Optimization of Mobility Parameters using Fuzzy Logic and Reinforcement Learning in Self-Organizing Networks

In this thesis, several optimization techniques for next-generation wireless networks are proposed to solve different problems in the field of Self-Organizing Networks and heterogeneous networks. The common basis of these problems is that network parameters are automatically tuned to deal with the specific problem. As the set of network parameters is extremely large, this work mainly focuses on parameters involved in mobility management. In addition, the proposed self-tuning schemes are based on Fuzzy Logic Controllers (FLC), whose potential lies in the capability to express the knowledge in a similar way to the human perception and reasoning. In addition, in those cases in which a mathematical approach has been required to optimize the behavior of the FLC, the selected solution has been Reinforcement Learning, since this methodology is especially appropriate for learning from interaction, which becomes essential in complex systems such as wireless networks. Taking this into account, firstly, a new Mobility Load Balancing (MLB) scheme is proposed to solve persistent congestion problems in next-generation wireless networks, in particular, due to an uneven spatial traffic distribution, which typically leads to an inefficient usage of resources. A key feature of the proposed algorithm is that not only the parameters are optimized, but also the parameter tuning strategy. Secondly, a novel MLB algorithm for enterprise femtocells scenarios is proposed. Such scenarios are characterized by the lack of a thorough deployment of these low-cost nodes, meaning that a more efficient use of radio resources can be achieved by applying effective MLB schemes. As in the previous problem, the optimization of the self-tuning process is also studied in this case. Thirdly, a new self-tuning algorithm for Mobility Robustness Optimization (MRO) is proposed. This study includes the impact of context factors such as the system load and user speed, as well as a proposal for coordination between the designed MLB and MRO functions. Fourthly, a novel self-tuning algorithm for Traffic Steering (TS) in heterogeneous networks is proposed. The main features of the proposed algorithm are the flexibility to support different operator policies and the adaptation capability to network variations. Finally, with the aim of validating the proposed techniques, a dynamic system-level simulator for Long-Term Evolution (LTE) networks has been designed

Learning-based tracking area list management in 4G and 5G networks

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMobility management in 5G networks is a very challenging issue. It requires novel ideas and improved management so that signaling is kept minimized and far from congesting the network. Mobile networks have become massive generators of data and in the forthcoming years this data is expected to increase drastically. The use of intelligence and analytics based on big data is a good ally for operators to enhance operational efficiency and provide individualized services. This work proposes to exploit User Equipment (UE) patterns and hidden relationships from geo-spatial time series to minimize signaling due to idle mode mobility. We propose a holistic methodology to generate optimized Tracking Area Lists (TALs) in a per UE manner, considering its learned individual behavior. The k -means algorithm is proposed to find the allocation of cells into tracking areas. This is used as a basis for the TALs optimization itself, which follows a combined multi-objective and single-objective approach depending on the UE behavior. The last stage identifies UE profiles and performs the allocation of the TAL by using a neural network. The goodness of each technique has been evaluated individually and jointly under very realistic conditions and different situations. Results demonstrate important signaling reductions and good sensitivity to changing conditions.This work was supported by the Spanish National Science Council and ERFD funds under projects TEC2014-60258-C2-2-R and RTI2018-099880-B-C32.Peer ReviewedPostprint (author's final draft

Cost based optimization for strategic mobile radio access network planning using metaheuristics

