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Evaluation of a school-based, teacher-delivered psychological intervention group program for trauma-affected Syrian refugee children in Istanbul, Turkey
No full textObjective: The purpose of this study was to evaluate an innovative, protocol-based, group cognitive behavioral therapy (CBT) program delivered by trained teachers to reduce emotional distress and improve psychological functioning among the war-traumatized Syrian refugee students living in Istanbul. Methods: A total of 32 participants, aged between 10 and 15 years (mean=12.41, SD=1.68) and mostly females (m/f=12/20) were randomly selected from a sample of 113 refugee students based on their trauma-related psychopathology as reflected in the Child PostTraumatic Stress – Reaction Index (CPTS-RI) total score. The treatment program was implemented by the teachers trained by the study team to deliver a weekly, eight-session, protocol-based intervention in school setting. The degree of the fidelity to the original program was tested via video-recordings and subsequent analyses of the sessions. Effectiveness of the intervention was evaluated by a pre-test/post-test comparison using the CPTS-RI, Spence Children’s Anxiety Scale (SCAS), and Strengths and Difficulties Questionnaire (SDQ). Results: All participants were accompanied minors. A significant proportion of them had either witnessed or been personally exposed to traumatic events. Statistically significant reduction in post-intervention evaluation was observed in the SCAS total score (t=3.73, p=0.001); CPTS-RI total score (t=2.72, p=0.011) and in the intrusive (t=3.88, p=0.001) and arousal (t=2.60, p= 0.015) symptoms of the post-traumatic stress disorder (PTSD). In line with improvement in emotional problems as revealed in the anxiety and PTSD scales, the SDQ subcategory of the emotional problems was the only symptom area that showed a significant improvement (t= 2.85, p=0.008). No significant change was seen in the SDQ subcategories of conduct (t= 1.01, p=0.32), hyperactivity (t=1.30, p=0.20), peer problems (t=.66, p=0.51), or in prosocial behavior (t=2.15, p=0.039). A significant proportion of the participants did no longer meet the diagnostic threshold for anxiety (p=0.001) and PTSD (p=0.021) after completion of the intervention. However, the post-intervention SDQ subcategories and the total SDQ score showed no significant difference as compared with the pre-intervention group. Conclusions: To the best of our knowledge, this is the first interventional study reporting promising results from a school-based, teacher-led and culturally sensitive psychological intervention program for refugee children in Turkey. Such protocol-based interventions need to be examined in controlled designs and larger samples so that a well-established intervention can be created and disseminated to provide the psychosocial support for this vulnerable and traumatized population
Eskimeyen İstanbul
No full textUnutma İstanbul projesinin bir parçası olarak İstanbul Şehir Üniversitesi ile Türkiye Turing ve Otomobil Kurumu (TURİNG) işbirliği kapsamında Sümeyye Koyunbakan tarafından çekilmiştir.İstanbul Kalkınma Ajansı (TR10/16/YNY/0101) İstanbul Development Agency (TR10/16/YNY/0101
Kuş ölür sen uçuşu hatırla
No full textUnutma İstanbul projesinin bir parçası olarak İstanbul Şehir Üniversitesi ile Türkiye Turing ve Otomobil Kurumu (TURİNG) işbirliği kapsamında Büşra Sürücü tarafından çekilmiştir.Unutma İstanbul projesi İstanbul Kalkınma Ajansı'nın 2016 yılı "Yenilikçi ve Yaratıcı İstanbul Mali Destek Programı" kapsamında desteklenmiştir. Proje No: TR10/16/YNY/010
İstanbul'da örgülü bir kız
No full textUnutma İstanbul projesinin bir parçası olarak İstanbul Şehir Üniversitesi ile Türkiye Turing ve Otomobil Kurumu (TURİNG) işbirliği kapsamında Yurdagül Aydın tarafından çekilmiştir.Unutma İstanbul projesi İstanbul Kalkınma Ajansı'nın 2016 yılı "Yenilikçi ve Yaratıcı İstanbul Mali Destek Programı" kapsamında desteklenmiştir. Proje No: TR10/16/YNY/010
