ABSTRACT
Cognitive radio technology introduces a revolutionary wireless communication network. This technology is employed to increase frequency spectrum efficiency of the limited spectral resources. Efficient resource allocation methodology has been a challenge to a good system performance of cognitive radio network. In this dissertation an improved power and sub channel resource allocation scheme is developed to ascertain the performance of a multi-hop relay cooperative communication system using orthogonal frequency division multiplexing (OFDM) technique. The cooperative diversity protocol employed at the relay terminal (Base Station) is the Amplify and forward (AAF) & decode and amplify (DAF) relaying protocols. The information is transmitted over a wireless Raleigh fading channel. The distance and number of relays are variable parameters which affect the performance and bit error rate (BER) of the secondary users (SU). However, comparing the result of the LOME model with theoretical value and another model it shows a reasonable agreement with the theoretical value than CDD and upper bound model. Since the bits’ error rate gradually reduced and tends to zero for power and subchannel simulation. The LOME model using DAF protocol at cognitive relays and subchannel scheme were implemented and results are validated by MATLAB simulation and statistics. Therefore, we propose an allocation scheme that maximizes the energy efficiency of the system while minimizing the BER of the network. Results validated shows that the mean square error (MSE) values for Figure 4.5, Figure 4.7, Figure 4.8 and Figure 4.9 has a value of 0.0000. Values closer to zero are better. The highest MSE value for the LOME model is 0.0005 compared with the Conventional differential detection (CDD) model which has a value as high as 0.0011. The MSE shows that the LOME model has a far better estimation of the theoretical model than the CDD model. The test results also show clearly that the root mean square error (RMSE) values for the LOME model are far lesser than the CDD model. The lowest value for the LOME model is seen as 0.0010 as in Figure 4.5 analysis compared to the CDD model of about 0.0167 as in Figure 4.3. Lower values of RMSE give a more accurate prediction model. We also have higher spread of median absolute deviation (MAD) in the CDD model than the LOME model. The highest value of 0.128 as in Figure 4.1 and lowest value of 0.06 as in Figure 4.5 analysis all occurred at the LOME model. The LOME model achieves about 62.5% efficiency compared with the theoretical model in terms of transmit power and BER. These validations proved that the LOME model gives a better BER and a better prediction of the theoretical scheme than the CDD model. These will generally result to a more efficient and robust cognitive radio network.
TABLE OF CONTENTS
Title Page i
Declaration ii
Dedication iii
Certification iv
Acknowledgements v
Table of Contents vi
List of Tables vii
List of Figures viii
Abbreviations ix
List of Symbols x
Definition of Terms xi
Abstract xii
CHAPTER 1: INTRODUCTION
1.1 Background of Study 1
1.2 Problem Statement 7
1.3 Aim and Objectives of the Study 8
1.4 Scope of Work 9
1.5 Significance of the Study 9
1.6 Thesis Outline 11
CHAPTER 2: LITERATURE REVIEW
2.1. Introduction 12
2.2. Overview of Cognitive Radio Networks (CRNs) 12
2.3. Resource Allocation in Cognitive Radio Networks (CRN) 18
2.4. Resource Allocation Problem Formulation in CRN 20
2.5. Solution Approaches to RA Problems 24
2.5.1. Solutions using classical optimization 24
2.5.2. Solutions by studying problem structure 26
2.5.3. Solution by separation or decomposition 26
2.5.4. Solution by linearization 27
2.5.5. Solution by relaxation 28
2.5.6. Solution by approximation 29
2.5.7. Solution by reformulation 30
2.5.8. Solutions by heuristics or meta-heuristics 31
2.5.9. Recursive-based and/or iterative-based heuristics 33
2.5.10. Solutions by multi-objective optimization 35
2.5.11. Solutions through soft computing 36
2.6. Solution by Graph Theory 39
2.7. Solution by Game Theory 40
2.8. Open-Ended Problems in Resource Allocation for CRN 42
2.9. Elements of Resource Allocation Problems in CRNs 44
2.10. Existing Algorithms 45
