Proactive TCP mechanism to improve Handover performance in Mobile Satellite and Terrestrial Networks

Emerging standardization of Geo Mobile Radio (GMR-1) for satellite system is having strong resemblance to terrestrial GSM (Global System for Mobile communications) at the upper protocol layers and TCP (Transmission Control Protocol) is one of them. This space segment technology as well as terrestrial technology, is characterized by periodic variations in communication properties and coverage causing the termination of ongoing call as connections of Mobile Nodes (MN) alter stochastically. Although provisions are made to provide efficient communication infrastructure this hybrid space and terrestrial networks must ensure the end-to-end network performance so that MN can move seamlessly among these networks. However from connectivity point of view current TCP performance has not been engineered for mobility events in multi-radio MN. Traditionally, TCP has applied a set of congestion control algorithms (slow-start, congestion avoidance, fast retransmit, fast recovery) to probe the currently available bandwidth on the connection path. These algorithms need several round-trip times to find the correct transmission rate (i.e. congestion window), and adapt to sudden changes connectivity due to handover. While there are protocols to maintain the connection continuity on mobility events, such as Mobile IP (MIP) and Host Identity Protocol (HIP), TCP performance engineering has had less attention. TCP is implemented as a separate component in an operating system, and is therefore often unaware of the mobility events or the nature of multi-radios' communication. This paper aims to improve TCP communication performance in Mobile satellite and terrestrial networks.Comment: 5 pages, 2 figure

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

Energy-efficient vertical handover parameters, classification and solutions over wireless heterogeneous networks: a comprehensive survey

In the last few decades, the popularity of wireless networks has been growing dramatically for both home and business networking. Nowadays, smart mobile devices equipped with various wireless networking interfaces are used to access the Internet, communicate, socialize and handle short or long-term businesses. As these devices rely on their limited batteries, energy-efficiency has become one of the major issues in both academia and industry. Due to terminal mobility, the variety of radio access technologies and the necessity of connecting to the Internet anytime and anywhere, energy-efficient handover process within the wireless heterogeneous networks has sparked remarkable attention in recent years. In this context, this paper first addresses the impact of specific information (local, network-assisted, QoS-related, user preferences, etc.) received remotely or locally on the energy efficiency as well as the impact of vertical handover phases, and methods. It presents energy-centric state-of-the-art vertical handover approaches and their impact on energy efficiency. The paper also discusses the recommendations on possible energy gains at different stages of the vertical handover process