ABSTRACT
An important component of cognitive radio is spectrum sensing to detect the presence or absence of primary (licensed) user in the spectrum band of interest. However, the traditional static spectrum allocation strategies cause temporal and geographical holes of spectrum usage in licensed bands. Spectrum occupancy was categorized into completely free (white hole), partially free (grey hole) and fully occupied (black hole) in spectrum usage. Cognitive radio has a potential to improve spectrum utilization by opportunistically identifying and exploiting the available spectrum holes without causing harmful interference. One such detection method is the energy detection, used for this research, which is capable of sensing primary user transmitted energy signal. Furthermore, due to signal degradation caused by multipath fading and path loss, a single secondary user running the cognitive software on-board cannot accurately detect the presence of primary user and thus leads to missed detections. This drawback in a single user demands a different approach to the sensing in terms of number of active sensors which lead to the introduction of multiple cognitive radios in a network called a cooperative spectrum sensing network. A major challenge in spectrum sensing is the uncertainty associated with the detection of the primary user by the secondary users.This uncertainty arisesfrom the effects of noise, multi-path effects like randomness of primary user‟s presence in radio spectrum. In order to reduce the impact associated with the uncertainty problem and improve detection performance, this research carried out a comprehensive study between the use of Adaptive NeuroFuzzy Inference System and Monte Carlo techniques with a view to estimating detection threshold value for both cooperative and non-cooperative sensing for efficient utilization of radio spectrum. The ANFIS estimation gave a threshold value of -39dBm while the Monte Carlo gave a simulated threshold value of -71dBm. A simulated threshold value of -71dBm was obtained for thenon-cooperative spectrum sensing using Monte Carlo technique. This was validated by conducting extensive indoor and outdoor measurements using a commercially available energy detector with an incorporated spectrum analyzer. A measured threshold value of -71.138dBm was obtained which was similar to the threshold value of -71dBm realized from the non-cooperative model. Measurements were also conducted at two GSM frequencies viz: 900MHz and 1800MHzwith a view to ascertaining existence of spectrum holes at those frequency bands and for noise level characterization. The measurements were twofold: Indoor measurements for the determination of benchmark for noise level and for calibration of the outdoor measurements. From the experiments and measurements made, a minimum noise level of -91dBm and maximum noise level of -69dBm were obtained for 1800MHz and Outdoor measurements gave the signal plus noise levels of -91dBm and -64dBmfor minimum and maximumsensed energy at 1800MHzrespectively. While at 900MHz -79dBm and -55dBm were obtained for the minimum and maximumrespectively.
TABLE OF CONTENTS
TITLE PAGE – – – – – – – – – – i DECLARATION – – – – – – – – – ii CERTIFICATION – – – – – – – – iii DEDICATION – – – – – – – – – iv ACKNOWLEDGEMENT – – – – – – – – v ABSTRACT – – – – – – – – – – vii TABLE OF CONTENTS – – – – – – – – viii LIST OF FIGURES – – – – – – – – – xiii LIST OF TABLES – – – – – – – – – xvii LIST OF ABBREVIATIONS – – – – – – – xviii CHAPTER ONE: INTRODUCTION
1.1 Background of the Study – – – – – – – 1
1.2 Radio Spectrum Management – – – – – – 2 1.3 The Need for Flexibility in Spectrum Management – – – – 5 1.4 Enabler of Flexibility Spectrum Management – – – – 6 1.5 Cognitive Radio and Spectrum Sensing – – – – – 7 1.6 Motivation for the study – – – – – – – 10 1.7 Problem Definition – – – – – – – – 11 I.8 Aims and objectives – – – – – – – – 12 1.9 Research Methodology – – – – – – – 12
1.10 Significance of the Study – – – – – – – 13
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1.11 Scope of Work – – – – – – – – 14
