희소인지를 이용한 전송기술 연구

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 심병효.The new wave of the technology revolution, named the fifth wireless systems, is changing our daily life dramatically. These days, unprecedented services and applications such as driverless vehicles and drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, and artificial intelligence-based personalized assistants are emerging. Communication mechanisms associated with these new applications and services are way different from traditional communications in terms of latency, energy efficiency, reliability, flexibility, and connection density. Since the current radio access mechanism cannot support these diverse services and applications, a new approach to deal with these relentless changes should be introduced. This compressed sensing (CS) paradigm is very attractive alternative to the conventional information processing operations including sampling, sensing, compression, estimation, and detection. To apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. In the last decade, CS techniques have spread rapidly in many applications such as medical imaging, machine learning, radar detection, seismology, computer science, statistics, and many others. Also, various wireless communication applications exploiting the sparsity of a target signal have been studied. Notable examples include channel estimation, interference cancellation, angle estimation, spectrum sensing, and symbol detection. The distinct feature of this work, in contrast to the conventional approaches exploiting naturally acquired sparsity, is to exploit intentionally designed sparsity to improve the quality of the communication systems. In the first part of the dissertation, we study the mapping data information into the sparse signal in downlink systems. We propose an approach, called sparse vector coding (SVC), suited for the short packet transmission. In SVC, since the data information is mapped to the position of sparse vector, whole data packet can be decoded by idenitifying nonzero positions of the sparse vector. From our simulations, we show that the packet error rate of SVC outperforms the conventional channel coding schemes at the URLLC regime. Moreover, we discuss the SVC transmission for the massive MTC access by overlapping multiple SVC-based packets into the same resources. Using the spare vector overlapping and multiuser CS decoding scheme, SVC-based transmission provides robustness against the co-channel interference and also provide comparable performance than other non-orthogonal multiple access (NOMA) schemes. By using the fact that SVC only identifies the support of sparse vector, we extend the SVC transmission without pilot transmission, called pilot-less SVC. Instead of using the support, we further exploit the magnitude of sparse vector for delivering additional information. This scheme is referred to as enhanced SVC. The key idea behind the proposed E-SVC transmission scheme is to transform the small information into a sparse vector and map the side-information into a magnitude of the sparse vector. Metaphorically, E-SVC can be thought as a standing a few poles to the empty table. As long as the number of poles is small enough and the measurements contains enough information to find out the marked cell positions, accurate recovery of E-SVC packet can be guaranteed. In the second part of this dissertation, we turn our attention to make sparsification of the non-sparse signal, especially for the pilot transmission and channel estimation. Unlike the conventional scheme where the pilot signal is transmitted without modification, the pilot signals are sent after the beamforming in the proposed technique. This work is motivated by the observation that the pilot overhead must scale linearly with the number of taps in CIR vector and the number of transmit antennas so that the conventional pilot transmission is not an appropriate option for the IoT devices. Primary goal of the proposed scheme is to minimize the nonzero entries of a time-domain channel vector by the help of multiple antennas at the basestation. To do so, we apply the time-domain sparse precoding, where each precoded channel propagates via fewer tap than the original channel vector. The received channel vector of beamformed pilots can be jointly estimated by the sparse recovery algorithm.5세대 무선통신 시스템의 새로운 기술 혁신은 무인 차량 및 항공기, 스마트 도시 및 공장, 원격 의료 진단 및 수술, 인공 지능 기반 맟춤형 지원과 같은 전례 없는 서비스 및 응용프로그램으로 부상하고 있다. 이러한 새로운 애플리케이션 및 서비스와 관련된 통신 방식은 대기 시간, 에너지 효율성, 신뢰성, 유연성 및 연결 밀도 측면에서 기존 통신과 매우 다르다. 