Performance Assessment of Massive MIMO liu.diva- 1117917/   of Massive MIMO Systems

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  • Master of Science Thesis in Electrical EngineeringDepartment of Electrical Engineering, Linkping University, 2017

    Performance Assessmentof Massive MIMO Systemsfor Positioning and Trackingof Vehicles in OpenHighways

    Markus Petersson

  • Master of Science Thesis in Electrical Engineering

    Performance Assessment of Massive MIMO Systems for Positioning andTracking of Vehicles in Open Highways

    Markus Petersson

    LiTH-ISY-EX--17/5049--SE

    Supervisors: Lic. Christopher MollnISY, Linkpings universitet

    Dr. Mrio CostaHuawei Technologies

    Examiner: Prof. Erik G. LarssonISY, Linkpings universitet

    Division of Communication SystemsDepartment of Electrical Engineering

    Linkping UniversitySE-581 83 Linkping, Sweden

    Copyright c 2017 Markus Petersson

  • Till min farfar Erik,min morfar Bertil

    och min gudfar Nils

  • AbstractThe next generation of mobile networks (5G) is currently being standardized, and massiveMIMO (Multiple-Input-Multiple-Output) is a strong candidate to be part of this standard.Other than providing higher data rates and lower latency, high accuracy positioning is alsorequired. In this thesis, we evaluate the achievable performance of positioning using mas-sive MIMO systems in open highway scenarios. Relevant theory from sensor array signalprocessing and Bayesian filtering is presented, and is used in a simulation environmenton large antenna arrays representing massive MIMO base stations. Positioning is done byutilizing the uplink pilot reference signals, where the Direction of Arrival (DOA) of thepilot signal is estimated, and then used for position estimation. Estimation of the DOAis done by both a maximum-likelihood method and by using an Extended Kalman Filter(EKF). A positioning error of less than 8 m is achieved with absolute certainty when thevehicle is less than 300 m from the base station. It is also concluded that this result couldbe improved by using more sophisticated filtering algorithms.

    v

  • AcknowledgmentsThis thesis concludes my five years at Linkping University, and what a ride it has been.The completion of this thesis calls for acknowledgement of some of the people that hasmade it possible.

    First and foremost I would like to thank Dr. Mrio Costa, my supervisor from HuaweiTechnologies, for giving me the opportunity to do this thesis work, for always havinganswers to my questions, and for always being positive and supportive. I would also liketo express my sincere gratitude to Lic. Christopher Molln, my supervisor at the Divisionof Communication Systems, for always having an open door, encouraging discussion,and for his precise and honest comments on my work. I am also indebted to my examinerProf. Erik G. Larsson, whose encouragement, involvement, and sharp eye has been bothinspirational and helpful. I am especially grateful for the discussions we had regardingnuisance parameters and the Cramr-Rao Bound. I would like to thank Ema Becirovic,with whom I have shared an office, for the countless coffee-breaks, for her help with thesmall things regarding the thesis, and for at times taking my mind off it. Furthermore, Iwould also like to thank everyone in the Division of Communication Systems for all beingnice and helpful.

    I would also like to take the opportunity of thanking all the people I have met and all thefriends I have made during my five years in Linkping. It is you who have made thesesome of the best, and some of the most memorable years of my life.

    Finally, I would like to thank my immediate family for always believing in me and foralways supporting me. For this I will always be grateful. You have had a bigger part thanyou think in the making of this thesis.

    Linkping, June 2017Markus Petersson

    vii

  • Contents

    Notation xi

    1 Introduction 11.1 Future Cellular Communication . . . . . . . . . . . . . . . . . . . . . 11.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Novel Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.5 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

    2 Theory 52.1 Sensor Array Signal Processing . . . . . . . . . . . . . . . . . . . . . . 5

    2.1.1 Modeling the Sensor Array Output . . . . . . . . . . . . . . . . 52.1.2 Parametrization of the Steering Vector by DOAs Under the Geo-

    metric Far-Field Assumption . . . . . . . . . . . . . . . . . . . 72.1.3 Wavefield Modeling . . . . . . . . . . . . . . . . . . . . . . . 72.1.4 Array Manifold Separation Using a 2-D Fourier Basis . . . . . 9

    2.2 Classical Estimation Theory . . . . . . . . . . . . . . . . . . . . . . . 102.2.1 Maximum-Likelihood Estimation . . . . . . . . . . . . . . . . 112.2.2 Deterministic Maximum-Likelihood Estimation . . . . . . . . . 112.2.3 The Cramr-Rao Bound . . . . . . . . . . . . . . . . . . . . . 142.2.4 Derivation of the CRB for the Used Array Model . . . . . . . . 15