1.12 Thesis Outline – – – – – – – – – 14 CHAPTER TWO: LITERATURE REVIEW 2.1 Introduction – – – – – – – – – 16 2.2 Review of Fundamental Concepts – – – – – – 16 2.2.1 Operation of Cognitive Radio – – – – – – 16 2.2.2 Area of Application of Cognitive Radio – – – – – 21 2.2.3 Dynamic Exclusive Use Model – – – – – – 22 2.2.4 Open Sharing Model – – – – – – – 23 2.2.5 Hierarchical Access Model – – – – – – – 23 2.2.6 Spectrum Sensing Techniques – – – – – – 25 2.2.7 Background to Fusion Center Operation – – – – – 36 2.2.8 Decision Analysis at the Fusion Center in Cooperative Spectrum sensing – 38 2.2.9 Detection Methods for Spectrum Sensing – – – – – 39 2.2.10 Spectrum Sensing Detection Methods Analyses – – – – 44 2.2.11 Background to Fuzzy Logic – – – – – – – 45 2.2.12 Background to Monte Carlo Method – – – – – – 47 2.2.13 Probability versus Energy Curve Characteristics – – – – 48 2.3 Review of Similar Research Works – – – – – – 50
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CHAPTER THREE: MATERIALS AND METHODS 3.1 Introduction – – – – – – – – – 61 3.2 Research Materials – – – – – – – – 61 3.2.1 MATLAB/Simulink – – – – – – – – 61 3.2.2 Cognitive Radio System – – – – – – – 62 3.3 Research Methodology – – – – – – – 63 3.3.1 Non-Cooperative Spectrum Sensing System Development – – – 64 3.3.2 Cooperative Spectrum Sensing System Development – – – 65 3.3.2.1 Determination of Detection Threshold – – – – 69 3.3.3 Spectrum Sensing Performance Indicators – – – – – 70 3.3.4 Spectrum Management System – – – – – – 71 3.3.5 Sensing Uncertainty and Threshold Selection – – – – 72 3.3.6 Monte Carlo Analysis – – – – – – – 73 3.3.7 ANFIS Analysis – – – – – – – 75 3.3.7.1 Fuzzy Variables – – – – – – – 76 3.3.7.2 Universe of Discourse – – – – – – 76 3.3.7.3 Linguistic Terms – – – – – – – 78 3.3.8 Management of Allocation and De-allocation of Radio Spectrum – – 79 3.3.9 Validation of Estimated Thresholds for Non-Cooperative Spectrum Sensing 80 3.3.9.1 Experimental Setup – – – – – – – 82 3.3.9.2 Indoor Measurements – – – – – – – 83 3.3.9.3 Outdoor Measurements – – – – – – 85
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CHAPTER FOUR: RESULTS AND DISCUSSIONS 4.1 Introduction – – – – – – – – – 87 4.2 Monte Carlo Experiments and Analyses – – – – – 87 4.2.1 Simulation Results – – – – – – – – 98 4.3 Presentation of results From Monte Carlo and ANFIS Estimation Experiments – – – – – – – – – 101 4.4 Comparative Analysis of Monte Carlo and ANFIS EstimationResults – 105 4.5 Experimental Results – – – – – – – – 106 4.5.1 Data Capture of Primary User Energy – – – – – 110 4.6 Further Quantitative Analysis and Collation of Relevant Results – – 111 4.7 Experiments for 1800MHz Generated from the Microwave Parabolic Dish Antenna – – – – – – – – – 112 4.8 Summary of Results – – – – – – – – 113 4.9 Interpretation of Results – – – – – – – 115 4.10 Discussion of Results – – – – – – – 117 4.10.1 Further Explanation of Experiments – – – – – 117 4.10.2 Capture Experiments at 900MHz and 1.8GHz – – – – 118 4.10.3 Microwave Generator Experiments at 1.8GHz – – – – 121 4.10.4 Outdoor Captured Channel Noise Signals at 900 and 1800MHZ – – 130 4.10.5 Validation Experiment for Comparison between Non-Cooperative Model andPrototype Results – – – – – – 135 4.10.6 Parameter Values for Non-Cooperative Cognitive Radio Model in MATLAB 135 4.11 Contribution to Academic Knowledge – – – – – 137
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CHAPTER FIVE: CONCLUSIONAND SUMMARY 5.1 Conclusion – – – – – – – – – 138 5.2 Limitation of the Work – – – – – – – 140 5.3 Recommendations for Further Work – – – – – 140 REFERENCES – – – – – – – – – 141 APPENDIX I – – – – – – – – – 148 APPENDIX II – – – – – – – – 151
CHAPTER ONE
INTRODUCTION
1.2 Background of Study