현재의 무선 액세스 방식을 비롯한 종래의 접근법은 이러한 요구 사항을 만족할 수 없기 때문에 최근에 sparse processing과 같은 새로운 접근 방법이 연구되고 있다. 이 새로운 접근 방법은 표본 추출, 감지, 압축, 평가 및 탐지를 포함한 기존의 정보 처리에 대한 효율적인 대체기술로 활용되고 있다. 지난 10년 동안 compressed sensing (CS)기법은 의료영상, 기계학습, 탐지, 컴퓨터 과학, 통계 및 기타 여러 분야에서 빠르게 확산되었다. 또한, 신호의 희소성(sparsity)를 이용하는 CS 기법은 다양한 무선 통신이 연구되었다. 주목할만한 예로는 채널 추정, 간섭 제거, 각도 추정, 및 스펙트럼 감지가 있으며 현재까지 연구는 주어진 신호가 가지고 있는 본래의 희소성에 주목하였으나 본 논문에서는 기존의 접근 방법과 달리 인위적으로 설계된 희소성을 이용하여 통신 시스템의 성능을 향상시키는 방법을 제안한다. 우선 본 논문은 다운링크 전송에서 희소 신호 매핑을 통한 데이터 전송 방법을 제안하며 짧은 패킷 (short packet) 전송에 적합한 CS 접근법을 활용하는 기술을 제안한다. 제안하는 기술인 희소벡터코딩 (sparse vector coding, SVC)은 데이터 정보가 인공적인 희소벡터의 nonzero element의 위치에 매핑하여 전송된 데이터 패킷은 희소벡터의 0이 아닌 위치를 식별함으로 원신호 복원이 가능하다. 분석과 시뮬레이션을 통해 제안하는 SVC 기법의 패킷 오류률은 ultra-reliable and low latency communications (URLLC) 서비스를 지원을 위해 사용되는 채널코딩방식보다 우수한 성능을 보여준다. 또한, 본 논문은 SVC기술을 다음의 세가지 영역으로 확장하였다. 첫째로, 여러 개의 SVC 기반 패킷을 동일한 자원에 겹치게 전송함으로 상향링크에서 대규모 전송을 지원하는 방법을 제안한다. 중첩된 희소벡터를 다중사용자 CS 디코딩 방식을 사용하여 채널 간섭에 강인한 성능을 제공하고 비직교 다중 접속 (NOMA) 방식과 유사한 성능을 제공한다. 둘째로, SVC 기술이 희소 벡터의 support만을 식별한다는 사실을 이용하여 파일럿 전송이 필요없는 pilotless-SVC 전송 방법을 제안한다. 채널 정보가 없는 경우에도 희소 벡터의 support의 크기는 채널의 크기에 비례하기 때문에 pilot없이 복원이 가능하다. 셋째로, 희소벡터의 support의 크기에 추가 정보를 전송함으로 복원 성능을 향상 시키는 enhanced SVC (E-SVC)를 제안한다. 제안된 E-SVC 전송 방식의 핵심 아디디어는 짧은 패킷을 전송되는 정보를 희소 벡터로 변환하고 정보 복원을 보조하는 추가 정보를 희소 벡터의 크기 (magnitude)로 매핑하는 것이다. 마지막으로, SVC 기술을 파일럿 전송에 활용하는 방법을 제안한다. 특히, 채널 추정을 위해 채널 임펄스 응답의 신호를 희소화하는 프리코딩 기법을 제안한다. 파일럿 신호을 프로코딩 없이 전송되는 기존의 방식과 달리, 제안된 기술에서는 파일럿 신호를 빔포밍하여 전송한다. 제안된 기법은 기지국에서 다중 안테나를 활용하여 채널 응답의 0이 아닌 요소를 최소화하는 시간 영역 희소 프리코딩을 적용하였다. 이를 통해 더 적확한 채널 추정을 가능하며 더 적은 파일럿 오버헤드로 채널 추정이 가능하다.Abstract i Contents iv List of Tables viii List of Figures ix 1 INTRODUCTION 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Three Key Services in 5G systems . . . . . . . . . . . . . . . 2 1.1.2 Sparse Processing in Wireless Communications . . . . . . . . 4 1.2 Contributions and Organization . . . . . . . . . . . . . . . . . . . . . 7 1.3 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Sparse Vector Coding for Downlink Ultra-reliable and Low Latency Communications 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 URLLC Service Requirements . . . . . . . . . . . . . . . . . . . . . 15 2.2.1 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.2 Ultra-High Reliability . . . . . . . . . . . . . . . . . . . . . 17 2.2.3 Coexistence . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 URLLC Physical Layer in 5G NR . . . . . . . . . . . . . . . . . . . 18 2.3.1 Packet Structure . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.2 Frame Structure and Latency-sensitive Scheduling Schemes . 20 2.3.3 Solutions to the Coexistence Problem . . . . . . . . . . . . . 22 2.4 Short-sized Packet in LTE-Advanced Downlink . . . . . . . . . . . . 24 2.5 Sparse Vector Coding . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.5.1 SVC Encoding and Transmission . . . . . . . . . . . . . . . 25 2.5.2 SVC Decoding . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5.3 Identification of False Alarm . . . . . . . . . . . . . . . . . . 33 2.6 SVC Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . 36 2.7 Implementation Issues . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.7.1 Codebook Design . . . . . . . . . . . . . . . . . . . . . . . . 48 2.7.2 High-order Modulation . . . . . . . . . . . . . . . . . . . . . 49 2.7.3 Diversity Transmission . . . . . . . . . . . . . . . . . . . . . 50 2.7.4 SVC without Pilot . . . . . . . . . . . . . . . . . . . . . . . 50 2.7.5 Threshold to Prevent False Alarm Event . . . . . . . . . . . . 51 2.8 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 52 2.8.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 52 2.8.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 53 2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3 Sparse Vector Coding for Uplink Massive Machine-type Communications 59 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.2 Uplink NOMA transmission for mMTC . . . . . . . . . . . . . . . . 61 3.3 Sparse Vector Coding based NOMA for mMTC . . . . . . . . . . . . 63 3.3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.3.2 Joint Multiuser Decoding . . . . . . . . . . . . . . . . . . . . 66 3.4 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 68 3.4.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 68 3.4.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 69 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4 Pilot-less Sparse Vector Coding for Short Packet Transmission 72 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.2 Pilot-less Sparse Vector Coding Processing . . . . . . . . . . . . . . 