An enhanced weighted performance-based handover parameter optimization algorithm for LTE networks

This article introduces an enhanced version of previously developed self-optimizing algorithm that controls the handover (HO) parameters of a long-term evolution base station in order to diminish and prevent the negative effects that can be introduced by HO (radio link failures, HO failures and ping-pong HOs) and thus improve the overall network performance. The default algorithm selects the best hysteresis and time-to-trigger combination based on the current network status. The enhancement proposed here aims to maximize the gain provided by the algorithm by improving its convergence time. The effects of this enhancement have been studied in a rural scenario setting and compared to the original algorithm; the results show a clear improvement, faster convergence, and better network performance, because of the enhancement

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

Enhancing security of MME handover via fractional programming and Firefly algorithm

Key update and residence management have been investigated as an effective solution to cope with desynchronisation attacks in Mobility Management Entity (MME) handovers. In this paper, we first analyse the impacts of the Key Update Interval (KUI) and MME Residence Interval (MRI) on handover processes and their secrecy performance in terms of the Number of Exposed Packets (NEP), Signaling Overhead Rate (SOR) and Outage Probability of Vulnerability (OPV). Specifically, the bounds of the derived NEP and SOR not only capture their behaviours at the boundary of the KUI and MRI, but also show the trade-off between the NEP and SOR. Additionally, through the analysis of the OPV, it is shown that the handover security can be enhanced by shortening the KUI and the desynchonisation attacks can be avoided with high-mobility users. The above facts accordingly motivate us to propose a Multi- objective Optimisation (MO) problem to find the optimal KUI and MRI that minimise both the NEP and SOR subject to the constraint on the OPV. To this end, two scalarisation techniques are adopted to transform the proposed MO problem into single- objective optimisation problems, i.e., an achievement-function method via Fractional Programming (FP) and a weighted-sum method. Based on the derived bounds on NEP and SOR, the FP approach can be optimally solved via a simple numerical method. For the weighted-sum method, the Firefly Algorithm (FA) is utilised to find the optimal solution. The results show that both techniques can solve the proposed MO problem with a significantly reduced searching complexity compared to the conventional heuristic iterative search technique