In recent years, wireless communication technologies have grown rapidly and more frequency spectrum resources are needed for the modern society to support numerous wireless services. It was established in a study that the current frequency spectrum resources are faced with two major challenges, namely, limited spectrum resources and inefficient spectrum utilization. Therefore, in order to address these challenges, cognitive radio was recently proposed to enhance efficient spectrum utilization(FCC, 2002; FCC, 2003). Additionally, spectrum is amongst the most heavily regulated and expensive natural resources around the world. Even though these facts are evident in almost all allocated spectrum available for wireless communications, studies indicate that many portions of the radio spectrum are grossly underutilized. An experimental analysis on shared spectrum shows that 62% of the white space below the 3GHz band even in the most densely populated areas such as Washington D.C., which host massive government and commercial activities (McHenry 2003). A band is considered as white space if it is wider than 1MHz and if it is unoccupied for 10 minutes or longer (McHenry 2003).
The existing regulatory polices ensure that license holder has exclusive rights to utilize the spectrum as no other parties may access it, hence protecting the licensee from interference. New wireless technologies aim to improve data rates, Quality of Services (QoS), robustness, latency, etc, and the bandwidth requirement for these technologies increases(3GPP, 2011). Consequent upon this, spectrum scarcity has become a major problem as network operators do not have access to large continuous blocks of spectrum. The increasingly large number of wireless devices also strains the capacity of wireless network and the accessible network spectrum such as the 2G
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–GSM bands around 900MHz and 1800MHz, 3G-UMTS band in 2.1GHz and ISM band of 2.4GHz (Chiang, Rowe et al. 2007). Furthermore, the high cost of prime spectrum constitutes a significant overhead cost to network operators and this was compounded by the accepted norm that customers expect to get more while paying less. Moreover, frequency ranges specifically meant for a particular usage are not necessarily licensed for such applications; therefore, operators often have to resort to less efficient systems of using the spectrum that is available to them. In general, wireless technologies appear to be encountering a critical problem of inadequate spectrum availability and perhaps more disturbingly, the available spectrum is grossly underutilized.
Contrary to common knowledge, recent studies on spectrum occupancy have provided a different insight into the spectrum usage and the problem with spectrum scarcity. Studies conducted around the world measured the activities of wireless technology over various allocated spectra throughout day and night made some stunning discoveries (Chiang, et al., 2007;Weiss, et al., 2010). While certain spectral ranges such as mobile communications systems and ISM bands are cluttered (Wellens, et al., 2007), there are large band of spectra where average usage is below 10% (Chiang, et al., 2007). 1.2 Radio Spectrum Management
The radio spectrum is one of the most important resources for communication. Traditionally, spectrum governance throughout the world has tended towards exclusivity of its use in large geographical areas, allocating frequency bands for specific applications and assigning license to specific users or service providers. This policy has generated a shortage of frequencies available for emerging wireless products and services, as most frequencies are already assigned. (Hassan
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and Boostanimehr, 2011).As a public resource, radio spectrum is being managed by governments to ensure that it is shared equitably to promote the public interest, convenience, or necessity (Nunno, 2002). It is being tightly regulated around the world by both the international and national regulators. At international level, the International Telecommunication Union (ITU) is managing spectrum. The International Telecommunication Union-Radio communication, Sector (ITU-R) maintains a table of frequency allocations which identifies spectrum bands for about fourty (40) categories of wireless services with the aim of avoiding interference among those services. Once the broad categories are established, each country may allocate spectrum for various services within its own borders in compliance with ITU‟s table of frequency allocations.