75 4.2.1 SVC Processing with Pilot Symbols . . . . . . . . . . . . . . 75 4.2.2 Pilot-less SVC . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.2.3 PL-SVC Decoding in Multiple Basestation Antennas . . . . . 78 4.3 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 80 4.3.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 80 4.3.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 81 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5 Joint Analog and Quantized Feedback via Sparse Vector Coding 84 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.2 System Model for Joint Spase Vector Coding . . . . . . . . . . . . . 86 5.3 Sparse Recovery Algorithm and Performance Analysis . . . . . . . . 90 5.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.4.1 Linear Interpolation of Sensing Information . . . . . . . . . . 96 5.4.2 Linear Combined Feedback . . . . . . . . . . . . . . . . . . 96 5.4.3 One-shot Packet Transmission . . . . . . . . . . . . . . . . . 96 5.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.2 Results and Discussions . . . . . . . . . . . . . . . . . . . . 98 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 6 Sparse Beamforming for Enhanced Mobile Broadband Communications 101 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 6.1.1 Increase the number of transmit antennas . . . . . . . . . . . 102 6.1.2 2D active antenna system (AAS) . . . . . . . . . . . . . . . . 103 6.1.3 3D channel environment . . . . . . . . . . . . . . . . . . . . 104 6.1.4 RS transmission for CSI acquisition . . . . . . . . . . . . . . 106 6.2 System Design and Standardization of FD-MIMO Systems . . . . . . 107 6.2.1 Deployment scenarios . . . . . . . . . . . . . . . . . . . . . 108 6.2.2 Antenna configurations . . . . . . . . . . . . . . . . . . . . . 108 6.2.3 TXRU architectures . . . . . . . . . . . . . . . . . . . . . . 109 6.2.4 New CSI-RS transmission strategy . . . . . . . . . . . . . . . 112 6.2.5 CSI feedback mechanisms for FD-MIMO systems . . . . . . 114 6.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.3.1 Basic System Model . . . . . . . . . . . . . . . . . . . . . . 116 6.3.2 Beamformed Pilot Transmission . . . . . . . . . . . . . . . . 117 6.4 Sparsification of Pilot Beamforming . . . . . . . . . . . . . . . . . . 118 6.4.1 Time-domain System Model without Pilot Beamforming . . . 119 6.4.2 Pilot Beamforming . . . . . . . . . . . . . . . . . . . . . . . 120 6.5 Channel Estimation of Beamformed Pilots . . . . . . . . . . . . . . . 124 6.5.1 Recovery using Multiple Measurement Vector . . . . . . . . . 124 6.5.2 MSE Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 128 6.6 Simulations and Discussion . . . . . . . . . . . . . . . . . . . . . . . 129 6.6.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 129 6.6.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 130 6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 7 Conclusion 136 7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 7.2 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . 139 Abstract (In Korean) 152Docto

Beam Tracking Strategies for 5G New Radio Networks Operating in the Millimetre Wave Bands

[ES] La llegada de la próxima generación del estándar de comunicaciones móviles, la llamada quinta generación (5G), es prácticamente una realidad. Las primeras redes comerciales han comenzado a ser desplegadas, centrándose en ofrecer altas velocidades de transferencia de datos. Sin embargo, el estándar 5G va mucho más allá y prevé dar soporte a nuevos servicios que pretenden revolucionar la sociedad. Estos nuevos servicios imponen un nivel alto de requisitos en no solo en cuanto a velocidad del tráfico de datos, sino en cuanto a latencia o número de dispositivos conectados simultáneamente. La amplia variedad de requisitos no puede ser soportada por las redes de cuarta generación (4G), por lo que se hizo necesario plantear un nuevo paradigma para las redes inalámbricas. Con la promesa de grandes cantidades de ancho de banda sin utilizar, el estándar 5G contempla utilizar frecuencias en la comúnmente conocida como banda de milimétricas (mmWave). Esta banda presenta grandes pérdidas de propagación, que se acentúan si existen bloqueos de señal. Actividades regulatorias del uso de las bandas de milimétricas atrajo el interés tanto de la industria como de la academia en plantear soluciones para dar servicio en estas bandas. En los últimos años se han presentado infinidad de trabajos basados en sistemas con múltiples antenas o MIMO, para conformar las señales transmitidas o recibidas en haces apuntando en