A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Algoritmos de transferência de redes LTE em meios de transporte massivo

Handover in LTE occurs when a device moves from the cell coverage serving it towards another; a process where the user established session must not be interrupted due to this cell change. Handovers in LTE are classified as hard ones, since the link with the serving cell is interrupted before establishing the new link with the target cell. This entails a larger failure risk and, consequently, a potential deterioration in the quality of service. This article presents a review of the handover algorithms in LTE, focusing on the ones oriented to massive means of transport. We show how the new algorithms offer a larger success in handovers, increasing the networkdata rate. This indicates that factors such as speed, position, and direction should be included in the algorithms to improve the handover in means of transport. We also present the algorithms focused on mobile relays such as an important study field for future research works.El traspaso en LTE se presenta cuando un equipo pasa de la cobertura de una celda a la de otra, un proceso en el que se debe asegurar que el usuario no vea interrumpida su sesión, como efecto de ese cambio de celda. Los traspasos en LTE son del tipo duro, en ellos, el enlace con la celda servidora se interrumpe antes de establecer el nuevo enlace con la celda destino, lo que conlleva a un mayor riesgo de falla y con ello a un probable deterioro de la calidad del servicio al usuario. Este artículo revisa algoritmos de traspaso LTE, enfocándose en aquellos orientados a medios de trasporte masivo. Muestra cómo los nuevos algoritmos ofrecen una tasa mayor de traspasos exitosos y con ello una mejor tasa de transferencia de datos; evidencia que factores como la velocidad, la posición y la dirección deben ser incluidos en los algoritmos dirigidos a mejorar el traspaso en medios de transporte; y presenta a los algoritmos enfocados en relays móviles, como un importante campo de estudio para futuras investigaciones.A transferência em LTE ocorre quando um dispositivo passa da cobertura de uma célula para outra, um processo no qual deve ser assegurado que o usuário não veja sua sessão interrompida, como resultado dessa mudança de célula. As transferências em LTE são do tipo duro, nelas, o link com a célula do servidor é interrompido antes de se estabelecer o novo link com a célula alvo, o que leva a um maior risco de falha e, portanto, a uma provável deterioração da qualidade do serviço ao usuário. Este artigo revisa os algoritmos de transferência LTE, com foco naqueles orientados a meios de transporte massivo. Mostra como os novos algoritmos oferecem uma taxa maior de transferências bem-sucedidas e, com isso, uma melhor taxa de transferência de dados; evidencia de que fatores como a velocidade, a posição e a direção devem ser incluídos nos algoritmos que visam melhorar a transferência nos meios de transporte; e apresenta os algoritmos focados em relés móveis, como um importante campo de estudo para futuras pesquisas

Regressive Prediction Approach to Vertical Handover in Fourth Generation Wireless Networks

The over increasing demand for deployment of wireless access networks has made wireless mobile devices to face so many challenges in choosing the best suitable network from a set of available access networks. Some of the weighty issues in 4G wireless networks are fastness and seamlessness in handover process. This paper therefore, proposes a handover technique based on movement prediction in wireless mobile (WiMAX and LTE-A) environment. The technique enables the system to predict signal quality between the UE and Radio Base Stations (RBS)/Access Points (APs) in two different networks. Prediction is achieved by employing the Markov Decision Process Model (MDPM) where the movement of the UE is dynamically estimated and averaged to keep track of the signal strength of mobile users. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency can be reduced. The performances of various handover approaches influenced by different metrics (mobility velocities) were evaluated. The results presented demonstrate good accuracy the proposed method was able to achieve in predicting the next signal level by reducing the total handover latency

Quick Handover in 5G for High Speed Railways and Highways Using Forward Handover and PN Sequence Detection

The cellular users, on high speed railways andhighways, travel at a very high speed and follow a nearly straightpath, in general. Thus, they typically undergo a maximumfrequency of handovers in the cellular environment. This requiresa very fast triggering of the handover. In the existing method ofhandover in 5G cellular communication, for high speed users,neither the decision-making of handover nor the triggering ofhandover is sufficiently fast. This can lead to poor signal qualityand packet losses and in the worst case, radio link failure (RLF)during a handover. This paper proposes a forward handover basedmethod, combined with PN sequence detections, to facilitate aquicker handover for high speed users on railways and highways.The proposed method adds some complexity but can offer asignificant improvement in the overall handover delay. A simplisticsimulation is used to demonstrate the improvement of the proposedmethod

Cell identity allocation and optimisation of handover parameters in self-organised LTE femtocell networks

A thesis submitted to the University of Bedfordshire in partial ful lment of the requirements for the degree of Doctor of PhilosophyFemtocell is a small cellular base station used by operators to extend indoor service coverage and enhance overall network performance. In Long Term Evolution (LTE), femtocell works under macrocell coverage and combines with the macrocell to constitute the two-tier network. Compared to the traditional single-tier network, the two-tier scenario creates many new challenges, which lead to the 3rd Generation Partnership Project (3GPP) implementing an automation technology called Self-Organising Network (SON) in order to achieve lower cost and enhanced network performance. This thesis focuses on the inbound and outbound handovers (handover between femtocell and macrocell); in detail, it provides suitable solutions for the intensity of femtocell handover prediction, Physical Cell Identity (PCI) allocation and handover triggering parameter optimisation. Moreover, those solutions are implemented in the structure of SON. In order to e ciently manage radio resource allocation, this research investigates the conventional UE-based prediction model and proposes a cell-based prediction model to predict the intensity of a femtocell's handover, which overcomes the drawbacks of the conventional models in the two-tier scenario. Then, the predictor is used in the proposed dynamic group PCI allocation approach in order to solve the problem of PCI allocation for the femtocells. In addition, based on SON, this approach is implemented in the structure of a centralised Automated Con guration of Physical Cell Identity (ACPCI). It overcomes the drawbacks of the conventional method by reducing inbound handover failure of Cell Global Identity (CGI). This thesis also tackles optimisation of the handover triggering parameters to minimise handover failure. A dynamic hysteresis-adjusting approach for each User Equipment (UE) is proposed, using received average Reference Signal-Signal to Interference plus Noise Ratio (RS-SINR) of the UE as a criterion. Furthermore, based on SON, this approach is implemented in the structure of hybrid Mobility Robustness Optimisation (MRO). It is able to off er the unique optimised hysteresis value to the individual UE in the network. In order to evaluate the performance of the proposed approach against existing methods, a System Level Simulation (SLS) tool, provided by the Centre for Wireless Network Design (CWiND) research group, is utilised, which models the structure of two-tier communication of LTE femtocell-based networks