La evolución experimentada por las comunicaciones móviles a lo largo de las últimas décadas ha sido motivada por dos factores principales: el surgimiento de nuevas aplicaciones y necesidades por parte del usuario, así como los avances tecnológicos. Los servicios ofrecidos para términales móviles han evolucionado desde el clásico servicio de voz y mensajes cortos (SMS), a servicios más atractivos y por lo tanto con una rápida aceptación por parte de usuario final como, video telephony, video streaming, online gaming, and the internet broadband access (MBAS). Todos estos nuevos servicios se han convertido en una realidad gracias a los avances técnologicos, avances tales como nuevas técnicas de acceso al medio compartido, nuevos esquemas de codificiación y modulación de la información intercambiada, sistemas de transmisión y recepción basados en múltiples antenas (MIMO), etc. Un aspecto importante en esta evolución fue la liberación del sector a principios de los años 90, donde la función reguladora llevado a cabo por las autoridades regulatorias nacionales (NRA) se ha antojado fundamental. Uno de los principales problemas tratados por la NRA espcífica de cada nación es la determinación de los costes por servicios mayoristas, esto es los servicios entre operadores de servicios móvilles, entre los que cabe destacar el coste por terminación de llamada o de inteconexión. El servicio de interconexión hace posible la comunicación de usuarios de diferente operadores, así como el acceso a la totalidad de servicios, incluso a aquellos no prestados por un operador en concreto gracias al uso de una red perteneciente a otro operador, por parte de todos los usuarios. El objetivo principal de esta tesis es la minimización de los costes de inversión en equipamiento de red, lo cual repercute en el establecimiento de las tarifas de interconexión como se verá a lo largo de este trabajo. La consecución de dicho objetivo se divide en dos partes: en primer lugar, el desarrollo de un conjunto de algoritmos para el dimesionado óptimo de una red de acceso radio (RAN) para un sistema de comunicaciones móvilles. En segundo lugar, el diseño y aplicación de algoritmos de optimización para la distribución óptima de los servicios sobre el conjunto de tecnologías móviles existentes (OSDP). El modulo de diseño de red proporciona cuatro algoritmos diferenciados encargados del dimensionado y planificación de la red de acceso móvil. Estos algoritmos se aplican en un entorno multi-tecnología, considerando sistemas de segunda (2G), tercera (3G) y cuarta (4G) generación, multi-usuario, teniendo en cuenta diferentes perfiles de usuarios con su respectiva carga de tráfico, y multo-servicio, incluyendo voz, servicios de datos de baja velocidad (64-144 Kbps), y acceso a internet de banda ancha móvil. La segunda parte de la tesis se encarga de distribuir de una manera óptima el conjunto de servicios sobre las tecnologías a desplegar. El objetivo de esta parte es hacer un uso eficiente de las tecnologías existentes reduciendo los costes de inversión en equipamiento de red. Esto es posible gracias a las diferencias tecnológicas existente entre los diferentes sistemas móviles, que hacen que los sistemas de segunda generación sean adecuados para proporcionar el servicio de voz y mensajería corta, mientras que redes de tercera generación muestran un mejor rendimiento en la transmisión de servicios de datos. Por último, el servicio de banda ancha móvil es nativo de redes de última generadón, como High Speed Data Acces (HSPA) y 4G. Ambos módulos han sido aplicados a un extenso conjunto de experimentos para el desarrollo de análisis tecno-económicos tales como el estudio del rendimiento de las tecnologías de HSPA y 4G para la prestación del servicio de banda ancha móvil, así como el análisis de escenarios reales de despliegue para redes 4G que tendrán lugar a partir del próximo año coinicidiendo con la licitación de las frecuencias en la banda de 800 MHz. Así mismo, se ha llevado a cabo un estudio sobre el despliegue de redes de 4G en las bandas de 800 MHz, 1800 MHz y 2600 MHz, comparando los costes de inversión obtenidos tras la optimización. En todos los casos se ha demostrado la mejora, en términos de costes de inversión, obtenida tras la aplicación de ambos módulos, posibilitando una reducción en la determinación de los costes de provisión de servicios. Los estudios realizados en esta tesis se centran en la nación de España, sin embargo todos los algoritmos implementados son aplicables a cualquier otro país europeo, prueba de ello es que los algoritmos de diseño de red han sido utilizados en diversos proyectos de regulación

Cost based optimization for strategic mobile radio access network planning using metaheuristics