2.10.1. Algorithm 1 greedy algorithm 45
2.10.2. Algorithm1K-means clustering algorithm for CSS 47
2.10.3. Algorithm 1 iterative partitioned weighted geometric water-filling with individual peak power constraints (IGPP) 47
2.10.4. Genetic algorithms 49
2.10.5. Particle swarm intelligence algorithms 49
2.11. System models 50
2.11.1. General representation of the resource allocation formulation for heterogeneous cognitive radio networks 52
2.12. Simulation of RA models in CRN 54
2.12.1. Features of CRN simulators 54
2.12.2. Simulator classification 56
2.13. Related Literature on Power and Sub-channel Allocation 71
2.13.1. Power allocation 71
2.13.1.1. Relay selection in power allocation 73
2.13. 2. Sub-channel allocation 75
2.14. Conclusion 76
CHAPTER 3: MATERIALS AND METHODS
3.1. Material Introduction 78
3.2. Description of the Methodology 79
3.3. System Model 80
3.4. Problem Formulation 91
3.4.1. Combiners: selection combining (SC) and maximum ratio combiner (MRC) 92
3.4.2. LOME power allocation algorithm 95
3.5. Modeling the Sub-Channel Allocation Scheme 98
3.5.1. LOME sub-channel allocation algorithm 100
CHAPTER 4: RESULT AND DISCUSSION
4.1. Simulation Parameters/Results for Power Allocation 104
4.2. Simulation Parameters/Results for Sub-Channel Allocation 127
4.3. Summary of Simulation Results for power and subchannel allocation 129
4.4 Statistical evaluation of LOME resource allocation models and validation 129
4.4.1 Statistical Evaluation for Figure 4.1 scenario 129
4.4.2. Statistical Evaluation for Figure 4.2 scenario 132
4.4.3. Statistical evaluation for figure 4.3 scenario 134
4.4.4. Statistical evaluation for figure 4.4 scenario 136
4.4.5. Statistical evaluation for figure 4.5 scenario 138
4.4.6. Statistical evaluation for figure 4.6 scenario 140
4.4.7. Statistical evaluation for figure 4.7 scenario 142
4.4.8. Statistical evaluation for figure 4.8 scenario. 144
4.4.9. Statistical evaluation for figure 4.9 scenario 146
4.4.10. Statistical evaluation for figure 4.10 scenario 148
4.5. Inferences of Statistical Evaluation On the Resource Allocation Scheme LOME. 151
4.5.1 MSE 151
4.5.2 RMSE 151
4.5.3 MAD 152
4.5.4 MAPE 152
4.6 Error performance 152
CHAPTER 5: CONCLUSION, RECOMMENDATION AND CONTRIBUTION TO KNOWLEDGE
5.1. Conclusion 154
5.2. Recommendations 155
5.3. Contributions to Knowledge 155
References 157
Appendices 170
LIST OF TABLES
2.1. Description of resource allocation problem formula in CRN 23
2.2. Summary of solution techniques to RA problems in CRNs 37
4.1. Simulation Parameters 104
4.2. BER for Theoretical value LOME and CDD power allocation using 2relays M=2, No = 1 and Ns = 1E6 105
4.3. BER for Theoretical value LOME and CDD power allocation using 3relays M=4, No = 1 and Ns = 1E6 107
4.4. BER for Theoretical value LOME and CDD power allocation using 2relays M=4, No = 1 and Ns = 1E6 109
4.5. BER for Theoretical value LOME and CDD power allocation using 2relays M=2, No = 1 and Ns = 1E6 110
4.6. BER for Theoretical value LOME and CDD power allocation using 1relays M=2, No = 1 and Ns = 1E6 112
4.7. BER for Theoretical value LOME and CDD power allocation using 2relays M=2, No = 1 and Ns = 1E4 113
4.8. BER for Theoretical value LOME and CDD power allocation using 3relays M=2, No = 1 and Ns = 1E4 114
4.9. BER for Theoretical value LOME and CDD power allocation using 4relays M=2, No = 1 and Ns = 1E6 116
4.10. BER for Theoretical value LOME and CDD power allocation using 4relays M=2, No = 2 and Ns = 1E4 117
4.11. BER for Theoretical value LOME and CDD power allocation using 4relays M=2, No = 3 and Ns = 1E4 119
4.12. Statistical evaluation for figure 4.1 130
4.13. Statistical evaluation for figure 4.2 132
4.14. Statistical evaluation for figure 4.3 134
4.15. Statistical evaluation for figure 4.4 136
4.16. Statistical evaluation for figure 4.5 138
4.17. Statistical evaluation for figure 4.6 140
4.18. Statistical evaluation for figure 4.7 142
4.19. Statistical evaluation for figure 4.8 144
4.20. Statistical evaluation for figure 4.9 146
4.21. Statistical evaluation for figure 4.10 148
4.22. Summary of error analysis of LOME model vs theoretical model 150
4.23. Summary of error analysis of CDD model vs theoretical model 150
LIST OF FIGURES
