Radio Resource Management Optimization For Next Generation Wireless Networks

The prominent versatility of today’s mobile broadband services and the rapid advancements in the cellular phones industry have led to a tremendous expansion in the wireless market volume. Despite the continuous progress in the radio-access technologies to cope with that expansion, many challenges still remain that need to be addressed by both the research and industrial sectors. One of the many remaining challenges is the efficient allocation and management of wireless network resources when using the latest cellular radio technologies (e.g., 4G). The importance of the problem stems from the scarcity of the wireless spectral resources, the large number of users sharing these resources, the dynamic behavior of generated traffic, and the stochastic nature of wireless channels. These limitations are further tightened as the provider’s commitment to high quality-of-service (QoS) levels especially data rate, delay and delay jitter besides the system’s spectral and energy efficiencies. In this dissertation, we strive to solve this problem by presenting novel cross-layer resource allocation schemes to address the efficient utilization of available resources versus QoS challenges using various optimization techniques. The main objective of this dissertation is to propose a new predictive resource allocation methodology using an agile ray tracing (RT) channel prediction approach. It is divided into two parts. The first part deals with the theoretical and implementational aspects of the ray tracing prediction model, and its validation. In the second part, a novel RT-based scheduling system within the evolving cloud radio access network (C-RAN) architecture is proposed. The impact of the proposed model on addressing the long term evolution (LTE) network limitations is then rigorously investigated in the form of optimization problems. The main contributions of this dissertation encompass the design of several heuristic solutions based on our novel RT-based scheduling model, developed to meet the aforementioned objectives while considering the co-existing limitations in the context of LTE networks. Both analytical and numerical methods are used within this thesis framework. Theoretical results are validated with numerical simulations. The obtained results demonstrate the effectiveness of our proposed solutions to meet the objectives subject to limitations and constraints compared to other published works

