Ultra Dense Networks Deployment for beyond 2020 Technologies

A new communication paradigm is foreseen for beyond 2020 society, due to the emergence of new broadband services and the Internet of Things era. The set of requirements imposed by these new applications is large and diverse, aiming to provide a ubiquitous broadband connectivity. Research community has been working in the last decade towards the definition of the 5G mobile wireless networks that will provide the proper mechanisms to reach these challenging requirements. In this framework, three key research directions have been identified for the improvement of capacity in 5G: the increase of the spectral efficiency by means of, for example, the use of massive MIMO technology, the use of larger amounts of spectrum by utilizing the millimeter wave band, and the network densification by deploying more base stations per unit area. This dissertation addresses densification as the main enabler for the broadband and massive connectivity required in future 5G networks. To this aim, this Thesis focuses on the study of the UDN. In particular, a set of technology enablers that can lead UDN to achieve their maximum efficiency and performance are investigated, namely, the use of higher frequency bands for the benefit of larger bandwidths, the use of massive MIMO with distributed antenna systems, and the use of distributed radio resource management techniques for the inter-cell interference coordination. Firstly, this Thesis analyzes whether there exists a fundamental performance limit related with densification in cellular networks. To this end, the UDN performance is evaluated by means of an analytical model consisting of a 1-dimensional network deployment with equally spaced BS. The inter-BS distance is decreased until reaching the limit of densification when this distance approaches 0. The achievable rates in networks with different inter-BS distances are analyzed for several levels of transmission power availability, and for various types of cooperation among cells. Moreover, UDN performance is studied in conjunction with the use of a massive number of antennas and larger amounts of spectrum. In particular, the performance of hybrid beamforming and precoding MIMO schemes are assessed in both indoor and outdoor scenarios with multiple cells and users, working in the mmW frequency band. On the one hand, beamforming schemes using the full-connected hybrid architecture are analyzed in BS with limited number of RF chains, identifying the strengths and weaknesses of these schemes in a dense-urban scenario. On the other hand, the performance of different indoor deployment strategies using HP in the mmW band is evaluated, focusing on the use of DAS. More specifically, a DHP suitable for DAS is proposed, comparing its performance with that of HP in other indoor deployment strategies. Lastly, the presence of practical limitations and hardware impairments in the use of hybrid architectures is also investigated. Finally, the investigation of UDN is completed with the study of their main limitation, which is the increasing inter-cell interference in the network. In order to tackle this problem, an eICIC scheduling algorithm based on resource partitioning techniques is proposed. Its performance is evaluated and compared to other scheduling algorithms under several degrees of network densification. After the completion of this study, the potential of UDN to reach the capacity requirements of 5G networks is confirmed. Nevertheless, without the use of larger portions of spectrum, a proper interference management and the use of a massive number of antennas, densification could turn into a serious problem for mobile operators. Performance evaluation results show large system capacity gains with the use of massive MIMO techniques in UDN, and even greater when the antennas are distributed. Furthermore, the application of ICIC techniques reveals that, besides the increase in system capacity, it brings