2.1: Basic elements of cognitive radio operation 16
2.2: Cognitive radio functionalities 17
2.3: CRN transceiver architecture showing the radio environment with PU activities 55
2.4: CRN simulation in GNU Radio 57
2.5: Visualization of NS-2 simulation (Dong et al., 2018) 61
2.6: The block structure of the CRE-NS3 extension in ns-3.1 65
3.1. Schematic diagram of cognitive radio network 80
3.2. System model for a multi-relaying protocol 81
3.3: LOME Algorithm Flowchart 94
3.4: Flowchart for sub-channel algorithm 102
4.1: Comparison of LOME model with theoretical value, CDD and upper bound model showing the BER, using 4 relays, M=4 No = 1, Ns = 1E6 106
4.2: Comparison of the LOME model with theoretical value, CDD and upper bound model showing the BER, using 3 relays, M=4, No = 1, Ns = 1E6 108
4.3: Comparison of the LOME model with theoretical value, CDD and upper bound model showing the BER, using 2 relays, M=4 No = 1, Ns = 1E6 109
4.4: Comparison of the LOME model with theoretical value, CDD and upper bound model showing the BER, using 2 relays, M=4 No = 2, Ns = 1E6 111
4.5: Comparison of the LOME model with theoretical value, CDD and upper bound model showing the BER, using 1 relay, M=2, number of symbols, Ns =104 and noise variance N0=1. 112
4.6: Comparison of the LOME model with theoretical value, CDD and upper bound model showing the BER, using 2 relays, M=2, number of symbols, Ns =104 and noise variance N0=1. 113
4.7: Comparison of the LOME model with theoretical value, CDD and upper bound model showing the BER, using 3 relays, M=2, number of symbols, Ns =104 and noise variance N0=1. 115
4.8: Comparison of the LOME model with theoretical value, CDD and upper bound model showing the BER, using 4 relays, M=2, number of symbols, Ns =104 and noise variance N0=1. 116
4.9: Comparison of the LOME model with theoretical value, CDD and upper bound model showing the BER, using 4 relays, M=2, number of symbols, Ns =104 and noise variance N0=2. 117
4.10: Comparison of the LOME model with theoretical value, CDD and upper bound model showing the BER, using 4 relays, M=2, number of symbols, Ns =104 and noise variance N0=3. 119
4.11: Comparison of simulation SC and MRC combining showing the BER, using 2 relays, M=2, number of symbols, Ns =104 and noise variance N0=1. 120
4.12: Comparison of simulation SC and MRC combining showing the BER, using 2 relays, M=2, number of symbols, Ns =103 and noise variance N0=1. 121
4.13: Comparison of simulation SC and MRC combining showing the BER, using 4 relays, M=2, number of symbols, Ns =102 and noise variance N0=1. 122
4.14: Comparison of simulation SC and MRC combining showing the BER, using 4 relays, M=2, number of symbols, Ns =101 and noise variance N0=1. 122
4.15: Combing MRC (maximal ratio combining) and SC (selection combining) from the LOME model, R=3. M = 2, No = 1E2, No = 1 124
4.16: Combing MRC (maximal ratio combining) and SC (selection combining)
from the LOME model, R=4. M = 2, No = 1E2, No = 1 125
4.17: Combing MRC (maximal ratio combining) and SC (selection combining) from the LOME model, R=2. M = 2, No = 1E2, No = 1 126
4.18: BER Vs Eb/No at M=2, N=2, and 8 pilot symbols per frame 127
4.19: BER Vs Eb/No at M=4, N=2, and 8 pilot symbols per frame 128
4.20: BER Vs Eb/No at M=3, N=2, and 8 pilot symbols per frame. 128
ABBREVIATIONS
AAF Amplify and Forward
AWGN Additive White Gaussian Noise
BER Bit error rate
CR Cognitive Radio
CSI Channel State Information
DAF Differential amplify and forward
DAF Decode and Forwards
DSA Dynamic spectrum access
MIMO Multiple input multiple output
M-QAM M-ary Quadrature Amplitude Modulation
MPSK M-ary Phase Shift Keying
MRC Maximum combining ratio
OFDM Orthogonal frequency-division multiplexing
PU Primary Users
QAM Quadrature Amplitude Modulation
QPSK Quadrature Phase Shift Keying
RA Resource allocation
SDR Software defined radio
SER Symbol Error Rate
SNR Signal to noise ratio
SU Secondary Users
CCD Conventional differential detection
SCF Small-cell First
SC Small Cell
PF Proportional Fairness
PSD Partially Shared Deployment
SINR Signal to Interference plus Noise Ratio
SUBS Secondary users base station
LIST OF SYMBOLS
P_s Source power
P_r Relay power
P Total power
M M is the constellation size.