Cellular and Wi-Fi technologies evolution: from complementarity to competition

This PhD thesis has the characteristic to span over a long time because while working on it, I was working as a research engineer at CTTC with highly demanding development duties. This has delayed the deposit more than I would have liked. On the other hand, this has given me the privilege of witnessing and studying how wireless technologies have been evolving over a decade from 4G to 5G and beyond. When I started my PhD thesis, IEEE and 3GPP were defining the two main wireless technologies at the time, Wi-Fi and LTE, for covering two substantially complementary market targets. Wi-Fi was designed to operate mostly indoor, in unlicensed spectrum, and was aimed to be a simple and cheap technology. Its primary technology for coexistence was based on the assumption that the spectrum on which it was operating was for free, and so it was designed with interference avoidance through the famous CSMA/CA protocol. On the other hand, 3GPP was designing technologies for licensed spectrum, a costly kind of spectrum. As a result, LTE was designed to take the best advantage of it while providing the best QoE in mainly outdoor scenarios. The PhD thesis starts in this context and evolves with these two technologies. In the first chapters, the thesis studies radio resource management solutions for standalone operation of Wi-Fi in unlicensed and LTE in licensed spectrum. We anticipated the now fundamental machine learning trend by working on machine learning-based radio resource management solutions to improve LTE and Wi-Fi operation in their respective spectrum. We pay particular attention to small cell deployments aimed at improving the spectrum efficiency in licensed spectrum, reproducing small range scenarios typical of Wi-Fi settings. IEEE and 3GPP followed evolving the technologies over the years: Wi-Fi has grown into a much more complex and sophisticated technology, incorporating the key features of cellular technologies, like HARQ, OFDMA, MU-MIMO, MAC scheduling and spatial reuse. On the other hand, since Release 13, cellular networks have also been designed for unlicensed spectrum. As a result, the two last chapters of this thesis focus on coexistence scenarios, in which LTE needs to be designed to coexist with Wi-Fi fairly, and NR, the radio access for 5G, with Wi-Fi in 5 GHz and WiGig in 60 GHz. Unlike LTE, which was adapted to operate in unlicensed spectrum, NR-U is natively designed with this feature, including its capability to operate in unlicensed in a complete standalone fashion, a fundamental new milestone for cellular. In this context, our focus of analysis changes. We consider that these two technological families are no longer targeting complementarity but are now competing, and we claim that this will be the trend for the years to come. To enable the research in these multi-RAT scenarios, another fundamental result of this PhD thesis, besides the scientific contributions, is the release of high fidelity models for LTE and NR and their coexistence with Wi-Fi and WiGig to the ns-3 open-source community. ns-3 is a popular open-source network simulator, with the characteristic to be multi-RAT and so naturally allows the evaluation of coexistence scenarios between different technologies. These models, for which I led the development, are by academic citations, the most used open-source simulation models for LTE and NR and havereceived fundings from industry (Ubiquisys, WFA, SpiderCloud, Interdigital, Facebook) and federal agencies (NIST, LLNL) over the years.Aquesta tesi doctoral té la característica d’allargar-se durant un llarg període de temps ja que mentre treballava en ella, treballava com a enginyera investigadora a CTTC amb tasques de desenvolupament molt exigents. Això ha endarrerit el dipositar-la més del que m’hagués agradat. D’altra banda, això m’ha donat el privilegi de ser testimoni i estudiar com han evolucionat les tecnologies sense fils durant més d’una dècada des del 4G fins al 5G i més enllà. Quan vaig començar la tesi doctoral, IEEE i 3GPP estaven definint les dues tecnologies sense fils principals en aquell moment, Wi-Fi i LTE, que cobreixen dos objectius de mercat substancialment complementaris. Wi-Fi va ser dissenyat per funcionar principalment en interiors, en espectre sense llicència, i pretenia ser una tecnologia senzilla i barata. La seva tecnologia primària per a la convivència es basava en el supòsit que l’espectre en el que estava operant era de franc, i, per tant, es va dissenyar simplement evitant interferències a través del famós protocol CSMA/CA. D’altra banda, 3GPP estava dissenyant tecnologies per a espectres amb llicència, un tipus d’espectre costós. Com a resultat, LTE està dissenyat per treure’n el màxim profit alhora que proporciona el millor QoE en escenaris principalment a l’aire lliure. La tesi doctoral comença amb aquest context i evoluciona amb aquestes dues tecnologies. En els primers capítols, estudiem solucions de gestió de recursos de radio per a operacions en espectre de Wi-Fi sense llicència i LTE amb llicència. Hem anticipat l’actual tendència fonamental d’aprenentatge automàtic treballant solucions de gestió de recursos de radio basades en l’aprenentatge automàtic per millorar l’LTE i Wi-Fi en el seu espectre respectiu. Prestem especial atenció als desplegaments de cèl·lules petites destinades a millorar la eficiència d’espectre llicenciat, reproduint escenaris de petit abast típics de la configuració Wi-Fi. IEEE i 3GPP van seguir evolucionant les tecnologies al llarg dels anys: El Wi-Fi s’ha convertit en una tecnologia molt més