Array Architectures and Physical Layer Design for Millimeter-Wave Communications Beyond 5G

Ever increasing demands in mobile data rates have resulted in exploration of millimeter-wave (mmW) frequencies for the next generation (5G) wireless networks. Communications at mmW frequencies is presented with two keys challenges. Firstly, high propagation loss requires base stations (BSs) and user equipment (UEs) to use a large number of antennas and narrow beams to close the link with sufficient received signal power. Consequently, communications using narrow beams create a new challenge in channel estimation and link establishment based on fine angular probing. Current mmW system use analog phased arrays that can probe only one angle at the time which results in high latency during link establishment and channel tracking. It is desirable to design low latency beam training by exploring both physical layer designs and array architectures that could replace current 5G approaches and pave the way to the communications for frequency bands in higher mmW band and sub-THz region where larger antenna arrays and communications bandwidth can be exploited. To this end, we propose a novel signal processing techniques exploiting unique properties of mmW channel, and show both theoretically, in simulation and experiments its advantages over conventional approaches. Secondly, we explore different array architecture design and analyze their trade-offs between spectral efficiency and power consumption and area. For comprehensive comparison, we have developed a methodology for optimal design of system parameters for different array architecture candidates based on the spectral efficiency target, and use these parameters to estimate the array area and power consumption based on the circuits reported in the literature. We show that the hybrid analog and digital architectures have severe scalability concerns in radio frequency signal distribution with increased array size and spatial multiplexing levels, while the fully-digital array architectures have the best performance and power/area trade-offs.The developed approaches are based on a cross-disciplinary research that combines innovation in model based signal processing, machine learning, and radio hardware. This work is the first to apply compressive sensing (CS), a signal processing tool that exploits sparsity of mmW channel model, to accelerate beam training of mmW cellular system. The algorithm is designed to address practical issues including the requirement of cell discovery and synchronization that involves estimation of angular channel together with carrier frequency offset and timing offsets. We have analyzed the algorithm performance in the 5G compliant simulation and showed that an order of magnitude saving is achieved in initial access latency for the desired channel estimation accuracy. Moreover, we are the first to develop and implement a neural network assisted compressive beam alignment to deal with hardware impairments in mmW radios. We have used 60GHz mmW testbed to perform experiments and show that neural networks approach enhances alignment rate compared to CS. To further accelerate beam training, we proposed a novel frequency selective probing beams using the true-time-delay (TTD) analog array architecture. Our approach utilizes different subcarriers to scan different directions, and achieves a single-shot beam alignment, the fastest approach reported to date. Our comprehensive analysis of different array architectures and exploration of emerging architectures enabled us to develop an order of magnitude faster and energy efficient approaches for initial access and channel estimation in mmW systems

Mobility management in multi-RAT multiI-band heterogeneous networks

Support for user mobility is the raison d'etre of mobile cellular networks. However, mounting pressure for more capacity is leading to adaption of multi-band multi-RAT ultra-dense network design, particularly with the increased use of mmWave based small cells. While such design for emerging cellular networks is expected to offer manyfold more capacity, it gives rise to a new set of challenges in user mobility management. Among others, frequent handovers (HO) and thus higher impact of poor mobility management on quality of user experience (QoE) as well as link capacity, lack of an intelligent solution to manage dual connectivity (of user with both 4G and 5G cells) activation/deactivation, and mmWave cell discovery are the most critical challenges. In this dissertation, I propose and evaluate a set of solutions to address the aforementioned challenges. The beginning outcome of our investigations into the aforementioned problems is the first ever taxonomy of mobility related 3GPP defined network parameters and Key Performance Indicators (KPIs) followed by a tutorial on 3GPP-based 5G mobility management procedures. The first major contribution of the thesis here is a novel framework to characterize the relationship between the 28 critical mobility-related network parameters and 8 most vital KPIs. A critical hurdle in addressing all mobility related challenges in emerging networks is the complexity of modeling realistic mobility and HO process. Mathematical models are not suitable here as they cannot capture the dynamics as well as the myriad parameters and KPIs involved. Existing simulators also mostly either omit or overly abstract the HO and user mobility, chiefly because the problems caused by poor HO management had relatively less impact on overall performance in legacy networks as they were not multi-RAT multi-band and therefore incurred much smaller number of HOs compared to emerging networks. The second key contribution of this dissertation is development of a first of its kind system level simulator, called SyntheticNET that can help the research community in overcoming the hurdle of realistic mobility and HO process modeling. SyntheticNET is the very first python-based simulator that fully conforms to 3GPP Release 15 5G standard. Compared to the existing simulators, SyntheticNET includes a modular structure, flexible propagation modeling, adaptive numerology, realistic mobility patterns, and detailed HO evaluation criteria. SyntheticNET’s python-based platform allows the effective application of Artificial Intelligence (AI) to various network functionalities. Another key challenge in emerging multi-RAT technologies is the lack of an intelligent solution to manage dual connectivity with 4G as well 5G cell needed by a user to access 5G infrastructure. The 3rd contribution of this thesis is a solution to address this challenge. I present a QoE-aware E-UTRAN New Radio-Dual Connectivity (EN-DC) activation scheme where AI is leveraged to develop a model that can accurately predict radio link failure (RLF) and voice muting using the low-level measurements collected from a real network. The insights from the AI based RLF and mute prediction models are then leveraged to configure sets of 3GPP parameters to maximize EN-DC activation while keeping the QoE-affecting RLF and mute anomalies to minimum. The last contribution of this dissertation is a novel solution to address mmWave cell discovery problem. This problem stems from the highly directional nature of mmWave transmission. The proposed mmWave cell discovery scheme builds upon a joint search method where mmWave cells exploit an overlay coverage layer from macro cells sharing the UE location to the mmWave cell. The proposed scheme is made more practical by investigating and developing solutions for the data sparsity issue in model training. Ability to work with sparse data makes the proposed scheme feasible in realistic scenarios where user density is often not high enough to provide coverage reports from each bin of the coverage area. Simulation results show that the proposed scheme, efficiently activates EN-DC to a nearby mmWave 5G cell and thus substantially reduces the mmWave cell discovery failures compared to the state of the art cell discovery methods