At the national level, the use of radio spectrum in most countries is currently being managed by government agencies rather than by market forces. For instance, in the United Kingdom, it is being regulated by the Office of Communications (OfCom) while the Federal Communications Commission (FCC) is responsible for radio spectrum regulation in the United States. The Nigerian Communications Commission (NCC) is responsible for radio spectrum regulation in Nigeria. In all of these countries, the primary tool of spectrum management by government is a licensing system through bidding. This involves spectrum being apportioned into blocks for specific uses, and assigned licenses for these blocks to specific users or companies. The main advantage of this licensing approach is that the licensee completely controls its assigned spectrum and can thus unilaterally manage interference between its users and their quality of service. However, one of the drawbacks of this policy is the impossibility of re-allocating spectrum to different technologies or other users who might have better use for the spectrum(Olafsson, et al., 2007). In addition, another demerit of this approach is that the
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frequency allocation procedures were lengthy and bureaucratic, opening up the possibility that the decision-making process could be influenced by non-relevant factors(Olafsson, et al., 2007).
Furthermore, the once and for all allocation of radio spectrum that gives exclusive right of using the spectrum to the licensed owners has been observed as the main cause of both spectrum underutilization and spectrum artificial scarcity (Akyildiz, et al., 2006;Haykin, 2005). This is because allocation by fixed spectrum assignment policy encourages the sporadic usage of spectrum as shown. Figure 1.1 shows the signal strength distribution over a large portion of the radio spectrum, reveals that while the spectrum usage is concentrated on certain portions of the spectrum, a significant amount of the spectrum remains unutilized in some bands. This necessitates the need for a more flexible means of controlling radio spectrum usage and control.
Figure 1.1: Spectrum Utilization (Akyildiz et al., 2006)
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1.3 The Need for Flexibility in Spectrum Management
Based on the disadvantages of the current fixed or rigid spectrum assignment policy, as well as increase in demand for radio spectrum, coupled with the increase in deployment of new wireless applications and devices in the last decade, it is obvious that strict command-and-control management of the spectrum is not suitable for the increasingly dynamic nature of spectrum usage. This has geared the regulatory body, such as the FCC, to begin to consider more flexible and comprehensive uses of available spectrum (FCC, 2002). The essence of this flexibility in spectrum usage is to deal with the conflicts between spectrum scarcity and spectrum underutilization, as well as to provide spectrum for emerging wireless communication technologies. Flexible usage means that an unlicensed or secondary user can opportunistically operate in an unused licensed spectrum bands. According to (Song, et al., 2007)and(Chen, et al., 2008), this new scheme is termed Opportunistic Spectrum Access (OSA) or Dynamic Spectrum Access (DSA).