significant energy savings to UDNs.A partir del año 2020 se prevé que un nuevo paradigma de comunicación surja en la sociedad, debido a la aparición de nuevos servicios y la era del Internet de las cosas. El conjunto de requisitos impuesto por estas nuevas aplicaciones es muy amplio y diverso, y tiene como principal objetivo proporcionar conectividad de banda ancha y universal. En las últimas décadas, la comunidad científica ha estado trabajando en la definición de la 5G de redes móviles que brindará los mecanismos necesarios para garantizar estos requisitos. En este marco, se han identificado tres mecanismos clave para conseguir el necesario incremento de capacidad de la red: el aumento de la eficiencia espectral a través de, por ejemplo, el uso de tecnologías MIMO masivas, la utilización de mayores porciones del espectro en frecuencia y la densificación de la red mediante el despliegue de más estaciones base por área. Esta Tesis doctoral aborda la densificación como el principal mecanismo que permitirá la conectividad de banda ancha y universal requerida en la 5G, centrándose en el estudio de las Redes Ultra Densas o UDNs. En concreto, se analiza el conjunto de tecnologías habilitantes que pueden llevar a las UDNs a obtener su máxima eficiencia y prestaciones, incluyendo el uso de altas frecuencias para el aprovechamiento de mayores anchos de banda, la utilización de MIMO masivo con sistemas de antenas distribuidas y el uso de técnicas de reparto de recursos distribuidas para la coordinación de interferencias. En primer lugar, se analiza si existe un límite fundamental en la mejora de las prestaciones en relación a la densificación. Con este fin, las prestaciones de las UDNs se evalúan utilizando un modelo analítico de red unidimensional con BSs equiespaciadas, en el que la distancia entre BSs se disminuye hasta alcanzar el límite de densificación cuando ésta se aproxima a 0. Las tasas alcanzables en redes con distintas distancias entre BSs son analizadas, considerando distintos niveles de potencia disponible en la red y varios grados de cooperación entre celdas. Además, el comportamiento de las UDNs se estudia junto al uso masivo de antenas y la utilización de anchos de banda mayores. Más concretamente, las prestaciones de ciertas técnicas híbridas MIMO de precodificación y beamforming se examinan en la banda milimétrica. Por una parte, se analizan esquemas de beamforming en BSs con arquitectura híbrida en función de la disponibilidad de cadenas de radiofrecuencia en escenarios exteriores. Por otra parte, se evalúan las prestaciones de ciertos esquemas de precodificación híbrida en escenarios interiores, utilizando distintos despliegues y centrando la atención en los sistemas de antenas distribuidos o DAS. Además, se propone un algoritmo de precodificación híbrida específico para DAS, y se evalúan y comparan sus prestaciones con las de otros algoritmos de precodificación utilizados. Por último, se investiga el impacto en las prestaciones de ciertas limitaciones prácticas y deficiencias introducidas por el uso de dispositivos no ideales. Finalmente, el estudio de las UDNs se completa con el análisis de su principal limitación, el nivel creciente de interferencia en la red. Para ello, se propone un algoritmo de control de interferencias basado en la partición de recursos. Sus prestaciones son evaluadas y comparadas con las de otras técnicas de asignación de recursos. Tras este estudio, se puede afirmar que las UDNs tienen gran potencial para la consecución de los requisitos de la 5G. Sin embargo, sin el uso conjunto de mayores porciones del espectro, adecuadas técnicas de control de la interferencia y el uso masivo de antenas, las UDNs pueden convertirse en serios obstáculos para los operadores móviles. Los resultados de la evaluación de prestaciones de estas tecnologías confirman el gran aumento de la capacidad de las redes mediante el uso masivo de antenas y la introducción de mecanismos de IA partir de l'any 2020 es preveu un nou paradigma