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

Technical considerations towards mobile user QoE enhancement via Cloud interaction

This paper discusses technical considerations of a Cloud infrastructure which interacts with mobile devices in order to migrate part of the computational overhead from the mobile device to the Cloud. The aim of the interaction between the mobile device and the Cloud is the enhancement of parameters that affect the Quality of Experience (QoE) of the mobile end user through the offloading of computational aspects of demanding applications. This paper shows that mobile user’s QoE can be potentially enhanced by offloading computational tasks to the Cloud which incorporates a predictive context-aware mechanism to schedule delivery of content to the mobile end-user using a low-cost interaction model between the Cloud and the mobile user. With respect to the proposed enhancements, both the technical considerations of the cloud infrastructure are examined, as well as the interaction between the mobile device and the Cloud

Design and analysis of a beacon-less routing protocol for large volume content dissemination in vehicular ad hoc networks

Largevolumecontentdisseminationispursuedbythegrowingnumberofhighquality applications for Vehicular Ad hoc NETworks(VANETs), e.g., the live road surveillance service and the video-based overtaking assistant service. For the highly dynamical vehicular network topology, beacon-less routing protocols have been proven to be efficient in achieving a balance between the system performance and the control overhead. However, to the authors’ best knowledge, the routing design for large volume content has not been well considered in the previous work, which will introduce new challenges, e.g., the enhanced connectivity requirement for a radio link. In this paper, a link Lifetime-aware Beacon-less Routing Protocol (LBRP) is designed for large volume content delivery in VANETs. Each vehicle makes the forwarding decision based on the message header information and its current state, including the speed and position information. A semi-Markov process analytical model is proposed to evaluate the expected delay in constructing one routing path for LBRP. Simulations show that the proposed LBRP scheme outperforms the traditional dissemination protocols in providing a low end-to-end delay. The analytical model is shown to exhibit a good match on the delay estimation with Monte Carlo simulations, as well