Π The steady-state probability vector for the number of available PU bands
B Signal
g(N) Variance between channel links
α Power allocation factor
ω_1,ω_2 Parameters representing two random variables X1 and X2
Z Harmonic mean computation
x Transmitted information
N0 Variance between complex gaussian random variables, is the power value of additive white Gaussian noise for each channel.
a_(s,d) (n),a_(s,r) (n)
Random variables
Z_(s,ri) (n) AWGN noises
x(n) Information transmitted from the source
N Number of relays
h_(r_(i,) d) Channel coefficient of the relay to the destination
S Source
D Destination
φ Symbol error rate
DEFINITION OF TERMS
Cognitive Radio: Cognitive Radio (CR) is an adaptive, intelligent radio and network technology that can automatically detect available spectrum and change transmission parameters enabling more communications to run concurrently and also improve radio operating behavior.
Cognitive radio network: Cognitive radio network is a network where the spectrum access is allowed only in opportunistic manner and does not have license to operate in a desired band is called Cognitive Radio Network. It can be deployed both as an infrastructure network and an ad hoc network (Aniqua, 2015).
Lagrange multipliers: (mathematical method) In mathematical optimization, the method of Lagrange multipliers is a strategy for finding the local maxima and minima of a function subject to equality constraints (i.e., subject to the condition that one or more equations have to be satisfied exactly by the chosen values of the variables).
Multiple input, multiple output (MIMO): Multiple input, multiple output is an antenna technology for wireless communications in which multiple antennas are used at both the source (transmitter) and the destination (receiver). The antennas at each end of the communications circuit are combined to minimize errors and optimize data speed.
M-ary Phase Shift Keying (MPSK): is a modulation where data bits select one of M phase shifted versions of the carrier to transmit the data
Orthogonal frequency-division multiplexing (OFDM): Orthogonal frequency-division multiplexing (OFDM) is a method of digital signal modulation in which a single data stream is split across several separate narrowband channels at different frequencies to reduce interference and crosstalk.
Primary Users: These wireless devices are the primary license-holders of the spectrum band of interest. In general, they have priority accessing the spectrum, and subject to certain Quality of Service (QoS) constraints which must be guaranteed (Aniqua, 2015).
Resource allocation: Resource allocation describes the problem of sharing the network resources to competing users so that we maximize the satisfaction in the network
SDR: A software defined radio (SDR) is a wireless communication system which can be reconfigured by software reprogramming to operate on different frequencies with different protocols.
Secondary Users: These users may access the spectrum which is licensed to the primary users. They are thus secondary users of the wireless spectrum, and are often envisioned to be cognitive radios.
Signal to Interference plus Noise Ratio SINR: is commonly used to measure the quality of wireless connections. Typically, the energy of a signal fades with distance, which is referred to as a path loss in wireless networks. Conversely, in wired networks the existence of a wired path between the sender or transmitter and the receiver determines the correct reception of data. In a wireless network ones has to take other factors into account (e.g. the background noise, interfering strength of other simultaneous transmission).