Bir kadın
No full textUnutma İstanbul projesinin bir parçası olarak İstanbul Şehir Üniversitesi ile Türkiye Turing ve Otomobil Kurumu (TURİNG) işbirliği kapsamında Yasemin Özben tarafından çekilmiştir.Unutma İstanbul projesi İstanbul Kalkınma Ajansı'nın 2016 yılı "Yenilikçi ve Yaratıcı İstanbul Mali Destek Programı" kapsamında desteklenmiştir. Proje No: TR10/16/YNY/010
Sandalda bir garip adam
No full textUnutma İstanbul projesinin bir parçası olarak İstanbul Şehir Üniversitesi ile Türkiye Turing ve Otomobil Kurumu (TURİNG) işbirliği kapsamında Kübra YEŞİLSOY tarafından çekilmiştir.Unutma İstanbul projesi İstanbul Kalkınma Ajansı'nın 2016 yılı "Yenilikçi ve Yaratıcı İstanbul Mali Destek Programı" kapsamında desteklenmiştir. Proje No: TR10/16/YNY/010
High gain, high bandwidth, wide ICMR, and highly linear fully differential amplifier with large dynamic range and process corner configurable output stage
No full textTezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.Pre-ampifier is one of the most important building blocks in designing analog to digital converters (ADC). Most of the ADCs have a pre-amplifier implemented for driving the large input capacitance of the ADC or amplifying the weak naturally occurred input signals. One of the key factors in designing a pre-amplifier is its signal to noise and distortion ratio (SINAD) as the noise and distortion introduced by the pre-amplifier might degrade the effective number of bits (ENOB) of ADC.
This work introduces a high gain, high bandwidth, wide input common mode range (ICMR), and highly linear fully differential folded cascode pre-amplifier for a 14-bit analog to digital converter. The system uses native n-channel transistors as differential input pair to achieve a wide ICMR with minimum transconductance variation. The total harmonic distortion (THD) of the pre-amplifier is minimized over different process corners. A process corner configurable class AB output stage is implemented to provide a railtorailoutputsignal. Additionally, aprocesscornerconfigurablecompensationcircuit is implemented to ensure the stability of system over different process corners. The preamplifier achieves a closed-loop gain of 130dB, a unity gain bandwidth of 200MHz, a THD of−92.2dB, and a phase margin of 68◦. The pre-amplifier is designed using TSMC 180nm CMOS technology.Declaration of Authorship ii
Abstract iv
Öz v
Acknowledgments vii
List of Figures x
List of Tables xiii
Abbreviations xiv
1 Introduction 1
1.1 Thesis Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Thesis Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Fundamentals of Amplifier Design 5 2.1 Introduction to MOS Transistors . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Enhancement Mode MOSFET . . . . . . . . . . . . . . . . . . . . 6
2.1.2 Depletion Mode MOSFET . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Performance of CMOS Amplifiers . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.1 Frequency Response . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.2 Offset Voltage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.3 Common Mode Rejection Ratio . . . . . . . . . . . . . . . . . . . . 11
2.2.4 Power Supply Rejection Ratio . . . . . . . . . . . . . . . . . . . . . 12
2.2.5 Input Common Mode Range . . . . . . . . . . . . . . . . . . . . . 12
2.2.6 Output Voltage Swing Range . . . . . . . . . . . . . . . . . . . . . 13
2.2.7 Slew Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.8 Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.9 Linearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Topologies for Fully Differential Amplifier . . . . . . . . . . . . . . . . . . 16
2.3.1 Fully Differential Two Stage Amplifier . . . . . . . . . . . . . . . . 17
2.3.2 Fully Differential Folded Cascode Amplifier . . . . . . . . . . . . . 18