La evolución experimentada por las comunicaciones móviles a lo largo de las últimas décadas ha sido motivada por dos factores principales: el surgimiento de nuevas aplicaciones y necesidades por parte del usuario, así como los avances tecnológicos. Los servicios ofrecidos para términales móviles han evolucionado desde el clásico servicio de voz y mensajes cortos (SMS), a servicios más atractivos y por lo tanto con una rápida aceptación por parte de usuario final como, video telephony, video streaming, online gaming, and the internet broadband access (MBAS). Todos estos nuevos servicios se han convertido en una realidad gracias a los avances técnologicos, avances tales como nuevas técnicas de acceso al medio compartido, nuevos esquemas de codificiación y modulación de la información intercambiada, sistemas de transmisión y recepción basados en múltiples antenas (MIMO), etc. Un aspecto importante en esta evolución fue la liberación del sector a principios de los años 90, donde la función reguladora llevado a cabo por las autoridades regulatorias nacionales (NRA) se ha antojado fundamental. Uno de los principales problemas tratados por la NRA espcífica de cada nación es la determinación de los costes por servicios mayoristas, esto es los servicios entre operadores de servicios móvilles, entre los que cabe destacar el coste por terminación de llamada o de inteconexión. El servicio de interconexión hace posible la comunicación de usuarios de diferente operadores, así como el acceso a la totalidad de servicios, incluso a aquellos no prestados por un operador en concreto gracias al uso de una red perteneciente a otro operador, por parte de todos los usuarios. El objetivo principal de esta tesis es la minimización de los costes de inversión en equipamiento de red, lo cual repercute en el establecimiento de las tarifas de interconexión como se verá a lo largo de este trabajo. La consecución de dicho objetivo se divide en dos partes: en primer lugar, el desarrollo de un conjunto de algoritmos para el dimesionado óptimo de una red de acceso radio (RAN) para un sistema de comunicaciones móvilles. En segundo lugar, el diseño y aplicación de algoritmos de optimización para la distribución óptima de los servicios sobre el conjunto de tecnologías móviles existentes (OSDP). El modulo de diseño de red proporciona cuatro algoritmos diferenciados encargados del dimensionado y planificación de la red de acceso móvil. Estos algoritmos se aplican en un entorno multi-tecnología, considerando sistemas de segunda (2G), tercera (3G) y cuarta (4G) generación, multi-usuario, teniendo en cuenta diferentes perfiles de usuarios con su respectiva carga de tráfico, y multo-servicio, incluyendo voz, servicios de datos de baja velocidad (64-144 Kbps), y acceso a internet de banda ancha móvil. La segunda parte de la tesis se encarga de distribuir de una manera óptima el conjunto de servicios sobre las tecnologías a desplegar. El objetivo de esta parte es hacer un uso eficiente de las tecnologías existentes reduciendo los costes de inversión en equipamiento de red. Esto es posible gracias a las diferencias tecnológicas existente entre los diferentes sistemas móviles, que hacen que los sistemas de segunda generación sean adecuados para proporcionar el servicio de voz y mensajería corta, mientras que redes de tercera generación muestran un mejor rendimiento en la transmisión de servicios de datos. Por último, el servicio de banda ancha móvil es nativo de redes de última generadón, como High Speed Data Acces (HSPA) y 4G. Ambos módulos han sido aplicados a un extenso conjunto de experimentos para el desarrollo de análisis tecno-económicos tales como el estudio del rendimiento de las tecnologías de HSPA y 4G para la prestación del servicio de banda ancha móvil, así como el análisis de escenarios reales de despliegue para redes 4G que tendrán lugar a partir del próximo año coinicidiendo con la licitación de las frecuencias en la banda de 800 MHz. Así mismo, se ha llevado a cabo un estudio sobre el despliegue de redes de 4G en las bandas de 800 MHz, 1800 MHz y 2600 MHz, comparando los costes de inversión obtenidos tras la optimización. En todos los casos se ha demostrado la mejora, en términos de costes de inversión, obtenida tras la aplicación de ambos módulos, posibilitando una reducción en la determinación de los costes de provisión de servicios. Los estudios realizados en esta tesis se centran en la nación de España, sin embargo todos los algoritmos implementados son aplicables a cualquier otro país europeo, prueba de ello es que los algoritmos de diseño de red han sido utilizados en diversos proyectos de regulación