In this new scheme for spectrum access control and management, the secondary users must not cause any interference to the primary or licensed users, as well as the other unlicensed users sharing the same portion of the spectrum. As the primary user still holds exclusive right to the spectrum; it is not its responsibility to mitigate any additional interference caused by unlicensed or secondary user‟s operation. It is the secondary user that periodically has to sense the spectrum to detect both the primary and other secondary users‟ transmissions and should be able to adapt to the varying spectrum conditions for the avoidance of mutual interference. An approach, which can meet these goals according to(Čabrić, et al., 2005), is to develop a radio that is able to reliably sense the spectral environment over a wide bandwidth, detect the presence/absence of a legacy or primary user, and use the spectrum only if communications do not interfere with the
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primary user. Radios that have such capability are termed cognitive radios (Chakravarthy, et al., 2005, Haykin, 2005 and Akyildiz, et al., 2006). 1.4 Enabler of Flexible Spectrum Management
In order to implement dynamic spectrum management and break the spectrum inflexibility policy, (Olafsson, et al., 2007)suggested that the following three close coupling elements: spectrum, ownership and applications needs to be broken. This is because the tight relationships, as shown in Figure 1.2, among these three elements support the present rigid regulatory policy. Hence, to break the interdependence of these three elements, a radio device that is neither application-bound nor licensed-bound will be the very viable solution (Olafsson, et al., 2007)
Figure 1.2: Relationship between Applications, Ownership and Spectrum (Olafsson et al., 2007)
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1.5 Cognitive Radio and Spectrum Sensing
The term „cognitive radio‟ is given to an emerging wireless access scheme that is „an intelligent wireless communication system that is aware of its surrounding environment to allow changes in certain operating parameters for the objective of providing reliable communications and efficient utilization of the radio spectrum‟ (Haykin, 2005). Cognitive radio is based on the concept of dynamic spectrum access, whereby licensed holders known as the primary users grant permission for spectrum access to non-licensed secondary users as long as interference to Primary User (PU) activity is minimal and confined(Hossain and Bhargava, 2007). Since the first mentioning in 1999(Mitola III, et al., 2012), cognitive radio has become the focus of significant research from industry, research centers and universities alike (Bergman et al., 2008). In fact, the popularity and potential of cognitive radio is acknowledged by IEEE to promote and develop a standard based on cognitive radio technology, „IEEE 802.22 Wireless Region Area Networks, to deliver wireless broadband to regional area using UHF /VHF TV bands between 54-862 MHZ in America (Stevenson, et al., 2009).
The integrity of cognitive radio relies on the ability of the secondary users to restrict interference to primary users and maintain a reliable QoS for its own operations(Mitola III and Maguire Jr, 1999). To achieve this goal, secondary users must actively sense primary user‟s spectrum to detect spectrum holes or spectrum opportunity. Spectrum opportunity is defined as a specific „dimension‟ of wireless communication that is currently not in use by the primary users which can be utilized by the secondary users without causing interference to the primary users(Horne, 2004). The most common model of spectrum opportunity is the time-frequency-space relation: a spectral band not in use by the primary users of that band at a particular time within a particular geographical space can be utilized by secondary users for secondary access(Kolodzy, 2001).
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Other possible dimensions for spectrum opportunity include coding, modulation, polarization, transmission direction/ angle, and possibly many more. All these dimensions collectively form the „spectrum space‟ as the radio resource that can be shared among primary users and secondary users(Hoyhtya, et al., 2008). Spectrum sensing is the task where the secondary users identify possible spectrum opportunities and is one of the most crucial components of cognitive radio.
Spectrum sensing is performed by the secondary users to sense a spectrum of interest with the objective of detecting the presence of any primary user‟s signals to prevent interference and identify spectrum opportunity for secondary access (Haykin, 2005andLiang, et al., 2008). Spectrum sensing poses new challenges in both hardware and software: radio front end to operate over wide frequency ranges, high speed signal processors and efficient system and algorithms to process computation demands, to name a few (Song, et al., 2007). Primary user activity changes from time to time, hence spectrum sensing has to be performed in a periodic manner, thus, forming a sensing cycle(Ghasemi and Sousa, 2007).
A common model of sensing cycle adopted by various studies to focus on the physical layer sensing consists of a spectrum sensing period directly followed by a data transmission period(Pei et al., 2007).
