Mobility management in 5G heterogeneous networks

In recent years, mobile data traffic has increased exponentially as a result of widespread popularity and uptake of portable devices, such as smartphones, tablets and laptops. This growth has placed enormous stress on network service providers who are committed to offering the best quality of service to consumer groups. Consequently, telecommunication engineers are investigating innovative solutions to accommodate the additional load offered by growing numbers of mobile users. The fifth generation (5G) of wireless communication standard is expected to provide numerous innovative solutions to meet the growing demand of consumer groups. Accordingly the ultimate goal is to achieve several key technological milestones including up to 1000 times higher wireless area capacity and a significant cut in power consumption. Massive deployment of small cells is likely to be a key innovation in 5G, which enables frequent frequency reuse and higher data rates. Small cells, however, present a major challenge for nodes moving at vehicular speeds. This is because the smaller coverage areas of small cells result in frequent handover, which leads to lower throughput and longer delay. In this thesis, a new mobility management technique is introduced that reduces the number of handovers in a 5G heterogeneous network. This research also investigates techniques to accommodate low latency applications in nodes moving at vehicular speeds

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

Client-based and Cross-layer Optimized Flow Mobility for Android Devices in Heterogeneous Femtocell/Wi-Fi Networks*

AbstractThe number of subscribers accessing Internet resources from mobile and wireless devices has been increasing continually since i-mode, the first mobile Internet service launched in 1999. The handling and support of dramatic growth of mobile data traffic create serious challenges for the network operators. Due to the spreading of WLAN networks and the proliferation of multi-access devices, offloading from 3G to Wi-Fi seems to be a promising step towards the solution. To solve the bandwidth limitation and coverage issues in 3G/4G environments, femtocells became key players. These facts motivate the design and development of femtocell/Wi-Fi offloading schemes. Aiming to support advanced offloading in heterogeneous networks, in this paper we propose a client-based, cross-layer optimized flow mobility architecture for Android devices in femtocell/Wi-Fi access environments. The paper presents the design, implementation and evaluation details of the aforementioned mechanisms

Handoff Decision for Multi-user Multi-class Traffic in MIMO-LTE-A Networks

AbstractLTE-A networks do not have a central controlling system or node and is made up of several networking technologies. Handover is a method to assure that users can move freely within a network without losing the network connection. Thus, handoff is important in LTE-A to maintain the quality of service. But, handoffs in LTE-A face numerous issues like rapid change in network topology, failure in calls maintenance, etc. Thus, making efficient handoff decision is important. So, in this paper we develop a vertical handoff decision model on the basis of the utility model such that the handoff occurs only to the suitable cells in order to avoid any problem in maintaining the network connectivity

Mobile Networks

The growth in the use of mobile networks has come mainly with the third generation systems and voice traffic. With the current third generation and the arrival of the 4G, the number of mobile users in the world will exceed the number of landlines users. Audio and video streaming have had a significant increase, parallel to the requirements of bandwidth and quality of service demanded by those applications. Mobile networks require that the applications and protocols that have worked successfully in fixed networks can be used with the same level of quality in mobile scenarios. Until the third generation of mobile networks, the need to ensure reliable handovers was still an important issue. On the eve of a new generation of access networks (4G) and increased connectivity between networks of different characteristics commonly called hybrid (satellite, ad-hoc, sensors, wired, WIMAX, LAN, etc.), it is necessary to transfer mechanisms of mobility to future generations of networks. In order to achieve this, it is essential to carry out a comprehensive evaluation of the performance of current protocols and the diverse topologies to suit the new mobility conditions