de comunicació en la societat, degut a l'aparició de nous serveis i la era de la Internet de les coses. El conjunt de requeriments imposat per aquestes noves aplicacions és ampli i divers, i té com a principal objectiu proporcionar connectivitat universal i de banda ampla. En les últimes dècades, la comunitat científica ha estat treballant en la definició de la 5G, que proveirà els mecanismes necessaris per a garantir aquests exigents requeriments. En aquest marc, s'han identificat tres mecanismes claus per a aconseguir l'increment necessari en la capacitat: l'augment de l'eficiència espectral a través de, per exemple, l'ús de tecnologies MIMO massives, la utilització de majors porcions de l'espectre i la densificació mitjançant el desplegament de més estacions base per àrea. Aquesta Tesi aborda la densificació com a principal mecanisme que permetrà la connectivitat de banda ampla i universal requerida en la 5G, centrant-se en l' estudi de les xarxes ultra denses (UDNs). Concretament, el conjunt de tecnologies que poden dur a les UDNs a la seua màxima eficiència i prestacions és analitzat, incloent l'ús d'altes freqüències per a l'aprofitament de majors amplàries de banda, la utilització de MIMO massiu amb sistemes d'antenes distribuïdes i l'ús de tècniques distribuïdes de repartiment de recursos per a la coordinació de la interferència. En primer lloc, aquesta Tesi analitza si existeix un límit fonamental en les prestacions en relació a la densificació. Per això, les prestacions de les UDNs s'avaluen utilitzant un model analític unidimensional amb estacions base equidistants, en les quals la distància entre estacions base es redueix fins assolir el límit de densificació quan aquesta distància s'aproxima a 0. Les taxes assolibles en xarxes amb diferents distàncies entre estacions base s'analitzen considerant diferents nivells de potència i varis graus de cooperació entre cel·les. A més, el comportament de les UDNs s'estudia conjuntament amb l'ús massiu d'antenes i la utilització de majors amplàries de banda. Més concretament, les prestacions de certes tècniques híbrides MIMO de precodificació i beamforming s'examinen en la banda mil·limètrica. D'una banda, els esquemes de beamforming aplicats a estacions base amb arquitectures híbrides és analitzat amb disponibilitat limitada de cadenes de radiofreqüència a un escenari urbà dens. D'altra banda, s'avaluen les prestacions de certs esquemes de precodificació híbrida en escenaris d'interior, utilitzant diferents estratègies de desplegament i centrant l'atenció en els sistemes d' antenes distribuïdes (DAS). A més, es proposa un algoritme de precodificació híbrida distribuïda per a DAS, i s'avaluen i comparen les seues prestacions amb les de altres algoritmes. Per últim, s'investiga l'impacte de les limitacions pràctiques i altres deficiències introduïdes per l'ús de dispositius no ideals en les prestacions de tots els esquemes anteriors. Finalment, l' estudi de les UDNs es completa amb l'anàlisi de la seua principal limitació, el nivell creixent d'interferència entre cel·les. Per tractar aquest problema, es proposa un algoritme de control d'interferències basat en la partició de recursos. Les prestacions de l'algoritme proposat s'avaluen i comparen amb les d'altres tècniques d'assignació de recursos. Una vegada completat aquest estudi, es pot afirmar que les UDNs tenen un gran potencial per aconseguir els ambiciosos requeriments plantejats per a la 5G. Tanmateix, sense l'ús conjunt de majors amplàries de banda, apropiades tècniques de control de la interferència i l'ús massiu d'antenes, les UDNs poden convertir-se en seriosos obstacles per als operadors mòbils. Els resultats de l'avaluació de prestacions d' aquestes tecnologies confirmen el gran augment de la capacitat de les xarxes obtingut mitjançant l'ús massiu d'antenes i la introducciGiménez Colás, S. (2017). Ultra Dense Networks Deployment for beyond 2020 Technologies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86204TESI