2.4 Frequency Compensation . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4.1 Pole Splitting Miller Compensation . . . . . . . . . . . . . . . . . . 19
2.4.2 Active Compensation . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 Common Mode Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.6 Output Stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.6.1 Class A Output Stage . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.6.2 Class B Output Stage . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.6.3 Class AB Output Stage . . . . . . . . . . . . . . . . . . . . . . . . 24
2.7 Gain Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.7.1 Normal Cascode Circuit . . . . . . . . . . . . . . . . . . . . . . . . 25
2.7.2 Regulated Cascode Circuit . . . . . . . . . . . . . . . . . . . . . . . 26
3 Literature Review 28
3.1 Complementary Input Pair . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Complementary Input Pair with Dummy Input . . . . . . . . . . . . . . . 31
3.3 Complementary Input Pair with Overlapped Regions . . . . . . . . . . . . 32
3.4 Dual n-channel Input Pair . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.5 Dual p-channel Input Pair . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4 Wide ICMR Fully Differential Amplifier Design 36
4.1 Proposed Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2 Design Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.3 Fully Differential Folded Cascode Amplifier . . . . . . . . . . . . . . . . . 39
4.3.1 Bias Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.3.2 Folded Cascode Amplifier with Gain Boosting . . . . . . . . . . . . 42
4.3.3 Common Mode Feedback . . . . . . . . . . . . . . . . . . . . . . . 44
4.4 Process Corner Configurable Class AB Output Stage . . . . . . . . . . . . 45
4.5 Process Corner Configurable Miller Compensation . . . . . . . . . . . . . 48
5 Simulation Results 50
5.1 Schematic Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.1.1 AC Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.1.2 Transconductance variation . . . . . . . . . . . . . . . . . . . . . . 52
5.1.3 Input Common Mode Range . . . . . . . . . . . . . . . . . . . . . 53
5.1.4 Common Mode Rejection Ratio . . . . . . . . . . . . . . . . . . . . 54
5.1.5 Power Supply Rejection Ratio . . . . . . . . . . . . . . . . . . . . . 55
5.1.6 Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.1.7 Output Voltage Swing Range . . . . . . . . . . . . . . . . . . . . . 57
5.1.8 Total Harmonic Distortion . . . . . . . . . . . . . . . . . . . . . . . 58
5.1.9 Transient Response . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.2 Temperature Simulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.3 Process Corner Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.3.1 AC Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.3.2 Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.3.3 Total Harmonic Distortion . . . . . . . . . . . . . . . . . . . . . . . 79
6 Conclusion and Future Work 84
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
A serious game with data analysis to diagnose and treat children with visual sequential memory decit
No full textTezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.Visual Sequential Memory (VSM) allows a person to perform tasks such as remembering letters, numbers, objects or shapes in the correct order. Its de cit can lead to challenges in one's personal life, including dyslexia and dyscalculia. Detecting Visual Sequential Memory De cit (VSMD) is essential for those who su er from its related consequences. But current clinical methods don't have a high rate of diagnosis, and also treatment is limited to the few hours the person spends in the clinic. In this thesis, we propose an Origami based Serious Game, called Memori, for the diagnosis and treatment of childrenwithVSMD.WeillustratetherationalebehindusingOrigami, thedesignprocess of our game, its implementation, data construction, data analysis methods and their comparison.