The secondary user uses spectrum sensing detectors to analyze the signal captured or observed during the sensing period, and based on the detection results, decides whether or not to utilize the spectrum during the transmission period. Sensing can be performed using the same transceiver and radio chain as data transmission, or independent radio chains dedicated for spectrum sensing and transmission(Shankar, et al., 2005). Spectrum sensing results in one of two decisions: false alarm where the secondary user declares primary user is present when the spectrum is empty and
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detection where the secondary user correctly declares a primary user is using the spectrum(Cabric et al., 2006). The performance of sensing detection is thus measured through the probability of these two events. Probability of false alarm is desired to be as low as possible, as false alarms results in the secondary user wasting spectrum opportunity by not transmitting. Probability of detection is desired to be as high as possible to minimize interference to primary user. These two metrics however, are conflicting parameters and the ideal case of perfect detection performance is rarely achievable. Therefore in practice, the primary user and secondary user specifies a set of detection requirements based on the maximum interference the primary user can tolerate and the minimum spectral efficiency required to operate the secondary user network (Cavalcanti et al. 2008). It is inevitable for cognitive radio to find unutilized portion of the spectrum more accurately for successful deployment of dynamic spectrum sensing, usually cooperative spectrum sensing is employed but still there is a margin to improve local sensing overhead among cognitive radio users, which can be reduced by improving local spectrum sensing. Several signal processing techniques for primary user‟s detection have been proposed in literature but there is still room for researchers to explore more sophisticated approaches to enhance sensing efficiency. This research proposes a two stage local spectrum sensing approach. In the first stage each cognitive radio performs one of the existing spectrum sensing techniquesthat is energy detection, matched filter detection, or cyclostationary detection. While in the second stage output from the technique employed in step one is combined using fuzzy logic to ultimately decide about the presence or absence of primary user. Monte Carlo simulation technique would then be used to analyze the performance of the spectrum detection algorithms in cognitive radio networks.
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1.6 Motivation for the study Cognitive radio which forms the cornerstone of secondary user equipment in intelligent, dynamic spectrum sensing and usage has created the need for profound studies in recent literature of new methods to optimally harness the scarce resource described previously. The appeal for cognitive radio amongst spectrum users draws inspiration from different sources and for many reasons. Some of the reasons for this study as highlighted in literature and based on current trends in cognitive radio research are as follows:
1. Limited spectrum availability due to saturated bands with simultaneous underutilization of spectrum by certain primary user , random or stochastic nature of primary user presence on allocated frequency spectrum which leads to opportunistic transmission on unused spectrum by secondary user(Ÿucek and Arslan, 2007). The uncertain nature of this exploitation leads to algorithms that are probability based.
2. Interference issues, presence of noise in the spectrum and false alarm rate due to imperfect sensing.
3. The above reasons create uncertainty in the spectrum sensing by cognitive radio which must be reduced sufficiently to allow more effective use of cognitive radio technology. This uncertainty reduction in the cognitive radio sensing operations has inspired the current research.
While in most of the literature, an underlying base scheme for solving the problem of spectrum sensing is made to consist of:
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1. A spectrum estimation methodology or model for sensing the primary user parameters of interest which will be used to obtain PU utilization information on the spectrum. Such a parameter to be used for the estimation might be the frequency or RF transmission power, energy.
2. Which when combined with the sensed data and analyzed with a statistical measure or heuristic is used in decision making. This decision engine or algorithm must be able to reach a sufficiently accurate and timely assessment of the spectrum characteristics in terms of primary user presence or absence and availability of holes to be exploited by secondary user.
The motivation for this study is to attempt the implementation of a fuzzy logic and monte-carlo based cognitive radio model to solve the problem of detection in the presence of uncertainty and also build a prototype system to test our designed and simulated hypothesis. 1.7 Problem Definition Spectrum sensing comes with its own attendant issues such as interference on the services of primary users caused by opportunistic probing of primary user spectrum bands by the secondary users, the non-availability of intelligent(accurate and timely) algorithms for detection, test and analysis of sensed data.These have a bearing on reducing the level of false alarm rate to a reasonable degree and making the sensing by the secondary user non-intrusive. These are the major challenges of the cognitive radio sensing process.
