Massive MIMO: Fundamentals and System Designs .Massive MIMO: Fundamentals and System Designs

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  • Linkping Studies in Science and TechnologyDissertations, No. 1642

    Massive MIMO: Fundamentals and System


    Hien Quoc Ngo

    Division of Communication SystemsDepartment of Electrical Engineering (ISY)

    Linkping University, SE-581 83 Linkping,

    Linkping 2015

  • Massive MIMO: Fundamentals and System Designs

    c 2015 Hien Quoc Ngo, unless otherwise noted.

    ISBN 978-91-7519-147-8ISSN 0345-7524

    Printed in Sweden by LiU-Tryck, Linkping 2015

  • Cm n gia nh ti, cm n Em, v lun bn cnh ti.

  • Abstract

    The last ten years have seen a massive growth in the number of connected wire-less devices. Billions of devices are connected and managed by wireless networks.At the same time, each device needs a high throughput to support applicationssuch as voice, real-time video, movies, and games. Demands for wireless through-put and the number of wireless devices will always increase. In addition, there isa growing concern about energy consumption of wireless communication systems.Thus, future wireless systems have to satisfy three main requirements: i) having ahigh throughput; ii) simultaneously serving many users; and iii) having less energyconsumption. Massive multiple-input multiple-output (MIMO) technology, wherea base station (BS) equipped with very large number of antennas (collocated or dis-tributed) serves many users in the same time-frequency resource, can meet the aboverequirements, and hence, it is a promising candidate technology for next generationsof wireless systems. With massive antenna arrays at the BS, for most propagationenvironments, the channels become favorable, i.e., the channel vectors between theusers and the BS are (nearly) pairwisely orthogonal, and hence, linear processingis nearly optimal. A huge throughput and energy eciency can be achieved dueto the multiplexing gain and the array gain. In particular, with a simple powercontrol scheme, Massive MIMO can oer uniformly good service for all users. Inthis dissertation, we focus on the performance of Massive MIMO. The dissertationconsists of two main parts: fundamentals and system designs of Massive MIMO.

    In the rst part, we focus on fundamental limits of the system performance underpractical constraints such as low complexity processing, limited length of each coher-ence interval, intercell interference, and nite-dimensional channels. We rst studythe potential for power savings of the Massive MIMO uplink with maximum-ratiocombining (MRC), zero-forcing, and minimum mean-square error receivers, underperfect and imperfect channels. The energy and spectral eciency tradeo is inves-tigated. Secondly, we consider a physical channel model where the angular domainis divided into a nite number of distinct directions. A lower bound on the capacityis derived, and the eect of pilot contamination in this nite-dimensional channelmodel is analyzed. Finally, some aspects of favorable propagation in Massive MIMOunder Rayleigh fading and line-of-sight (LoS) channels are investigated. We showthat both Rayleigh fading and LoS environments oer favorable propagation.


  • In the second part, based on the fundamental analysis in the rst part, we pro-pose some system designs for Massive MIMO. The acquisition of channel stateinformation (CSI) is very important in Massive MIMO. Typically, the channelsare estimated at the BS through uplink training. Owing to the limited length ofthe coherence interval, the system performance is limited by pilot contamination.To reduce the pilot contamination eect, we propose an eigenvalue-decomposition-based scheme to estimate the channel directly from the received data. The pro-posed scheme results in better performance compared with the conventional train-ing schemes due to the reduced pilot contamination. Another important issue ofCSI acquisition in Massive MIMO is how to acquire CSI at the users. To addressthis issue, we propose two channel estimation schemes at the users: i) a downlinkbeamforming training scheme, and ii) a method for blind estimation of the ef-fective downlink channel gains. In both schemes, the channel estimation overheadis independent of the number of BS antennas. We also derive the optimal pilotand data powers as well as the training duration allocation to maximize the sumspectral eciency of the Massive MIMO uplink with MRC receivers, for a giventotal energy budget spent in a coherence interval. Finally, applications of MassiveMIMO in relay channels are proposed and analyzed. Specically, we consider multi-pair relaying systems where many sources simultaneously communicate with manydestinations in the same time-frequency resource with the help of a Massive MIMOrelay. A Massive MIMO relay is equipped with many collocated or distributed an-tennas. We consider dierent duplexing modes (full-duplex and half-duplex) anddierent relaying protocols (amplify-and-forward, decode-and-forward, two-way re-laying, and one-way relaying) at the relay. The potential benets of massive MIMOtechnology in these relaying systems are explored in terms of spectral eciency andpower eciency.

