401 research outputs found

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

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    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

    A survey of machine learning techniques applied to self organizing cellular networks

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    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

    Project Final Report – FREEDOM ICT-248891

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    This document is the final publishable summary report of the objective and work carried out within the European Project FREEDOM, ICT-248891.This document is the final publishable summary report of the objective and work carried out within the European Project FREEDOM, ICT-248891.Preprin

    Technical advances in the design and deployment of future heterogeneous networks

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    The trend in wireless communications systems is the enhancement of the network infrastructure with the introduction of small cells, where a specific geographical area is served by low-range, low-power access points. The result is the creation of a heterogeneous topology where macrocells coexist with a variety of small-cell types. In this editorial article we briefly summarize the recent technical advances in the design and deployment of future heterogeneous networks addressed in the papers that compose this special issue. In particular the following aspects are considered: the design of interference and radio resource management algorithms, the analysis of the energy efficiency and power control issues in heterogeneous networks, the concept of coordination in small cell networks, key backhaul aspects of HetNets, deployment issues and overall management strategies.Peer ReviewedPostprint (published version

    Recent advances in radio resource management for heterogeneous LTE/LTE-A networks

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    As heterogeneous networks (HetNets) emerge as one of the most promising developments toward realizing the target specifications of Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, radio resource management (RRM) research for such networks has, in recent times, been intensively pursued. Clearly, recent research mainly concentrates on the aspect of interference mitigation. Other RRM aspects, such as radio resource utilization, fairness, complexity, and QoS, have not been given much attention. In this paper, we aim to provide an overview of the key challenges arising from HetNets and highlight their importance. Subsequently, we present a comprehensive survey of the RRM schemes that have been studied in recent years for LTE/LTE-A HetNets, with a particular focus on those for femtocells and relay nodes. Furthermore, we classify these RRM schemes according to their underlying approaches. In addition, these RRM schemes are qualitatively analyzed and compared to each other. We also identify a number of potential research directions for future RRM development. Finally, we discuss the lack of current RRM research and the importance of multi-objective RRM studies

    Energy Efficient Mobility Management for the Macrocell – Femtocell LTE Network

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    Femtocells will play a key role in future deployments of the 3rd Generation Partnership Project (3GPP) the Long Term Evolution (LTE) system, as they are expected to enhance system capacity, and greatly improve the energy-efficiency in a cost-effective manner. Due to the short transmit-receive distance, femtocells prolong handset battery life and enhance the Quality of Service (QoS) perceived by the end users. However, large-scale femtocell deployment comprises many technical challenges, mainly including security, interference and mobility management. Under the viewpoint of energy-efficient mobility management, this chapter discusses the key features of the femtocell technology and presents a novel energy-efficient handover decision policy for the macrocell – femtocell LTE network. The proposed HO decision policy aims at reducing the transmit power of the LTE mobile terminals in a backwards compatible with the standard LTE handover decision procedure. Simulation results show that significantly lower energy and power consumption can be attained if the proposed approach is employed, at the cost of a moderately increased number of handover executions events

    Localization and mobility management in heterogeneous wireless networks with network-assistance

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    The nowadays heterogeneous wireless network (HWN) is a collection of ubiquitous wireless networking elements (WNEs) that support diverse functional capabilities and networking purposes. In such a heterogeneous networking environment, localization and mobility management will play a key role for the seamless support of emerging applications, such as social networking, massive multiplayer online gaming, device-todevice (D2D) communications, smart metering, first-responder communications, and unsupervised navigation of communication-aware robotic nodes. Since most of the existing wireless networking technologies enable the WNEs to assess their current radio status and directly (or indirectly) estimate their relative distance and angle with respect to other WNEs of the same Radio Access Technology (RAT), the integration of such information from the ubiquitous WNEs arises as a natural solution for robustly handling localization between (not necessarily homogeneous) WNEs and mobility management of moving WNEs governed by resource-constrained operation. Under the viewpoint of investigating how the utilization of such spatial information can be used to enhance the performance of localization and mobility management in the nowadays HWN, in this work we focus and contribute in the following four research areas: i) localization and peer-discovery between non-homogeneous WNEs, ii) network-assisted D2D discovery in cellular networks, iii) energy-efficient handover (HO) decision in the macrocell – femtocell network, and iv) network-assisted vertical handover decision (VHO) for the integrated cellular and WLAN heterogeneous wireless network
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