Small cells: Small cells are low-powered cellular radio access nodes that operate in licensed and unlicensed spectrum that have a range of 10 meters to a few kilometers. They make best use of available spectrum by re-using the same frequencies many times within a geographical area.
Conventional differential detection (CDD): Co channel deployment can bound channel access delay and improve network capacity due to mitigating the collision and interference among different Access Points (APs).
Quadrature amplitude modulation: Quadrature amplitude modulation (QAM) is the name of a family of digital modulation methods and a related family of analog modulation methods widely used in modern telecommunications to transmit information.
QPSK: QPSK is a form of Phase Shift Keying in which two bits are modulated at once, selecting one of four possible carrier phase shifts (0, 90, 180, or 270 degrees). QPSK allows the signal to carry twice as much information as ordinary PSK using the same bandwidth.
AWGN: Additive White Gaussian Noise (AWGN) is the statistically random radio noise characterized by a wide frequency range with regards to a signal in a communications channel.
DAF: The Decode and amplify forward relay protocol is a method also known as regenerative processing in increasing diversity gain.
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND OF STUDY
The wireless communication systems are making the transition from wireless telephony to interactive internet data and multi-media type of applications, for desired higher data rate transmission. As more and more devices go wireless, it is not hard to imagine that future technologies will face spectral crowding, congestion and the coexistence of wireless devices will be a major issue. (Mustafa, et al., 2008). The trend seems to be that every gadget and application is eventually becoming wireless. Having no wires may enhance the physical look of the product, ease up the installation process but especially improve the mobility of the device. In the traditional approach of spectrum allocation, the spectrum is assigned particularly to primary users to operate in a certain specific band. Till now most of the useful spectrum is still occupied or licensed. Even license-exempt has also become overcrowded. On the other hand, being wireless also means that all communication needs to be done through the radio spectrum. When all the wireless systems are allocated a specific frequency band on which they can operate, the spectrum will eventually become a scarce resource. However, the spectrum may often remain severely under-utilized since the users of the spectrum do not normally transmit at all times and especially not in all locations simultaneously. Cognitive Radio Network (CRN) is an intelligent radio network that can be programmed and configured dynamically, in which a transceiver can intelligently detect which communication configured to move into the vacant channel while avoiding occupied one. CRN was introduced to alleviate the spectrum shortage issues by enabling secondary users (unlicensed) to co-exist with primary users (licensed) thereby improving the utilization efficiency of the existing radio spectrum. Generally, the operation of a CR network consists of three fundamental tasks, which are (1) radio-scene analysis, (2) channel identification and (3) power control and spectrum management (Haykin, 2005; Wang and Liu, 2011). Through interaction with the radio environment, these three tasks constitute a basic cognitive cycle. The task of channel identification encompasses the estimation ofchannel state information and the prediction of channel capacity for utilization by the transmitter. In dynamic spectrum management, a SU may share the spectrum with PUs, other SUs, or both, while the spectrum rights are owned by primary systems. Thus, the high spectrum efficiency is mainly rooted in an appropriate spectrum sharing mechanism between the primary and the secondary networks. When SUs coexist with PUs in a licensed band, the interference level to the PUs should be limited under a certain threshold. When multiple SUs share the same portion of the spectrum, their access should be coordinated to mitigate their mutual collisions and interference.