Multiuser MIMO techniques with feedback

Kooperative Antennenanlagen haben vor kurzem einen heißen Forschungsthema geworden, da Sie deutlich höhere spektrale Effizienz als herkömmliche zelluläre Systeme versprechen. Der Gewinn wird durch die Eliminierung von Inter-Zelle Störungen (ICI) durch Koordinierung der-Antenne Übertragungen erworben. Vor kurzem, verteilte Organisation Methoden vorgeschlagen. Eine der größten Herausforderungen für das Dezentrale kooperative Antennensystem ist Kanalschätzung für den Downlink Kanal besonders wenn FDD verwendet wird. Alle zugehörigen Basisstationen im genossenschaftlichen Bereich müssen die vollständige Kanal Informationen zu Wissen, die entsprechenden precoding Gewicht Matrix zu berechnen. Diese Information ist von mobilen Stationen übertragen werden Stationen mit Uplink Ressourcen zu stützen. Wird als mehrere Basisstationen und mehreren mobilen Stationen in kooperativen Antennensysteme und jede Basisstation und Mobilstation beteiligt sind, können mit mehreren Antennen ausgestattet sein, die Anzahl der Kanal Parameter wieder gefüttert werden erwartet, groß zu sein. In dieser Arbeit wird ein effizientes Feedback Techniken der downlink Kanal Informationen sind für die Multi-user Multiple Input Multiple Output Fall vorgeschlagen, der insbesondere auf verteilte kooperative Antennensysteme zielt. Zuerst wird ein Unterraum-basiertes Kanalquantisierungsverfahren vorgeschlagen, das ein vorbestimmtes Codebuch verwendet. Ein iterativer Codebuchentwurfsalgorithmus wird vorgeschlagen, der zu einem lokalen optimalen Codebuch konvergiert. Darüber hinaus werden Feedback-Overhead-Reduktionsverfahren entwickelt, die die zeitliche Korrelation des Kanals ausnutzen. Es wird gezeigt, dass das vorgeschlagene adaptive Codebuchverfahren in Verbindung mit einem Datenkomprimierungsschema eine Leistung nahe an dem perfekten Kanalfall erzielt, was viel weniger Rückkopplungsoverhead im Vergleich zu anderen Techniken erfordert. Das auf dem Unterraum basierende Kanalquantisierungsverfahren wird erweitert, indem mehrere Antennen auf der Senderseite und/oder auf der Empfängerseite eingeführt werden, und die Leistung eines Vorcodierungs- (/Decodierungs-) Schemas mit regulierter Blockdiagonalisierung (RBD) wurde untersucht. Es wird ein kosteneffizientes Decodierungsmatrixquantisierungsverfahren vorgeschlagen, dass eine komplexe Berechnung an der Mobilstation vermeiden kann, während es nur eine leichte Verschlechterung zeigt. Die Arbeit wird abgeschlossen, indem die vorgeschlagenen Feedback-Methoden hinsichtlich ihrer Leistung, ihres erforderlichen Feedback-Overheads und ihrer Rechenkomplexität verglichen werden.Cooperative antenna systems have recently become a hot research topic, as they promise significantly higher spectral efficiency than conventional cellular systems. The gain is acquired by eliminating inter-cell interference (ICI) through coordination of the base antenna transmissions. Recently, distributed organization methods have been suggested. One of the main challenges of the distributed cooperative antenna system is channel estimation for the downlink channel especially when FDD is used. All of the associated base stations in the cooperative area need to know the full channel state information to calculate the corresponding precoding weight matrix. This information has to be transferred from mobile stations to base stations by using uplink resources. As several base stations and several mobile stations are involved in cooperative antenna systems and each base station and mobile station may be equipped with multiple antennas, the number of channel state parameters to be fed back is expected to be big. In this thesis, efficient feedback techniques of the downlink channel state information are proposed for the multi-user multiple-input multiple-output case, targeting distributed cooperative antenna systems in particular. First, a subspace based channel quantization method is proposed which employs a predefined codebook. An iterative codebook design algorithm is proposed which converges to a local optimum codebook. Furthermore, feedback overhead reduction methods are devised exploiting temporal correlation of the channel. It is shown that the proposed adaptive codebook method in conjunction with a data compression scheme achieves a performance close to the perfect channel case, requiring much less feedback overhead compared with other techniques. The subspace based channel quantization method is extended by introducing multiple antennas at the transmitter side and/or at the receiver side and the performance of a regularized block diagonalization (RBD) precoding(/decoding) scheme has been investigated as well as a zero-forcing (ZF) precoding scheme. A cost-efficient decoding matrix quantization method is proposed which can avoid a complex computation at the mobile station while showing only a slight degradation. The thesis is concluded by comparing the proposed feedback methods in terms of their performance, their required feedback overhead, and their computational complexity. The techniques that are developed in this thesis can be useful and applicable for 5G, which is envisioned to support the high granularity/resolution codebook and its efficient deployment schemes. Keywords: MU-MIMO, COOPA, limited feedback, CSI, CQ, feedback overhead reduction, Givens rotatio

Enabling Technologies for 5G and Beyond: Bridging the Gap between Vision and Reality