determinadas direcciones. La ganancia que aportan los sistemas MIMO pueden compensar las altas pérdidas de propagación, asegurando la viabilidad de las comunicaciones mmWave. Se ha detectado una evidente falta de estudios sobre la viabilidad de sistemas MIMO en entornos móviles y dinámicos con bloqueos que hagan necesario que el sistema se reconfigure. Esta Tesis pretende cubrir este espacio desde un enfoque práctico y propone mecanismos de gestión de los haces para hacerles un seguimiento utilizando los recursos y mecanismos del nuevo estándar 5G. Las soluciones aportadas se basan en el uso eficiente de los reportes de medidas de las señales de referencia estandarizadas en enlace descendente. En primer lugar, esta Tesis recoge un análisis minucioso del estado del arte, donde se corrobora la necesidad de aportar soluciones de seguimiento de haces en sistemas de comunicaciones en la banda de milimétricas. Además, se estudian los diferentes mecanismos definidos en el estándar 5G y que posibilitan el seguimiento. Cabe destacar que el estándar no define un mecanismo único a seguir, permitiendo presentar propuestas. Una vez conocidas las tecnologías, se centra el estudio en el impacto del seguimiento sobre las prestaciones a nivel de red y de enlace. Dicho estudio se realiza sobre un sistema punto a punto, donde el terminal móvil se desplaza por un entorno urbano. En base a simulaciones de red, se cuantifica el índice de seguimiento de haz y de cómo dicho seguimiento afecta a la relación señal a ruido más interferencia (SINR) y la tasa de transmisión del usuario. Las soluciones de seguimiento propuestas en esta Tesis se pueden clasificar en dos categorías. En una primera categoría, se realiza el seguimiento en base a reportes de medidas de las señales de referencia. Independientemente de la velocidad, se alcanza un seguimiento del 91% con poca penalización en la tasa de transmisión si se monitorizan los haces de interés con una periodicidad menor de 20 ms. En la segunda categoría caben mecanismos de seguimiento que hacen uso de fuentes externas de información. Dentro de esta categoría, se propone un fingerprinting que relacione haces con la localización reportada y un modelo de machine learning (ML) que prediga los haces a utilizar. El fingerprinting proporciona los mismos niveles de rendimiento. Sin embargo, esta solución es muy sensible a errores y requiere considerar todos los casos posibles, lo que la hace tecnológicamente inviable. En cambio, el modelo de ML, que hace p[CA] L'arribada de la següent generació de l'estàndard de comunicacions mòbils, l'anomenada cinquena generació (5G), es pràcticament una realitat. Les primeres xarxes comercials han començat a desplegar-se i s'han centrat en oferir altes velocitats de transferència de dades. No obstant, l'estàndard 5G va molt mes allà y preveu donar suport a nous serveis que pretenen revolucionar la societat. Estos nous serveis imposen un alt nivell de requisits no sols en quant a velocitat de tràfic de dades, si no també en quant a latència o número de connexions simultànies. L'ampla varietat de requisits no es suportada per les xarxes de quarta generació (4G) actuals, per el qual es va fer necessari un nou paradigma de xarxes sense fil. Amb la promesa de amplies quantitats d'ample de banda, l'estàndard 5G contempla utilitzar freqüències a la banda de mil·limètriques. Esta banda presenta l'inconvenient d'experimentar grans pèrdues de propagació, que s'accentuen en cas de bloqueigs. L'apertura de les bandes de mil·limètriques va atraure l'interès tant de l'industria com de l'acadèmia en plantejar solucions per a donar servei en estes bandes. En els últims anys s'han presentat infinitat de treballs basats en sistemes amb múltiples antenes o MIMO, per a conformar els senyals transmesos o rebuts en feixos apuntant en determinades direccions d'interès. El guany de feix es pot utilitzar per a compensar les pèrdues de propagació, assegurant la viabilitat de les comunicacions en la banda de mil·limètriques. No obstant això, s'ha detectat una preocupant manca d'estudis sobre la viabilitat d'estos sistemes en entorns mòbils i dinàmics, amb obstacles que bloquejen els feixos i facen necessari que el sistema es reconfigure. El present treball de Tesi pretén cobrir este espai buit i des d'un punt de vista pràctic, es proposen mecanismes de gestió dels feixos per a ser el seguiment utilitzant els recursos i mecanismes dels que disposa l'estàndard 5G. D'esta manera, les solucions aportades es basen en la utilització eficient dels reports de mesures dels senyals de referència del enllaç descendent. En primer