The basic function of a cognitive radio network is to sense the spectrum accurately by avoiding any chances for interfering with primary users or licensed users. The cognitive radio achieves these by the spectrum sensing methodology where a region of the spectrum is being sensed to detect whether it is already occupied or not occupied. If it is found idle, it is temporarily used by the cognitive user (secondary user) to transmit its own signals before the licensed primary user returns (Shen et al 2005). Cognitive radio can interact with its radio environment to acquire important information about its surroundings, including the presence of primary users and the appearance of spectrum holes (white space) during spectrum sensing. It is only with this information that it can adapt its transmitting and receiving parameters, such as transmission power, frequency, and modulation schemes, in order to achieve efficient spectrum utilization. Therefore, spectrum sensing and analysis is the first critical step toward dynamic spectrum management. In this dissertation, we discuss three aspects of spectrum sensing in terms of spectrum-hole detection, for determining additional available spectrum resources, including a comparison of several detection techniques. The potential requirements of spectrum sensing are Spectrum Sensing Techniques, multiple cognitive users; and interference temperature detection, which measures the interference level observed at a receiver and is used to protect licensed primary users from harmful interference due to unlicensed secondary users. A cognitive radio network with a multi-hop requirement is called a cognitive radio ad-hoc network (Hou, et al., 2008). Due to the increase in the wireless device count, the radio spectrum is becoming increasingly congested which may result in a high level of interference and inefficient use of spectrum. These unlicensed bands cannot be used by the conventional wireless system. CRN also is known as smart/intelligent radio technology learns its environment and changes its transmission parameters. CRN is autonomous and is software controlled. It changes its parameters dynamically without the intervention of the user. The unlicensed frequency band is known as the spectrum holes (Sallent, et al., 2008). However, the allocation of resources such as power and frequency has been regarded as one of the effective ways to improve the capacity of wireless communication networks. In a cooperative Orthogonal frequency-division multiplexing access (OFDMA) system, the subchannel and power allocation (SPA) determines the throughput of the networks. Meanwhile, as the growth of cognitive radio networks, there is intense interest in systems built of multiple levels, in which interference exists between subsystems. Most of the recent works on cognitive radio networks are about spectrum sensing, i.e., spectrum interference avoidance. Researchers rarely consider power interference between primary and secondary users. However, the scarcity of the radio spectrum and exponential growth in the demand for wireless services have motivated researchers to develop new technologies for wireless and mobile communication to provide a ubiquitous, efficient, and seamless connectivity (Sendonaris et al., 2003). Considering the limited bandwidth availability, accommodating the demand for higher capacity and data rates is a challenging task, requiring innovative technologies that can offer new ways of exploiting the available radio spectrum (Mustafa et al., 2008).
There are various techniques to detect the spectrum which include energy, matched filter, transmission, and spectral correlation. After the frequency is sensed, the spectrum management requires the allocation of various parameters such as transmited power and proper subcarrier OFDM-orthogonal frequency division multiplexing is a multi-carrier modulation technique in which orthogonal subcarriers modulates chunk of data. OFDM then has the advantage of high bit data rate and also reduces inter-symbol interference (ISI). OFDM provides spectra efficiency and is very flexible and adaptive (Chowdhury and Akyildiz, 2008). MIMO seems to be a new technology developed in the physical layer and it has several advantages since it uses many antennas and follows enhanced signal processing techniques. Dynamic spectrum access ensures that the user (SU) doesn’t interfere with the licensed user (PU). The shortage of resources in the frequency spectrum can be overcome by adapting CRN technology. Cognitive radio is an exciting technology that offers new approaches to spectrum usage and efficiency. (Hou, et al., 2008). Cognitive radio is a novel concept for future wireless communications, and it has been gaining significant interest in academia, industry, and regulatory bodies. It provides a tempting solution to spectral crowding problem by introducing the opportunistic usage of frequency bands that are not heavily occupied by their licensed users (Chowdhury and Akyildiz, 2008). The cognitive radio concept proposes to furnish the radio systems with the abilities to measure and be aware of parameters related to the radio channel characteristics, availability of spectrum and power, interference and noise temperature, available networks, nodes, and infrastructures. An interconnected set of cognitive radio devices that share information is defined as a Cognitive Radio Network (CRN) (Aleksandar, et al., 2010). To the dynamic radio environment, a cognitive radio (CR) transceiver is ready to adapt for the restricted radio resources and the network parameters to maximize the employment whereas providing flexibility in wireless access. For the radio environment (in terms of spectrum usage, power spectral density of transmitted /received signals, wireless protocol signaling) and intelligence is the key options of a CR transceiver and awareness. For adaptive standardization of system parameters like transmit power, carrier frequency, and