It is common knowledge that the fifth generation (5G) of cellular networks will come with drastic transformation in the cellular systems capabilities and will redefine mobile services. 5G (and beyond) systems will be used for human interaction, in addition to person-to-machine and machine-to-machine communications, i.e., every-thing is connected to every-thing. These features will open a whole line of new business opportunities and contribute to the development of the society in many different ways, including developing and building smart cities, enhancing remote health care services, to name a few. However, such services come with an unprecedented growth of mobile traffic, which will lead to heavy challenges and requirements that have not been experienced before. Indeed, the new generations of cellular systems are required to support ultra-low latency services (less than one millisecond), and provide hundred times more data rate and connectivity, all compared to previous generations such as 4G. Moreover, they are expected to be highly secure due to the sensitivity of the transmitted information. Researchers from both academia and industry have been concerting significant efforts to develop new technologies that aim at enabling the new generation of cellular systems (5G and beyond) to realize their potential. Much emphasis has been put on finding new technologies that enhance the radio access network (RAN) capabilities as RAN is considered to be the bottleneck of cellular networks. Striking a balance between performance and cost has been at the center of the efforts that led to the newly developed technologies, which include non-orthogonal multiple access (NOMA), millimeter wave (mmWave) technology, self-organizing network (SON) and massive multiple-input multiple-output (MIMO). Moreover, physical layer security (PLS) has been praised for being a potential candidate for enforcing transmission security when combined with cryptography techniques. Although the main concepts of the aforementioned RAN key enabling technologies have been well defined, there are discrepancies between their intended (i.e., vision) performance and the achieved one. In fact, there is still much to do to bridge the gap between what has been promised by such technologies in terms of performance and what they might be able to achieve in real-life scenarios. This motivates us to identify the main reasons behind the aforementioned gaps and try to find ways to reduce such gaps. We first focus on NOMA where the main drawback of existing solutions is related to their poor performance in terms of spectral efficiency and connectivity. Another major drawback of existing NOMA solutions is that transmission rate per user decreases slightly with the number of users, which is a serious issue since future networks are expected to provide high connectivity. To this end, we develop NOMA solutions that could provide three times the achievable rate of existing solutions while maintaining a constant transmission rate per user regardless of the number of connected users. We then investigate the challenges facing mmWave transmissions. It has been demonstrated that such technology is highly sensitive to blockage, which limits its range of communication. To overcome this obstacle, we develop a beam-codebook based analog beam-steering scheme that achieves near maximum beamforming gain performance. The proposed technique has been tested and verified by real-life measurements performed at Bell Labs. Another line of research pursued in this thesis is investigating challenges pertaining to SON. It is known that radio access network self-planning is the most complex and sensitive task due to its impact on the cost of network deployment, etc., capital expenditure (CAPEX). To tackle this issue, we propose a comprehensive self-planning solution that provides all the planning parameters at once while guaranteeing that the system is optimally planned. The proposed scheme is compared to existing solutions and its superiority is demonstrated. We finally consider the communication secrecy problem and investigated the potential of employing PLS. Most of the existing PLS schemes are based on unrealistic assumptions, most notably is the assumption of having full knowledge about the whereabouts of the eavesdroppers. To solve this problem, we introduce a radically novel nonlinear precoding technique and a coding strategy that together allow to establish secure communication without any knowledge about the eavesdroppers. Moreover, we prove that it is possible to secure communications while achieving near transmitter-receiver channel capacity (the maximum theoretical rate)