lloc, esta Tesi recull una anàlisi minuciosa de l'estat de l'art on es corrobora la necessitat de aportar solucions de seguiment de feixos per a comunicacions en la banda de freqüències mil·limètriques. A més a més, s'estudien els diferents mecanismes definits a l'estàndard 5G i que possibiliten el seguiment. Cap destacar que l'estàndard no defineix un mecanisme únic, si no que deixa la porta oberta a presentar propostes. Una vegada conegudes les tecnologies, l'estudi es centra en l'impacte del seguiment sobre les prestacions a nivell de xarxa i d'enllaç. Este estudi es realitza sobre un sistema MIMO punt a punt, en una única estació base i un terminal mòbil desplaçant-se en un entorn urbà. En base a simulacions d'extrem a extrem, es quantifica l'índex de seguiment de feix i com l'anomenat seguiment afecta a la relació senyal a soroll més interferència (SINR) i a la taxa instantània de transmissió de l'usuari. Les solucions de seguiment de feixos propostes a la Tesi es poden classificar en dos categories. A la primera categoria, el seguiment de feixos es realitza en base als reports de mesures dels senyals de referència. Independentment de la velocitat, s'arriba a una taxa de seguiment del 91% amb poca penalització de taxa de transmissió si els feixos d'interès es mesuren amb una periodicitat menor a 20 ms. A la segona categoria pertanyen els algoritmes que utilitzen fonts d'informació externes. Dins d'aquesta categoria es proposa un fingerprinting que relaciona un parell de feixos amb la ubicació de l'usuari, i a banda un model d'intel·ligència artificial (IA) que preveu el feix a utilitzar. El fingerprinting ofereix el mateix rendiment. Però, esta solució es molt sensible a errors i requereix considerar tots els casos possibles, fent-la tecnològicament inviable. En canvi, el[EN] The arrival of the next generation of mobile communication standards, the so-called Fifth Generation (5G), is already a reality. The first commercial networks have begun to be deployed, and they focus on providing higher data rates. However, the 5G standard goes much further from that and aims at providing support to new services which will revolutionise the society. These new services impose a high level of requirements not only in terms of the data traffic speed, but also in terms of very low latency or incredibly large number of simultaneous connections. This wide variety of requirements cannot be technologically supported by the current Fourth Generation (4G) networks, so it became necessary to move forward with a new paradigm for wireless networks. With the promise of large amounts of bandwidth, in the order of GHz, the 5G standard contemplates the use of frequencies in the commonly known Millimetre Wave (mmWave) band. The mmWave band experiences large propagation losses, which are accentuated in blockage events. Regulatory activities worldwide in the mmWave bands attracted the interest of both the industry and the academia. In the last few years, a tremendous number of contributions on mmWave propagation studies and networks have appeared, most of them based on Multiple-Input Multiple-Output (MIMO) solutions. MIMO architectures allow to beamform, which focuses the radiated energy on certain directions of interest called beams. The additional beam gain compensates the high propagation losses, ensuring the viability of the communications in the mmWave band. There is an evident lack of viability studies of mmWave MIMO systems in mobile and highly-dynamic environments, where obstacles may block beams and forcing frequent re-configurations. This Thesis work aims to fill this gap from a practical approach. This Thesis proposes beam management mechanisms utilising the mechanisms and resources offered by the Third Generation Partnership Project (3GPP) 5G radio access standard: 5G New Radio (NR). The practical solutions are based on the efficient use of measurement reports of standardised downlink Reference Signals (RS). In first place, this Thesis provides a thorough state-of-the-art analysis and corroborates the need of adopting beam tracking solutions for mmWave networks. Then, a complete overview of the 5G standard mechanisms that enable beam tracking is given. The NR standard does not define a standardised mechanism for beam tracking, leaving the door open to proposals to carry out such monitoring. Once the technologies have been identified, the Thesis continues with assessing the impact of the beam tracking strategies on the network and link-level performance. The study is focused on individual point-to-point mmWave links in a realistic urban environment. Based