modulation strategy (at the physical layer), and higher-layer protocol parameters this intelligence is achieved through learning. The development of cognitive radio technology should upset technical and sensible concerns (which are extremely multidisciplinary) still as regulative needs. There’s an increasing interest in this technology among the researchers in each domain and business and therefore the spectrum policy manufacturers. The key enabling techniques for cognitive radio networks (also cited as dynamic spectrum access networks) are broadband signal process techniques for digital radio, advanced wireless communications ways, artificial intelligence and machine learning techniques, and cognitive radio-aware adaptive wireless/mobile networking protocols (Krishnamurthy et al., 2013) to meet user desires from its experiences to reason, plan, and choose future actions. Cognitive radio is an adaptive, multi-dimensionally autonomous radio system that learns. For using, permitting access to, or allocating spectrum Standards teams and regulating bodies around the world square measure progressively seeking new ways to improve on channel allocation. The SDR Forum’s world regulative Summit on SDR and cognitive Radio Technologies from around the world mentioned their spectrum management challenges and goals, and therefore the role of recent technologies.. This interest in developing new spectrum utilization technologies combined and for new and promising technologies like cognitive radio, the conclusion that machine learning will be applied to radios are making intriguing prospects. However, deficient resources with spectrum turning into, as expeditiously as doable it's fastidious that new systems utilize all accessible frequency bands. Spectrum a lot of dynamically is allocates by Dynamic spectrum access and for analysis, it's a vigorous space. Not solely advances in technology however additionally new policy DSA is extremely vital and for spectrum, use could be an economic model. (Senhua, et al., 2008). Cognitive radios are widely viewed because of the technology that may radically ameliorate each spectrum potency and utilization. Cognitive radios are totally programmable wireless devices that may sense their environment and dynamically change their transmission waveform, channel access methodology, spectrum use, and networking protocols as required permanently by the network and application performance. For addressing the number of analysis challenges of mixing the DSA and cognitive radio conferred by analysis community (Jafar and Srinivasa, 2009). Cognitive Radio Networks is aimed at performing cognitive operations such as sensing the spectrum, managing available resources, and making user-independent, intelligent decisions based on the cooperation of multiple cognitive nodes. In order to be able to achieve the goals of the cognitive radio concept, an improved and more reliable resource allocation scheme for cognitive radio networks need to be developed (Ishibashi et al., 2008). This section covers the broader aspects of the research topic. It has highlighted the basic aspects of Cognitive Radio Networks. The problem statement is given to clarify the scope of the project.
1.2 PROBLEM STATEMENT
Spectrum scarcity in wireless communication is mainly caused by the static license spectrum policy rather than physical shortage (Senhua, et al 2008). This have led to poor spectrum utilization efficiency of the radio network (Swara and Varum, 2011). A novel approach, known as cognitive radio network (CRN) was introduced (Mitola, 2015) to improve radio spectrum utilization efficiency but has been limited to self-interference among CR users (Sekchin et al, 2014). Many practical limitations such as imperfect spectrum sensing, limited transmission power, etc. exist in CRN (Shaowei, 2010). Another challenge encountered by the CRN network is the problem of data corruption, low data rate and poor system capacity (Remika et al.,2012). However, the static spectrum allocation mechanism results in lots of free spectrum resources in space, and the spectrum is wasted. The cognitive radio technology with intelligent searching and efficient utilization of the idle spectrum resources just provides the opportunity to use the free spectrum resources. Despite the fact that significant advancements have been made in exploring and experimentally deploying some prototypes of CRN, there are still a number of open-ended problems that require adequate investigation. One of such problem of high significance is the designing methods for achieving the utmost in the allocation of the limited resources on which CRN usually have to build communication. It has already been well established that the amount of resources available for use in CRN is generally limited and that, the demands of users in CRN are usually large and diverse. Hence, unless adequate methods for efficiently utilizing the resources of CRN are devised and the limiting problems are addressed, it would be very difficult for CRN to achieve meaningful results.
1.3 AIM AND OBJECTIVES OF THE STUDY
The aim of this research is to develop improved resource allocation (RA) scheme and algorithm for cognitive radio network.
The objectives of this research include the following:
i. To Review In Literature The Current State Of The Art In Cognitive Radio Network And Achievement So Far.
ii. To Design An Efficient Resource Allocation (Ra) Algorithms For Cognitive Radio Network Using Cooperative Relay Technique For Better Efficient Utilization Of Radio Spectrum.
iii. To Develop An Efficient Ra Scheme To Allocate Sub-Channels, Powers, And Bits In Cognitive Radio (Cr) Systems By Minimizing The Bit Error Rate.
iv. To Control The Communication Processes Among Competing Users Through The Proper Allocation Of Available Resources Using Signal Processing Tool
v. To Compare The Developed Resource Allocation (Ra) Algorithms/scheme With The resent Existing Schemes/Algorithms.