complexa i sofisticada, incorporant les característiques clau de les tecnologies cel·lulars, com ara HARQ i la reutilització espacial. D’altra banda, des de la versió 13, també s’han dissenyat xarxes cel·lulars per a espectre sense llicència. Com a resultat, els dos darrers capítols d’aquesta tesi es centren en aquests escenaris de convivència, on s’ha de dissenyar LTE per conviure amb la Wi-Fi de manera justa, i NR, l’accés a la radio per a 5G amb Wi-Fi a 5 GHz i WiGig a 60 GHz. A diferència de LTE, que es va adaptar per funcionar en espectre sense llicència, NR-U està dissenyat de forma nativa amb aquesta característica, inclosa la seva capacitat per operar sense llicència de forma autònoma completa, una nova fita fonamental per al mòbil. En aquest context, el nostre focus d’anàlisi canvia. Considerem que aquestes dues famílies de tecnologia ja no estan orientades cap a la complementarietat, sinó que ara competeixen, i afirmem que aquesta serà el tendència per als propers anys. Per permetre la investigació en aquests escenaris multi-RAT, un altre resultat fonamental d’aquesta tesi doctoral, a més de les aportacions científiques, és l’alliberament de models d’alta fidelitat per a LTE i NR i la seva coexistència amb Wi-Fi a la comunitat de codi obert ns-3. ns-3 és un popular simulador de xarxa de codi obert, amb la característica de ser multi-RAT i, per tant, permet l’avaluació de manera natural d’escenaris de convivència entre diferents tecnologies. Aquests models, pels quals he liderat el desenvolupament, són per cites acadèmiques, els models de simulació de codi obert més utilitzats per a LTE i NR i que han rebut finançament de la indústria (Ubiquisys, WFA, SpiderCloud, Interdigital, Facebook) i agències federals (NIST, LLNL) al llarg dels anys.Esta tesis doctoral tiene la característica de extenderse durante mucho tiempo porque mientras trabajaba en ella, trabajaba como ingeniera de investigación en CTTC con tareas de desarrollo muy exigentes. Esto ha retrasado el depósito más de lo que me hubiera gustado. Por otro lado, gracias a ello, he tenido el privilegio de presenciar y estudiar como las tecnologías inalámbricas han evolucionado durante una década, de 4G a 5G y más allá. Cuando comencé mi tesis doctoral, IEEE y 3GPP estaban definiendo las dos principales tecnologías inalámbricas en ese momento, Wi-Fi y LTE, cumpliendo dos objetivos de mercado sustancialmente complementarios. Wi-Fi fue diseñado para funcionar principalmente en interiores, en un espectro sin licencia, y estaba destinado a ser una tecnología simple y barata. Su tecnología primaria para la convivencia se basaba en el supuesto en que el espectro en el que estaba operando era gratis, y así fue diseñado simplemente evitando interferencias a través del famoso protocolo CSMA/CA. Por otro lado, 3GPP estaba diseñando tecnologías para espectro con licencia, un tipo de espectro costoso. Como resultado, LTE está diseñado para aprovechar el espectro al máximo proporcionando al mismo tiempo el mejor QoE en escenarios principalmente al aire libre. La tesis doctoral parte de este contexto y evoluciona con estas dos tecnologías. En los primeros capítulos, estudiamos las soluciones de gestión de recursos de radio para operación en espectro Wi-Fi sin licencia y LTE con licencia. Anticipamos la tendencia ahora fundamental de aprendizaje automático trabajando en soluciones de gestión de recursos de radio para mejorar LTE y funcionamiento deWi-Fi en su respectivo espectro. Prestamos especial atención a las implementaciones de células pequeñas destinadas a mejorar la eficiencia de espectro licenciado, reproduciendo los típicos escenarios de rango pequeño de la configuración Wi-Fi. IEEE y 3GPP siguieron evolucionando las tecnologías a lo largo de los años: Wi-Fi se ha convertido en una tecnología mucho más compleja y sofisticada, incorporando las características clave de las tecnologías celulares, como HARQ, OFDMA, MU-MIMO, MAC scheduling y la reutilización espacial. Por otro lado, desde la Release 13, también se han diseñado redes celulares para espectro sin licencia. Como resultado, los dos últimos capítulos de esta tesis se centran en estos escenarios de convivencia, donde LTE debe diseñarse para coexistir con Wi-Fi de manera justa, y NR, el acceso por radio para 5G con Wi-Fi en 5 GHz y WiGig en 60 GHz. A diferencia de LTE, que se adaptó para operar en espectro sin licencia, NR-U está diseñado de forma nativa con esta función, incluyendo su capacidad para operar sin licencia de forma completamente independiente, un nuevo hito fundamental para los celulares. En este contexto, cambia nuestro enfoque de análisis. Consideramos que estas dos familias tecnológicas ya no tienen como objetivo la complementariedad, sino que ahora están compitiendo, y afirmamos que esta será la tendencia para los próximos años. Para permitir la investigación en estos escenarios de múltiples RAT, otro resultado fundamental de esta tesis doctoral, además de los aportes científicos, es el lanzamiento de modelos de alta fidelidad para LTE y NR y su coexistencia con Wi-Fi y WiGig a la comunidad de código abierto de ns-3. ns-3 es un simulador popular de red de código abierto, con la característica de ser multi-RAT y así, naturalmente, permite la evaluación de escenarios de convivencia entre diferentes tecnologías. Estos modelos, para los cuales lideré el desarrollo, son por citas académicas, los modelos de simulación de código abierto más utilizados para LTE y NR y han recibido fondos de la industria (Ubiquisys, WFA, SpiderCloud, Interdigital, Facebook) y agencias federales (NIST, LLNL) a lo largo de los años.Postprint (published version

