Self-organising comprehensive handover strategy for multi-tier LTE-advanced heterogeneous networks

Long term evolution (LTE)-advanced was introduced as real fourth generation (4G) with its new features and additional functions, satisfying the growing demands of quality and network coverage for the network operators' subscribers. The term muti-tier has also been recently used with respect to the heterogeneity of the network by applying the various subnetwork cooperative systems and functionalities with self-organising capabilities. Using indoor short-range low-power cellular base stations, for example, femtocells, in cooperation with existing long-range macrocells are considered as the key technical challenge of this multi-tier configuration. Furthermore, shortage of network spectrum is a major concern for network operators which forces them to spend additional attentions to overcome the degradation in performance and quality of services in 4G HetNets. This study investigates handover between the different layers of a heterogeneous LTE-advanced system, as a critical attribute to plan the best way of interactive coordination within the network for the proposed HetNet. The proposed comprehensive handover algorithm takes multiple factors in both handover sensing and decision stages, based on signal power reception, resource availability and handover optimisation, as well as prioritisation among macro and femto stations, to obtain maximum signal quality while avoiding unnecessary handovers

Context-Aware Handover Policies in HetNets

Next generation cellular systems are expected to entail a wide variety of wireless coverage zones, with cells of different sizes and capacities that can overlap in space and share the transmission resources. In this scenario, which is referred to as Heterogeneous Networks (HetNets), a fundamental challenge is the management of the handover process between macro, femto and pico cells. To limit the number of handovers and the signaling between the cells, it will hence be crucial to manage the user's mobility considering the context parameters, such as cells size, traffic loads, and user velocity. In this paper, we propose a theoretical model to characterize the performance of a mobile user in a HetNet scenario as a function of the user's mobility, the power profile of the neighboring cells, the handover parameters, and the traffic load of the different cells. We propose a Markov-based framework to model the handover process for the mobile user, and derive an optimal context-dependent handover criterion. The mathematical model is validated by means of simulations, comparing the performance of our strategy with conventional handover optimization techniques in different scenarios. Finally, we show the impact of the handover regulation on the users performance and how it is possible to improve the users capacity exploiting context information

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