1.4 SCOPE OF WORK
The work presented in this dissertation provides novel insights and design frameworks for the gainful application of RA into the wireless communication systems, from the aspects of radio resource allocation and optimization which is focused on the development of an improved model for power and subchannel allocation scheme. The study involves the design and development of an improved cognitive radio network scheme and an algorithm for implementation of the model with more emphasis laid on spectrum sensing, identification, spread spectrum techniques which shall involve spectrum capacity, resource allocation and resource management.
On one hand, the simultaneous transmission and reception at the same channel appear as a promising communication paradigm with the potential to obtain a higher level of spectral efficiency and security. On the other hand, the co-existence of multiple transmissions at the same channel appears as a potential threat to the performance of wireless systems, if the impact of the additional interference paths is not properly controlled. This calls for smart resource and interference management schemes, in order to avoid the negative impacts of the additional interference. In this thesis, optimized allocation of the fundamental communication resources, i.e., power, spectrum, antenna and radio front ends, have been studied for different promising scenarios regarding the application of CRNs.
1.5 SIGNIFICANCE OF THE STUDY
Allocation of resources such as power and frequency has been regarded as one of the effective ways to improve the capacity of wireless communication networks. The research work will offer better efficient utilization of the frequency spectrum and more wireless device applications. The research will also provide a more efficient and reliable network for future research studies. Following the unique challenge imposed on CRNs due to their coexistence with primary networks is mainly related to spectrum management functionalities such as spectrum sensing, analyzing, sharing, and spectrum mobility. The spectrum-sensing mechanism in cognitive radios should find answers to the following issues: where to sense, what to sense, how to sense, how long to sense and allowable interference limits. Once spectrum sensing is completed, a CR should take a decision by analyzing the sensed information. Then, the spectrum management system should find answers to the spectrum-sharing specific questions such as how to access and how to allocate PUs’ idle bands. Nevertheless the spectrum management system should release the spectrum band being used by the CRs upon re-appearance of a PU in that spectrum band. Also, cognitive-communication should be shifted to another band if there are idle spectrum bands available. At this point, solutions to the spectrum-mobility-specific issues such as how to stop CR transmission, where to shift and how to change configuration parameters are of significant importance for smooth operation. Optimizing spectrum management issues in such a way that mitigates PU interference while improving cognitive throughput is not an easy task.
The resource allocation scheme in a CRN is the entity that is responsible for avoiding harmful interference caused to the PUs while optimally utilizing the available resources (i.e., power and spectrum). For this, the resource allocation schemes need to consider the effect of PU activities in different bands. Therefore, the resource allocation schemes used in legacy wireless networks are not suitable for CRNs. Also, different types of resource allocation methods will be required based on the CRN architecture (i.e., centralized or distributed CRN) or the type of operation mode of the CRN (e.g., overlay mode or underlay mode). While power control is a crucial component of resource allocation for CRNs operating in the underlying mode (i.e., mode in which PUs and CRs can access a spectrum band simultaneously), it is not that crucial in case of overlay mode (i.e., mode in which PUs and CRs do not access a spectrum band simultaneously).
1.6 THESIS OUTLINE
The remaining chapters of this thesis are structured as follows:
Chapter 2: Literature Review. Introduce background information, as a fundamental tool in radio resource management and exploring the various techniques for solving the RA problems in CRN while considering and incorporating the identified limitations in existing models in past works.
Chapter 3:Research Methodology. Development of an improved model, Algorithms and Procedures (flow chart). Describe the methods which we use for the proposed new algorithms. Present the proposed algorithms for the RA scheme. Illustrate the proposed algorithms in detail.
Chapter 4: Numerical simulation and results of the power and sub-channel resource allocation. Compare the proposed simulation results with the related work results.
Chapter 5: Conclusion, Recommendation, and Contributions to Knowledge. Conclude the thesis, and propose future work for improvement
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