Optimize Power Allocation Scheme to Maximize Sum Rate in CoMP with Limited Channel State Information

Extensive use of mobile applications throws many challenges in cellular systems like cell edge throughput, inter cell interference and spectral e�ciency. Many of these challenges have been resolved using Coordinated Multi-Point (CoMP), developed in the Third Generation Partnership Project for LTE-Advanced) to a great extent. CoMP cooperatively process signals from base sta- tions that are connected to various multiple terminals (user equipment (UEs)) at transmission and reception. This CoMP improves throughput, reduces or even removes inter-cell interference and increases spectral e�ciency in the downlink of multi-antenna coordinated multipoint systems. Many researchers addressed these issues assuming that BSs have the knowledge of the common control channels dedicated to all UEs and also about the full or partial channel state information (CSI) of all the links. From the CSI available at the BSs, multiuser interference can be managed at the BSs. To make this feasible, UEs are responsible for collecting downlink CSI. But, CSI measurement (instantaneous and/or statistical) is imperfect in nature because of the randomly varying nature of the channels at random times. These incorrect CSI values available at the BSs may, in turn, create multi-user interference. There are many techniques to suppress the multi-user interference, among which the feedback scheme is the one which is gaining a lot of attention. In feedback schemes, CSI information needs to be fed back to the base station from UEs in the uplink. It is obvious, the question arises on the type and amount of feedback need to be used. Research has been progressing in this front and some feedback techniques have been proposed. Three basic CoMP Feedback schemes are available. Explicit or statistical channel information feedback scheme in which channel information like channels's covariance matrix of the channel are shared between the transmitter and receiver. Next, implicit or statistical channel information feedback which contains information such as Channel quality indication or Precoding matrix indicator or Rank indicator. 1st applied to TDD LTE type structure and 2nd of feedback scheme can be applied in the FDD system. Finally, we have UE which tranmit the sounding reference signal (CSI). This type of feedback scheme is applied to exploit channel reciprocity and to reduce channel intercell interference and this can be applied in the TDD system. We have analyzed the scenario of LTE TDD based system. After this, optimization of power is also required because users at the cell edge required more attention than the user locating at the center of the cell. In my work, it shows estimated power gives exponential divercity for high SNR as low SNR too. In this method, a compression feedback method is analyzed to provide multi-cell spatial channel information. It improves the feedback e�ciency and throughput. The rows and columns of the channel matrix are compressed using Eigenmode of the user and codebook based scheme speci�ed in LTE speci�cation. The main drawback of this scheme is that spectral e�ciency is achieved with the cost of increased overheads for feedback and evolved NodeB (eNB). Other factor is complexity of eNodeB which is to be addressed in future work

Next-Generation Self-Organizing Networks through a Machine Learning Approach

Fecha de lectura de Tesis Doctoral: 17 Diciembre 2018.Para reducir los costes de gestión de las redes celulares, que, con el tiempo, aumentaban en complejidad, surgió el concepto de las redes autoorganizadas, o self-organizing networks (SON). Es decir, la automatización de las tareas de gestión de una red celular para disminuir los costes de infraestructura (CAPEX) y de operación (OPEX). Las tareas de las SON se dividen en tres categorías: autoconfiguración, autooptimización y autocuración. El objetivo de esta tesis es la mejora de las funciones SON a través del desarrollo y uso de herramientas de aprendizaje automático (machine learning, ML) para la gestión de la red. Por un lado, se aborda la autocuración a través de la propuesta de una novedosa herramienta para una diagnosis automática (RCA), consistente en la combinación de múltiples sistemas RCA independientes para el desarrollo de un sistema compuesto de RCA mejorado. A su vez, para aumentar la precisión de las herramientas de RCA mientras se reducen tanto el CAPEX como el OPEX, en esta tesis se proponen y evalúan herramientas de ML de reducción de dimensionalidad en combinación con herramientas de RCA. Por otro lado, en esta tesis se estudian las funcionalidades multienlace dentro de la autooptimización y se proponen técnicas para su gestión automática. En el campo de las comunicaciones mejoradas de banda ancha, se propone una herramienta para la gestión de portadoras radio, que permite la implementación de políticas del operador, mientras que, en el campo de las comunicaciones vehiculares de baja latencia, se propone un mecanismo multicamino para la redirección del tráfico a través de múltiples interfaces radio. Muchos de los métodos propuestos en esta tesis se han evaluado usando datos provenientes de redes celulares reales, lo que ha permitido demostrar su validez en entornos realistas, así como su capacidad para ser desplegados en redes móviles actuales y futuras

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

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