Furthermore, radio communications by licensed primary user within the spectrum band is a random process andits stochastic nature, makes for a certain degree of “uncertainty”; this is the chief culprit in efficient utilization of the cognitive radio. Analyzing this uncertainty using
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relevant metric like probability of false-alarm (Pfa) and probability of detection (Pd) in addition to setting of the accurate detection threshold will be used to address the uncertainty problem in the sensing process of the cognitive radio. I.8 Aim and Objectives The aim of this research is to estimate the detection thresholds for spectrum sensing in cognitive radio using Adaptive Fuzzy Inference System and MonteCarlo techniques to enhance spectrum sensing performance by reducing the uncertainty region caused by noise. The objectives are to:
i. Estimate detection threshold for non-cooperative spectrum sensing;
ii. Estimate detection threshold for cooperative spectrum sensing
iii. Validate the estimated detection threshold values for the non-cooperative spectrum sensing system.
1.9 Research Methodology The methodology set out shows the sequence of activities carried out in the implementation of the overall Research work. These are:
1) Developing a model for Non-Cooperative spectrum sensing
2) Simulating theNon-Cooperative model using (a) Monte-Carlo algorithm and (b) ANFIS algorithm for the determination of threshold value for spectrum sensing
3) Developing a model for Cooperative sensing
4) Simulating the Cooperative model in using (a) Monte-Carlo and (b) Fuzzy Logic algorithms with a view to determining threshold values for spectrum sensing.
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5) Comparison of (2) and (4) above
6) Experimental validation of the threshold for non-cooperative sensing.
1.10 Significance of the Study Despite the fact that a series of studies have been carried out on the spectrum sensing to detect different primary radio signals in a cognitive radio environment or network, none of these has been able to detect all forms of radio signals due to fundamental limitations of the central features employed in developing those detection methods. Preliminary investigations into a series of earlier-developed detection methods reveal that there are so many challenges associated with the determination of detection threshold due to uncertainty with regards to probabilities of miss detection and false alarm. Based on this observation, in order to improve the spectrum sensing performance this research made the following significant contributions:
i. Development of non-cooperative and cooperative simulation models in MATLAB‟S Simulink from suitable algorithms employing energy detectors for the sensing block. These models could provide a platform for studying and understanding of operational principles of cognitive radio. A detection threshold of -64dB was obtained for non-cooperative sensing and a detection threshold value of -39dB was obtained for the cooperative sensing. This result shows the superiority of cooperative sensing over non-cooperative.
ii. The research explored the implementation of two estimation techniques namely; Adaptive Neural Fuzzy Inference System (ANFIS) and Monte Carlo for the detection of optimum detection threshold in both models. A detection threshold seed value of 25dB was improved to 12.875dB (51.5%) using Monte Carlo techniques. While the estimated
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value of 14.3dB (57.2%) was obtained using ANFIS. Hence, ANFIS performed better than Monte Carlo technique.
iii. The research exploited the use of commercially acquired energy detection (SPECTRAN X) and the microwave link facilities at the ETEP laboratory to carry out the validation experiments. Indoor and outdoor measurements were conducted at 900MHz and 1800MHz. The detection threshold of -64dBm obtained from the non-cooperative simulation model was validated and a value of -71dBm was obtained using the hardware energy detector. Hence, the analytical and the simulation results obtained in this research provided significant improvement in the probability of detection of primary user signals.
1.11 Scope of Work This research work covers the estimation of the detection thresholds for spectrum sensing using Adaptive Fuzzy Inference System (ANFIS) and Monte Carlo technique in cognitive radio to enhance efficient utilization of radio spectrum. 1.12 Thesis Outline
The report of the thesis is organized into five chapters. Chapter One is the introductory chapter which contains the aim and objective of the work, motivation, problem definition, methodology and scope of the work. Chapter Two contains the literature review. Chapter Three is on Materials and Methods. It contains the detailed design concept and working principles of cognitive radio networks including non-cooperative and cooperative spectrum sensing models. Chapter Four is on Results and Discussions. It contains details of the field tests to evaluate the performance of the prototype in terms primary user‟s detection and validation of the model developed. It also
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contains the results and their discussions and analysis. Conclusion and recommendations are contained in Chapter Five. References and Appendices presented at the end of the report.
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