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

Generalized Coordinated Multipoint Framework for 5G and Beyond

The characteristic feature of 5G is the diversity of its services for different user needs. However, the requirements for these services are competing in nature, which impresses the necessity of a coordinated and flexible network architecture. Although coordinated multipoint (CoMP) systems were primarily proposed to improve the cell edge performance in 4G, their collaborative nature can be leveraged to support the diverse requirements and enabling technologies of 5G and beyond networks. To this end, we propose generalization of CoMP to a proactive and efficient resource utilization framework capable of supporting different user requirements such as reliability, latency, throughput, and security while considering network constraints. This article elaborates on the multiple aspects, inputs, and outputs of the generalized CoMP (GCoMP) framework. Apart from user requirements, the GCoMP decision mechanism also considers the CoMP scenario and network architecture to decide upon outputs such as CoMP technique or appropriate coordinating clusters. To enable easier understanding of the concept, popular use cases, such as vehicle-to-everything (V2X) communication and eHealth, are studied. Additionally, interesting challenges and open areas in GCoMP are discussed.Comment: 11 pages, 7 figure

Quantum-aided multi-user transmission in non-orthogonal multiple access systems

With the research on implementing a universal quantum computer being under the technological spotlight, new possibilities appear for their employment in wireless communications systems for reducing their complexity and improving their performance. In this treatise, we consider the downlink of a rank-deficient, multi-user system and we propose the discrete-valued and continuous-valued Quantum-assisted Particle Swarm Optimization (QPSO) algorithms for performing Vector Perturbation (VP) precoding, as well as for lowering the required transmission power at the Base Station (BS), while minimizing the expected average Bit Error Ratio (BER) at the mobile terminals. We use the Minimum BER (MBER) criterion. We show that the novel quantum-assisted precoding methodology results in an enhanced BER performance, when compared to that of a classical methodology employing the PSO algorithm, while requiring the same computational complexity in the challenging rank-deficient scenarios, where the number of transmit antenna elements at the BS is lower than the number of users. Moreover, when there is limited Channel State Information (CSI) feedback from the users to the BS, due to the necessary quantization of the channel states, the proposed quantum-assisted precoder outperforms the classical precoder

Improving next-generation wireless network performance and reliability with deep learning

A rudimentary question whether machine learning in general, or deep learning in particular, could add to the well-established field of wireless communications, which has been evolving for close to a century, is often raised. While the use of deep learning based methods is likely to help build intelligent wireless solutions, this use becomes particularly challenging for the lower layers in the wireless communication stack. The introduction of the fifth generation of wireless communications (5G) has triggered the demand for “network intelligence” to support its promises for very high data rates and extremely low latency. Consequently, 5G wireless operators are faced with the challenges of network complexity, diversification of services, and personalized user experience. Industry standards have created enablers (such as the network data analytics function), but these enablers focus on post-mortem analysis at higher stack layers and have a periodicity in the time scale of seconds (or larger). The goal of this dissertation is to show a solution for these challenges and how a data-driven approach using deep learning could add to the field of wireless communications. In particular, I propose intelligent predictive and prescriptive abilities to boost reliability and eliminate performance bottlenecks in 5G cellular networks and beyond, show contributions that justify the value of deep learning in wireless communications across several different layers, and offer in-depth analysis and comparisons with baselines and industry standards. First, to improve multi-antenna network reliability against wireless impairments with power control and interference coordination for both packetized voice and beamformed data bearers, I propose the use of a joint beamforming, power control, and interference coordination algorithm based on deep reinforcement learning. This algorithm uses a string of bits and logic operations to enable simultaneous actions to be performed by the reinforcement learning agent. Consequently, a joint reward function is also proposed. I compare the performance of my proposed algorithm with the brute force approach and show that similar performance is achievable but with faster run-time as the number of transmit antennas increases. Second, in enhancing the performance of coordinated multipoint, I propose the use of deep learning binary classification to learn a surrogate function to trigger a second transmission stream instead of depending on the popular signal to interference plus noise measurement quantity. This surrogate function improves the users' sum-rate through focusing on pre-logarithmic terms in the sum-rate formula, which have larger impact on this rate. Third, performance of band switching can be improved without the need for a full channel estimation. My proposal of using deep learning to classify the quality of two frequency bands prior to granting the band switching leads to a significant improvement in users' throughput. This is due to the elimination of the industry standard measurement gap requirement—a period of silence where no data is sent to the users so they could measure the frequency bands before switching. In this dissertation, a group of algorithms for wireless network performance and reliability for downlink are proposed. My results show that the introduction of user coordinates enhance the accuracy of the predictions made with deep learning. Also, the choice of signal to interference plus noise ratio as the optimization objective may not always be the best choice to improve user throughput rates. Further, exploiting the spatial correlation of channels in different frequency bands can improve certain network procedures without the need for perfect knowledge of the per-band channel state information. Hence, an understanding of these results help develop novel solutions to enhancing these wireless networks at a much smaller time scale compared to the industry standards todayElectrical and Computer Engineerin