For this work, we chose 3D graphics to give the user a realistic feeling about the game environment. To be easily accessible on any device and platform, we chose WebGL since it supports 3D graphics on any web browser. For the analysis of the data gathered by our system, we compared di erent clustering methods to achieve better diagnosis of children with VSMD. Our preliminarily performance evaluations with 24 adults revealed a 13% improvement of memory and 1.00 score increase in performance while a slight decrease occurred in attentiveness from 2.33 to 2.02 for people who used our tool. We also experimented with children in 2 schools. They were exposed to two di erent methods of testing in diagnosis and treatment. The data collected from these two experiments was analyzed using 3 variant clustering methodologies in order to verify the performance of the game.Declaration of Authorship ii
Abstract iv
Öz v
Acknowledgments vii
List of Figures x
List of Tables xi
Abbreviations xii
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Research Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Related Work 5
2.1 Current Clinical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Serious Games for Visual Memory . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Serious Games for Dyslexia . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.4 Serious Games for Dyscalculia . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.5 Origami for VSM in Therapy and Education . . . . . . . . . . . . . . . . . 8
3 Background 9 3.1 Psychology Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1.1 The Knox Cube Imitation Test . . . . . . . . . . . . . . . . . . . . 9
3.1.2 The Corsi Block-Tapping Test . . . . . . . . . . . . . . . . . . . . . 9
3.2 Clustering Methods For Data Analysis . . . . . . . . . . . . . . . . . . . . 10
3.2.1 K-means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2.2 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2.3 Spectral Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Proposed Game 14
4.1 Game Play . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2 Game Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2.1 Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2.1.1 Strengths & Weaknesses . . . . . . . . . . . . . . . . . . . 16
4.2.2 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2.2.1 Paper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2.2.2 Folds Type . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2.2.3 Folding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Lattice Modi ers. . . . . . . . . . . . . . . . . . . . . . . . . 18
Armatures. . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2.2.4 Animation . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Lattice Modi ers. . . . . . . . . . . . . . . . . . . . . . . . . 22
Armatures. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.2.2.5 Material & Lighting . . . . . . . . . . . . . . . . . . . . . 23
4.2.2.6 Logic Editor . . . . . . . . . . . . . . . . . . . . . . . . . 24
5 Performance Evaluation 27 5.1 Preliminary Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5.1.1 The Test Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5.1.2 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5.1.3 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5.1.4 Experimentation Stage . . . . . . . . . . . . . . . . . . . . . . . . . 28
5.1.5 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
5.1.6 Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5.1.7 Experimental Results & Discussion . . . . . . . . . . . . . . . . . . 30
5.2 Experiments with Children . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.2.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.2.2 Experiment Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.2.3 Measurements & Results . . . . . . . . . . . . . . . . . . . . . . . . 34
5.2.3.1 Psychology Tests . . . . . . . . . . . . . . . . . . . . . . . 34
Knox Cube Imitation Test. . . . . . . . . . . . . . . . . . . 34
The Corsi Block Tapping Test. . . . . . . . . . . . . . . . . 34
5.2.3.2 Clustering Methods For Data Analysis . . . . . . . . . . . 36
K-means Clustering. . . . . . . . . . . . . . . . . . . . . . . 36
Hierarchical Clustering. . . . . . . . . . . . . . . . . . . . . 38
Spectral Clustering. . . . . . . . . . . . . . . . . . . . . . . . 38
5.3 Results Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.3.1 Clustering Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
6 Conclusion and Future Work 45 6.1 Areas in Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Bibliography 4
Joint estimation of direction of arrival with unknown mutual coupling in massive MIMO networks and LTE radio resource block allocation optimization in maritime channels
