The Wireless Regional Area Network (WRAN) under the IEEE 802.22 working group specify that for successful cognitive radio operation that opportunistically access a licensed spectrum as
6
the secondary user must be able to detect the presence of spectrum holes (absence of the primary user) with high level of precision that ensures minimum interference on the primary user. Earlier works on the use of time-frequency analysis for energy detection of the primary user transmitter has demonstrated a great potential of meeting the requirement of the standardization body hence, this research work was embarked on to further improve the performance of the technique by adopting smoothed pseudo Wigner-Ville distribution in order to better the limitation of the earlier research works. 1.6 Organization of the Dissertation The organization of this dissertation report is as follows: Chapter one presents the general background of the study. Chapter two discussed the review of fundamental concepts pertinent to the research work and detailed review of similar works was presented. In chapter three, the methods and materials adopted in this research are presented and explained in details. Chapter four presents the results and discussions and lastly in chapter five, conclusions and recommendations were discussed. Finally, all the references quoted in this dissertation report and appendices were provided.
7
IF YOU CAN'T FIND YOUR TOPIC, CLICK HERE TO HIRE A WRITER»