Keilaavan millimetriaaltoradiolinkin suuntaaminen ja seuraaminen

In order to provide high-throughput mobile broadband in a dense urban information society, upcoming cellular networks will finally employ the under-utilized millimeter-wave (mmW) frequencies. The challenging mmW radio environment, however, necessitates massive cell densification with wireless backhauling using very directional links. This thesis investigates how these links between access points may be aligned efficiently, and how alignment reflects the network organization. The work provides a thorough presentation of different high-level aspects and background information required when designing a mmW small cell system. In terms of alignment functionality, both automatic link establishment and proactive tracking are considered. Additionally, the presentation includes an overview of beam steerable antennas, mmW propagation in urban environments, and network organization. The thesis further specifies requirements, proposes possible approaches and compares those with existing implementations. Most of existing mmW beam alignment solutions are intended for short-range indoor communications and do not address the issues in cellular systems. While existing functionality considers only a single link between two devices, efficient design should consider both the entire network and the underlying phenomena. The devices should further exploit the existing network infrastructure, location and orientation information, and the concepts of machine learning. Even though the world has recently seen advancements in the related fields, there is still much work to be done before commercial deployment is possible.Seuraavan sukupolven matkaviestinjärjestelmien erittäin nopeissa datayhteyksissä tullaan hyödyntämään millimetriaaltoteknologiaa. Näillä taajuuksilla radioympäristö on kuitenkin hyvin haastava, mikä edellyttää verkon solutiheyden moninkertaistamista, täysin langattomia tukiasemia ja erittäin suuntaavia antenneja. Tässä diplomityössä tutkitaan eri keinoja kuinka tukiasemien väliset linkit kohdistetaan tehokkaasti, ja miten se vaikuttaa verkon rakenteeseen ja hallintaan. Työ tarjoaa kattavan taustaselvityksen mm-aaltosoluverkon toteuttamiseen tarvittavista asioista. Keilanohjausta tarkastellaan sekä verkon automaattisen laajentamisen että kohteen aktiivisen seurauksen kannalta. Tämän lisäksi työssä tutkitaan keilattavia antenneja, mm-aaltojen etenemistä kaupunkiympäristöissä ja verkkorakennetta. Näiden lisäksi työssä rajataan edellytykset, esitetään mahdollisia ratkaisuja, ja vertaillaan näitä olemassa oleviin toteutuksiin. Nykyiset keilaustoteutukset ovat pääasiassa suunniteltu lyhyen kantaman sisäyhteyksille, eivätkä siten vastaa ongelman asettelua. Aikaisempi toiminnallisuus keskittyy yhteen ainoaan linkkiin vaikka tehokas toteutus huomioisi koko järjestelmän kohdistusongelman fysikaalista perustaa unohtamatta. Verkkolaitteiden tulisi hyödyntää olemassa olevaa radioverkkoa, sekä paikka- että suuntatietoja, ja koneoppimisen keinoja. Vaikka aiheeseen liittyvä teknologia on kehittynyt viime vuosina harppauksin, mm-aaltosoluverkot ovat kaikkea muuta kuin valmiita markkinoille

Networks, Communication, and Computing Vol. 2

Networks, communications, and computing have become ubiquitous and inseparable parts of everyday life. This book is based on a Special Issue of the Algorithms journal, and it is devoted to the exploration of the many-faceted relationship of networks, communications, and computing. The included papers explore the current state-of-the-art research in these areas, with a particular interest in the interactions among the fields