ŞEHİR e-arşiv

Joint estimation of direction of arrival with unknown mutual coupling in massive MIMO networks and LTE radio resource block allocation optimization in maritime channels

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dc.contributor.advisor Özdemir, Mehmet Kemal
dc.contributor.author Kachroo, Amit
dc.date.accessioned 2017-12-12T13:08:31Z
dc.date.available 2017-12-12T13:08:31Z
dc.date.issued 2017-12-12
dc.date.submitted 2017-05-16
dc.identifier.uri http://hdl.handle.net/11498/47898
dc.description Tezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir. en_US
dc.description.abstract 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. en_US
dc.description.tableofcontents 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 51 en_US
dc.language.iso eng en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Convex Optimization en_US
dc.subject Optimization en_US
dc.subject Mathematical Optimization en_US
dc.subject Konveks Fonksiyonlar en_US
dc.subject Optimizasyon en_US
dc.subject Matematiksel Optimizasyon en_US
dc.title Joint estimation of direction of arrival with unknown mutual coupling in massive MIMO networks and LTE radio resource block allocation optimization in maritime channels en_US
dc.type Thesis en_US
dc.contributor.department Graduate School of Natural and Applied Sciences. Electronics and Computer Engineering. en_US


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