on end-to-end network simulations, the Thesis is interested in assessing the beam tracking success ratio and how beam misalignment affects the perceived Signal to Noise plus Interference Ratio (SINR) and user throughput at pedestrian and vehicular speeds. The beam tracking solutions proposed in this Thesis fall into two categories. The first category monitors beams based on measuring and reporting beamformed RS. Regardless of the speed, this beam tracking category provides up to 91 % tracking performance, with little throughput reduction if the beams of interest are measured with a periodicity below 20 ms. Beam tracking in the second category relies on external information sources. Within this category, this Thesis proposes a fingerprinting database relating beams to the user position and a machine learning (ML) model. Fingerprinting beam tracking is technologically viable and provides similar performance levels. However, this solution is very sensitive to errors and requires considering all possible situations. The ML beam tracking, which makes predictions with a 16 % of estimation error for the reference data set.I want to thank the Spanish Ministry of Education and Professional Formation for funding this Thesis work with an official pre-doctoral contract grant.Herranz Claveras, C. (2019). Beam Tracking Strategies for 5G New Radio Networks Operating in the Millimetre Wave Bands [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/130845TESI

Massive MIMO Systems for 5G Communications

Massive MIMO will improve the performance of future 5G systems in terms of data rate and spectral efficiency, while accommodating a large number of users. Furthermore, it allows for 3D beamforming in order to provide more degrees of freedom and increase the number of high-throughput users. Massive MIMO is expected to provide more advantages compared to other solutions in terms of energy and spectral efficiency. This will be achieved by focusing the radiation towards the direction of the intended users, thus implementing simultaneous transmission to many users while keeping interference low. It can boost the capacity compared to a conventional antenna solution, resulting in a spectral efficiency up to 50 times greater than that provided by actual 4G technology. However, to take full advantage of this technology and to overcome the challenges of implementation in a real environment, a complicated radio system is required. The purpose of this work is to present the MIMO technology evolution and challenges in a simple introductory way and investigate potential system enhancements

AirSync: Enabling Distributed Multiuser MIMO with Full Spatial Multiplexing

The enormous success of advanced wireless devices is pushing the demand for higher wireless data rates. Denser spectrum reuse through the deployment of more access points per square mile has the potential to successfully meet the increasing demand for more bandwidth. In theory, the best approach to density increase is via distributed multiuser MIMO, where several access points are connected to a central server and operate as a large distributed multi-antenna access point, ensuring that all transmitted signal power serves the purpose of data transmission, rather than creating "interference." In practice, while enterprise networks offer a natural setup in which distributed MIMO might be possible, there are serious implementation difficulties, the primary one being the need to eliminate phase and timing offsets between the jointly coordinated access points. In this paper we propose AirSync, a novel scheme which provides not only time but also phase synchronization, thus enabling distributed MIMO with full spatial multiplexing gains. AirSync locks the phase of all access points using a common reference broadcasted over the air in conjunction with a Kalman filter which closely tracks the phase drift. We have implemented AirSync as a digital circuit in the FPGA of the WARP radio platform. Our experimental testbed, comprised of two access points and two clients, shows that AirSync is able to achieve phase synchronization within a few degrees, and allows the system to nearly achieve the theoretical optimal multiplexing gain. We also discuss MAC and higher layer aspects of a practical deployment. To the best of our knowledge, AirSync offers the first ever realization of the full multiuser MIMO gain, namely the ability to increase the number of wireless clients linearly with the number of jointly coordinated access points, without reducing the per client rate.Comment: Submitted to Transactions on Networkin