No full textTezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.The evolution of technology from one generation to other always brings a better user experiences in terms of high data rates and improved quality of service parameters like lowlatency. However,italsocomeswithitsownchallenges. Theupcoming5Gtechnology is one of those technologies that is now moving from theory to practical implementation with prototypes being developed all around the world. Massive MIMO is the key enabler for such 5G networks and one of the concerns with massive MIMO is the mutual coupling effect that causes wrong direction of arrival (DoA) estimations that leads to low capacity issues. In this thesis, several optimization techniques related to estimations of DoA and unknown mutual coupling in antenna arrays are studied and an extended joint iterative optimization with reduced rank method is proposed in that cause considering massive MIMO networks. The backbone of the work is based on joint iterative method with reduced rank matrix optimization, quadratic programming (QP), compressed sensing and L2 norms that are used to determine the DoAs and unknown mutual coupling with higher resolution capabilities. The proposed method is dynamic in nature and has very low complexity order giving it a big advantage over other methods. Furthermore, in absence of any 5G standards radio resource block allocation methods for LTE over sea are studied and a max-min optimization is proposed which is then compared with the previous resource allocation algorithms. The results of the proposed resource allocation method reflects the superiority of the algorithm in terms of fairness with variable load. In summary, this thesis shreds light into the application of convex optimization and linear algebra in wireless communication domain.Declaration of Authorship ii
Abstract iv
Öz v
Acknowledgments vii
List of Figures x
List of Tables xi
Abbreviations xii
Physical Constants xiii
Symbols xiv
1 Introduction to 5G Networks 1
1.1 Evolution of Cellular Technologies . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Massive MIMO for 5G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Motivation Behind the Work . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 DoA Estimation by Classical Methods in Massive MIMO 5
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Propagation Delay in Uniform Linear Arrays . . . . . . . . . . . . . . . . 6
2.3 Narrowband Approximation . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.4 Matrix Representation for Array Data . . . . . . . . . . . . . . . . . . . . 9
2.5 Antenna Beamforming Basics . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.6 Classical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.6.1 Delay and Sum Method . . . . . . . . . . . . . . . . . . . . . . . . 11
2.6.2 Capon’s Minimum Variance Distortionless Response Technique . . 11
3 DoA Estimation by Subspace Methods in Massive MIMO 13
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Multiple Signal Classification Algorithm or MUSIC . . . . . . . . . . . . . 13
3.3 Root MUSIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.4 Smooth MUSIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.5 The Minimum Norm Method . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.6 Estimation of Signal Parameters via Rotational Invariance Techniques or ESPRIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.7 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4 Joint DoA Estimation with Mutual Coupling in Massive MIMO 21
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 Mutual Coupling in Antenna Array . . . . . . . . . . . . . . . . . . . . . . 21
4.3 Mutual Coupling Matrices for Different Arrays . . . . . . . . . . . . . . . 24
4.3.1 Linear Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.3.2 Circular Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.4 Direction Finding in Presence of Direction Independent Mutual Coupling 25
4.4.1 Comparison and Simulation of DoA Algorithms in Absence and Presence of Mutual Coupling . . . . . . . . . . . . . . . . . . . . . 26
4.5 Joint Estimation of the DOAs and Unknown Mutual Coupling Matrix . . 27
4.5.1 Algorithm for Joint Estimation of DoA and Coupling Matrix . . . 28
4.5.2 Proposed Improvement in Resolution of the DoA Estimation using Convex Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.6 Joint Iterative Subspace Optimization with Rank Reduction to Estimate the DOAs and Unknown Mutual Coupling Matrix in Massive MIMO Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.6.1 Proposed Extended JIO . . . . . . . . . . . . . . . . . . . . . . . . 33
4.6.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5 LTE Radio Resource Block Allocation Optimization in Maritime Channels 37
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.2 LTE-SINR Path Loss Modelling in Sea Environment . . . . . . . . . . . . 39
5.3 LTE System Parameters and Problem Formulation . . . . . . . . . . . . . 41
5.3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.3.2 LTE System Parameters: . . . . . . . . . . . . . . . . . . . . . . . 42
5.3.3 Problem Formulation: . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.3.3.1 Max-min Problem Formulation . . . . . . . . . . . . . . . 43
5.3.3.2 Round Robin Method . . . . . . . . . . . . . . . . . . . . 44
5.3.3.3 Opportunistic Method . . . . . . . . . . . . . . . . . . . . 45
5.3.4 Performance Comparisons . . . . . . . . . . . . . . . . . . . . . . . 45
5.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
6 Conclusion and Future Work 49
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Bibliography 5
[Taha Toros'un eserlerinin kapak fotoğrafları]
No full textTaha Toros Arşivi, Dosya Adı: Taha Toros. Not: Unutma İstanbul projesinin bir parçası olarak İstanbul Şehir Üniversitesi çalışanlarından Doğucan Uslu tarafından çekilmiştir.Unutma İstanbul projesi İstanbul Kalkınma Ajansı'nın 2016 yılı "Yenilikçi ve Yaratıcı İstanbul Mali Destek Programı" kapsamında desteklenmiştir. Proje No: TR10/16/YNY/0101Unutma İstanbul projesi İstanbul Kalkınma Ajansı'nın 2016 yılı "Yenilikçi ve Yaratıcı İstanbul Mali Destek Programı" kapsamında desteklenmiştir. Proje No: TR10/16/YNY/010