Cognitive radio systems in LTE networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.The most important fact in the mobile industry at the moment is that demand for wireless services will continue to expand in the coming years. Therefore, it is vital to find more spectrums through cognitive radios for the growing numbers of services and users. However, the spectrum reallocations, enhanced receivers, shared use, or secondary markets-will not likely, by themselves or in combination, meet the real exponential increases in demand for wireless resources. Network operators will also need to re-examine network architecture, and consider integrating the fibre and wireless networks to address this issue. This thesis involves driving fibre deeper into cognitive networks, deploying microcells connected through fibre infrastructure to the backbone LTE networks, and developing the algorithms for diverting calls between the wireless and fibre systems, introducing new coexistence models, and mobility management. This research addresses the network deployment scenarios to a microcell-aided cognitive network, specifically slicing the spectrum spatially and providing reliable coverage at either tier. The goal of this research is to propose new method of decentralized-to-distributed management techniques that overcomes the spectrum unavailability barrier overhead in ongoing and future deployments of multi-tiered cognitive network architectures. Such adjustments will propose new opportunities in cognitive radio-to-fibre systematic investment strategies. Specific contributions include: 1) Identifying the radio access technologies and radio over fibre solution for cognitive network infrastructure to increase the uplink capacity analysis in two-tier networks. 2) Coexistence of macro and microcells are studied to propose a roadmap for optimising the deployment of cognitive microcells inside LTE macrocells in the case of considering radio over fibre access systems. 3) New method for roaming mobiles moving between microcells and macrocell coverage areas is proposed for managing spectrum handover, operator database, authentication and accounting by introducing the channel assigning agent entity. The ultimate goal is to reduce unnecessary channel adaptation

Neural network design for intelligent mobile network optimisation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe mobile networks users’ demands for data services are increasing exponentially, this is due to two main factors: the first is the evolution of smart phones and their application, and the second is the emerging new technologies for internet of things, smart cities…etc, which keeps pumping more data into the network; ‘though most of the data routed in the current mobile network is non-live data’. This increasing of demands arise the necessity for the mobile network operators to keep improving their network to satisfy it, this improvement takes place via adding hardware or increasing the resources or a combination of both. The radio resources are strictly limited due to spectrum licensing and availability, therefore efficient spectrum utilization is a major goal to be achieved for both network operators and developers. Simultaneous and multiple channel access,and adding more cells to the network are ways used to increase the data exchanged between the network nodes. The current 4G mobile system is based on the Orthogonal Frequency Division Multiple Access (OFDMA) for accessing the medium and the intercell interference degrades the link quality at the cell edge, with the introduction of heterogeneity concept to the LTE in Release 10 of the 3GPP the handover process became even more complex. To mitigate the intercell interference at the cell edge, coordinated multipoint and carrier aggregation techniques are utilized for dual connectivity. This work is focused on designing and proposing enhancing features to improve network performance and sustainability, these features comprises of distributing small cells for data only transmission, handover schemes performance evaluation at cell edge with dual connectivity, and Artificial Intelligence technology for balancing and prediction. In the proposed model design the data and controls of the Small eNodeB (SeNodeB) are processed at the network edge using a Mobile Edge Computing (MEC) server and the SeNodeBs are used to boost services provided to the users, also the concept of caching data has been investigated, the caching units where implemented in different network levels. The proposed system and resource management are simulated using the OPNET modeller and evaluated through multiple scenarios with and without full load, the UE is reconfigured to accommodate dual connectivity and have two separate connections for uplink and downlink, while maintaining connection to the Macro cell via uplink, the downlink is dedicated for small cells when content is requested from the cache. The results clearly show that the proposed system can decrease the latency while the total throughput delivered by the network has highly improved when SeNodeBs are deployed in the system, rising throughput will incur the rise of overall capacity which leads to better services being provided to the users or more users to join and benefit from the network. Handover improvement is also considered in this work, with the help of two Artificial Intelligence (AI) entities better handover performance are achieved. Balanced load over the SeNodeBs results in less frequent handover, the proposed load balancer is based on artificial neural network clustering model with self-organizing map as a hidden layer, it’s trained to forecast the network condition and learn to reduce the number of handovers especially for the UEs at the cell edge by performing only necessary ones, and avoid handovers to the Macro cell for the downlink direction. The examined handovers concern the downlinks when routing non live video stored at the small cell’s cache, and a reduction in the frequent handovers was achieved when running the balancer. Keep revolving in the handover orbit, another way to preserve and utilize network resources is by predicting the handovers before they occur, and allocate the required data in the target SeNodeB, the predictor entity in the proposed system architecture combines the features of Radial Basis Function Neural Network and neural network time series tool to create and update prediction list from the system’s collected data and learn to predict the next SeNodeB to associate with. The prediction entity is simulated using MATLAB, and the results shows that the system was able to deliver up to 92% correct predictions for handovers which led to overall throughput improvement of 75%