  • Populrvetenskaplig


    Det har skett en massiv tillvxt av antalet trdlst kommunicerande enheter desenaste tio ren. Idag r miljarder av enheter anslutna och styrda ver trdlsantverk. Samtidigt krver varje enhet en hg datatakt fr att stdja sina app-likationer, som rstkommunikation, realtidsvideo, lm och spel. Efterfrgan ptrdls datatakt och antalet trdlsa enheter kommer alltid att tillta. Samtidigtkan inte strmfrbrukningen hos de trdlsa kommunikationssystemen tilltas attka. Sledes mste framtida trdlsa kommunikationssystem uppfylla tre huvud-krav: i) hg datatakt ii) kunna betjna mnga anvndare samtidigt iii) lgre strm-frbrukning.

    Massiv MIMO (multiple-input multiple output), en teknik dr basstationen rutrustad med ett stort antal antenner och samtidigt betjnar mnga anvndare versamma tid-frekvensresurs, kan uppfylla ovanstende krav. Fljaktligen kan det be-traktas som en lovande kandidat fr nsta generations trdlsa system. Fr de estautbredningsmiljer blir kanalen frdelaktig med en massiv antennuppstllning (enuppstllning av, lt sga, hundra antenner eller er), det vill sga kanalvektorernamellan anvndare och basstation blir (nstan) parvis ortogonala, vilket gr linjrsignalbehandling nstan optimal. Den hga datatakten och lga strmfrbruknin-gen kan stadkommas tack vare multiplexeringsvinsten och antennfrstrkningen. Isynnerhet kan massiv MIMO erbjuda en likformigt bra betjning av alla anvndaremed en enkel eektallokeringsmetod.

    I denna avhandling brjar vi med att fokusera p grunderna av massiv MIMO.Speciellt kommer vi att studera de grundlggande begrnsningarna av systemetsprestanda i termer av spektral eektivitet och energieektivitet nr massiva an-tennuppstllningar anvnds. Detta kommer vi att gra med beaktande av prak-tiska begrnsningar hos systemet, som lgkomplexitetsbehandling (till exempel lin-jr behandling av signaler), begrnsad lngd av varje koherensinterval, ofullstndigkanalknnedom, intercell-interferens och ndlig-dimensionella kanaler. Dessutomundersks ngra aspekter hos frdelaktig utbredning i massiv MIMO med rayleigh-fdning och kanaler med rakt sikt. Baserat p dessa grundlggande analyser freslrvi sedan ngra systemkonstruktioner fr massiv MIMO. Mer precist freslr vi ngra


  • metoder fr kanalskattning bde fr basstationen och fr anvndarna, vilka mnarminimera eekten av pilotkontaminering och kanalovisshet. Den optimala pilot-och dataeekten s vl som valet av lngden av trningsperioden studeras. Till slutfresls och analyseras anvndandet av massiv MIMO i relkanaler.

  • Acknowledgments

    I would like to extend my sincere thanks to my supervisor, Prof. Erik G. Larsson,for his valuable support and supervision. His advice, guidance, encouragement, andinspiration have been invaluable over the years. Prof. Larsson always keeps an openmind in every academic discussion. I admire his critical eye for important researchtopics. I still remember when I began my doctoral studies, Prof. Larsson showedme the rst paper on Massive MIMO and stimulated my interest for this topic. Thisthesis would not have been completed without his guidance and support.

    I would like to thank Dr. Thomas L. Marzetta at Bell Laboratories, Alcatel-Lucent,USA, for his cooperative work, and for giving me a great opportunity to join hisresearch group as a visiting scholar. It has been a great privilege to be a part ofhis research team. He gave me valuable help whenever I asked for assistance. Ihave learnt many useful things from him. I would also like to thank Dr. AlexeiAshikhmin and Dr. Hong Yang for making my visit at Bell Laboratories, Alcatel-Lucent in Murray Hill such a great experience.

    I was lucky to meet many experts in the eld. I am thankful to Dr.Michail Matthaiou at Queen's University Belfast, U.K., for his great cooperation.I have learnt a lot from his maturity and expertise. Many thanks to Dr. Trung Q.Duong at Queen's University Belfast, U.K., and Dr. Himal A. Suraweera at Univer-sity of Peradeniya, Sri Lanka, for both technical and non-technical issues during thecooperative work. I would like to thank Dr. Le-Nam Tran at Maynooth University,Ireland, for his explanations and discussions on the optimization problems whichhelped me a lot. I am also thankful to all of my co-authors for the collaboration overthese years: Dr. G. C. Alexandropoulos (France Research Center, Huawei Technolo-gies Co. Ltd.), Prof. H-J. Zepernick (Blekinge Institute of Technology, Sweden),Dr. C. Yuen (Singapore University of Technology and Design, Singapore), Dr. A.K. Papazafeiropoulos (Imperial College, U.K.), Dr. H. Phan (University of Read-ing, U.K.), Dr. M. Elkashlan (Queen Mary University of London, U.K.), and Mr.L. Wang (Queen Mary University of London, U.K.).

    The warmest thank