    2.3 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.3.1 The Grid Search Method . . . . . . . . . . . . . . . . . . . . . 172.3.2 The Steepest Descent Algorithm . . . . . . . . . . . . . . . . . 172.3.3 The Newton Algorithm . . . . . . . . . . . . . . . . . . . . . . 182.3.4 The Fisher-Scoring Method . . . . . . . . . . . . . . . . . . . 192.3.5 The Levenberg-Marquardt Method . . . . . . . . . . . . . . . . 19

    2.4 Bayesian Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.4.1 The Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . 202.4.2 The Extended Kalman Filter . . . . . . . . . . . . . . . . . . . 212.4.3 An Alternative Form of the EKF . . . . . . . . . . . . . . . . . 212.4.4 Motion Models . . . . . . . . . . . . . . . . . . . . . . . . . . 22

    ix

  • x Contents

    3 Simulation Setup 253.1 The Madrid Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.2 Base Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.3 User Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.4 Transmission of Pilot Reference Signals . . . . . . . . . . . . . . . . . 263.5 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

    4 Simulations 294.1 Performance of the Derived DML Estimator . . . . . . . . . . . . . . . 29

    4.1.1 Uniform Cylindrical Array . . . . . . . . . . . . . . . . . . . . 294.1.2 Uniform Rectangular Array . . . . . . . . . . . . . . . . . . . 304.1.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

    4.2 DOA Estimation, Position Estimation and Tracking on the Madrid Grid 324.2.1 Performance with Different Number of Antennas . . . . . . . . 354.2.2 Performance with Different Transmit Powers . . . . . . . . . . 364.2.3 Performance of Filtering with Different Update Intervals . . . . 374.2.4 Performance of Filtering and Batch Estimation . . . . . . . . . 39

    5 Conclusion 455.1 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 455.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

    A Simulation Plots 49

    Bibliography 61

  • Notation

    SETS

    Notation MeaningR The set of real numbersC The set of complex numbers

    Cnm The set of complex nm matrices The set of parameters that parameterize the steering vector The set of nuisance parameters The union of and

    MATHEMATICAL NOTATION

    Notation Meaning( ) The Moore-Penrose Pseudo InverseH A Hilbert space

    ( )H The Hermitian transpose[v]n The n-th element of a vector v

    [M ]n,m The (n,m)-th element of a matrixM( )T Transpose

  • xii Notation

    ABBREVIATIONS

    Abbreviation MeaningDOA Direction of Arrival

    MIMO Multiple Input Multiple OutputML Maximum Likelihood

    DML Deterministic Maximum LikelihoodSML Stochastic Maximum LikelihoodKF Kalman Filter

    EKF Extended Kalman FilterTTI Transmission Time IntervalUE User EquipmentFIM Fisher Information MatrixCRB Cramr-Rao BoundMST Manifold Separation TechniquePDF Probability Density FunctionMSE Mean Square Error

    RMSE Root Mean Square ErrorCCDF Complementary Cumulative Distribution FunctionSNR Signal-to-Noise-RatioLTE (4G) Long Term Evolution

  • 1Introduction

    This thesis is about how a massive MIMO base station can be used to estimate and trackthe position of a single user in an open highway. The concept of massive MIMO is brieflydescribed in this chapter, where the specific questions we answer are also given. Further-more, we compare the contributions of this thesis to earlier work.

    1.1 Future Cellular Communication

    In the last five years, the amount of mobile data traffic has grown eighteen-fold and, in2016 alone, Global mobile data traffic grew by 63%. Hundreds of millions of new mobiledevices and connections are yearly added, and the amount of global mobile data traffic isprojected to increase from 7.2 exabytes per month in 2016 to 49 exabytes per month in2021 [5].

    The new generation of mobile networks (5G) is currently being standardized in order tomeet the quite remarkable goal in increased mobile data-traffic. Massive MIMO is oneof the technologies competing to be the technical enabler of 5G. The concept of massiveMIMO was proposed in [15], where the author considers a base station with an infinitenumber of antennas. Later, research has shown that massive MIMO requires neither aninfinite number of antennas, nor expensive high-end hardware to achieve its benefits [2].This makes massive MIMO viable in practice. Researchers at Lund University and theUniversity of Bristol have together constructed a real-time testbed for massive MIMO,which has displayed both great throughput and low latency [22], making a

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