Integration of a genetic optimisation algorithm in a simulation framework for optimising femtocell networks.

The developments in mobile communication systems from 1G to 4G have increased demands on the network due to the increased number of devices and increasing volume of data and 5G is expected to significantly increase demands further. Therefore, networks need to be more efficient to deliver the expected increase in volume. An energy and cost efficient way to cope with such an anticipated increase in the demand of voice and data is the dense deployment of small cells i.e. femtocells. Femtocells are identified as a crucial way to the delivery of the increased demands for heterogeneous networks in which macrocells work in combination with femtocells to provide coverage to offices, homes and enterprise. A survey of the literature is conducted to examine the mechanisms and approaches different authors have used to optimise the network. One of the major activities in this project before the transfer was the identification of the parameters. The literature was analysed and key performance parameters were identified. Based on the identified key performance parameters, a simulation framework is used to perform the experiments and to analyse the performance of a two-tier LTE-A system having femtocell overlays. A comprehensive and easy to use graphical user interface has been set up with the desired two- tier network topologies. It estimates the throughput and path loss of all the femto and macro users for all the supported bandwidths of an LTE-A system using different modulation schemes. A series of tests are carried out using the described simulation framework for a range of scenarios. The modulation scheme that yield highest throughput for a femtocell user is identified, and path loss is found to be independent from the modulation scheme but is dependent on the distance from its base station. In another series of experiments, the effects that walls inside buildings have on connectivity are examined and positioning of the femtocells is changed for each scenario inside buildings to analyse the performance. These results are used to find the optimised location of femtocells in different room layouts of the building. The simulation framework is further developed to be able to optimise the whole femtocell network by finding the optimised positioning of femtocells using the genetic optimisation algorithm. The end user can provide the inputs of the desired network topology to the simulation framework through a graphical user interface. The throughput and path loss of all the femto users are calculated before and after optimisation. The simulation results are generated in the form of tables before and after optimisation for comparison and analysis. The layouts depicting the indoor environment of the building before and after optimisation can be seen and analysed through the graphical user interface developed as a part of this simulation framework. Two case studies are defined and described to test the capacity and capability of the developed simulation framework and to show how the simulation framework can be used to identify the optimum positions of the femtocells under different configurations of room designs and number of users that represent contrasting loads on the network. Any desired network topology can be created and analysed on the basis of throughput and path loss by using this simulation framework to optimise the femtocell networks in an indoor environment of the building. The results of the experiments are compared against the claims in other published research

Journal of Telecommunications and Information Technology, 2018, nr 2

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