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

D6.3 Intermediate system evaluation results

The overall purpose of METIS is to develop a 5G system concept that fulfil s the requirements of the beyond-2020 connected information society and to extend today’s wireless communication systems for new usage cases. First, in this deliverable an updated view on the overall METIS 5G system concept is presented. Thereafter, simulation results for the most promising technology components supporting the METIS 5G system concept are reported. Finally, s imulation results are presented for one relevant aspect of each Horizontal Topic: Direct Device - to - Device Communication, Massive Machine Communication, Moving Networks, Ultra - Dense Networks, and Ultra - Reliable Communication.Popovski, P.; Mange, G.; Fertl, P.; Gozálvez - Serrano, D.; Droste, H.; Bayer, N.; Roos, A.... (2014). D6.3 Intermediate system evaluation results. http://hdl.handle.net/10251/7676

Improved handover decision scheme for 5g mm-wave communication: optimum base station selection using machine learning approach.

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyThe rapid growth in mobile and wireless devices has led to an exponential demand for data traf fic and exacerbated the burden on conventional wireless networks. Fifth generation (5G) and beyond networks are expected to not only accommodate this growth in data demand but also provide additional services beyond the capability of existing wireless networks, while main taining a high quality-of-experience (QoE) for users. The need for several orders of magnitude increase in system capacity has necessitated the use of millimetre wave (mm-wave) frequencies as well as the proliferation of low-power small cells overlaying the existing macro-cell layer. These approaches offer a potential increase in throughput in magnitudes of several gigabits per second and a reduction in transmission latency, but they also present new challenges. For exam ple, mm-wave frequencies have higher propagation losses and a limited coverage area, thereby escalating mobility challenges such as more frequent handovers (HOs). In addition, the ad vent of low-power small cells with smaller footprints also causes signal fluctuations across the network, resulting in repeated HOs (ping-pong) from one small cell (SC) to another. Therefore, efficient HO management is very critical in future cellular networks since frequent HOs pose multiple threats to the quality-of-service (QoS), such as a reduction in the system throughput as well as service interruptions, which results in a poor QoE for the user. How ever, HO management is a significant challenge in 5G networks due to the use of mm-wave frequencies which have much smaller footprints. To address these challenges, this work in vestigates the HO performance of 5G mm-wave networks and proposes a novel method for achieving seamless user mobility in dense networks. The proposed model is based on a double deep reinforcement learning (DDRL) algorithm. To test the performance of the model, a com parative study was made between the proposed approach and benchmark solutions, including a benchmark developed as part of this thesis. The evaluation metrics considered include system throughput, execution time, ping-pong, and the scalability of the solutions. The results reveal that the developed DDRL-based solution vastly outperforms not only conventional methods but also other machine-learning-based benchmark techniques. The main contribution of this thesis is to provide an intelligent framework for mobility man agement in the connected state (i.e HO management) in 5G. Though primarily developed for mm-wave links between UEs and BSs in ultra-dense heterogeneous networks (UDHNs), the proposed framework can also be applied to sub-6 GHz frequencies