734 research outputs found
Handover Management in Highly Dense Femtocellular Networks
For dense femtocells, intelligent integrated femtocell/macrocell network
architecture, a neighbor cell list with a minimum number of femtocells,
effective call admission control (CAC), and handover processes with proper
signaling are the open research issues. An appropriate traffic model for the
integrated femtocell/macrocell network is also not yet developed. In this
paper, we present the major issue of mobility management for the integrated
femtocell/macrocell network. We propose a novel algorithm to create a neighbor
cell list with a minimum, but appropriate, number of cells for handover. We
also propose detailed handover procedures and a novel traffic model for the
integrated femtocell/macrocell network. The proposed CAC effectively handles
various calls. The numerical and simulation results show the importance of the
integrated femtocell/macrocell network and the performance improvement of the
proposed schemes. Our proposed schemes for dense femtocells will be very
effective for those in research and industry to implement
Self-organising comprehensive handover strategy for multi-tier LTE-advanced heterogeneous networks
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
Context-Aware Handover Policies in HetNets
Next generation cellular systems are expected to entail a wide variety of wireless coverage zones, with cells of different sizes and capacities that can overlap in space and share the transmission resources. In this scenario, which is referred to as Heterogeneous Networks (HetNets), a fundamental challenge is the management of the handover process between macro, femto and pico cells. To limit the number of handovers and the signaling between the cells, it will hence be crucial to manage the user's mobility considering the context parameters, such as cells size, traffic loads, and user velocity. In this paper, we propose a theoretical model to characterize the performance of a mobile user in a HetNet scenario as a function of the user's mobility, the power profile of the neighboring cells, the handover parameters, and the traffic load of the different cells. We propose a Markov-based framework to model the handover process for the mobile user, and derive an optimal context-dependent handover criterion. The mathematical model is validated by means of simulations, comparing the performance of our strategy with conventional handover optimization techniques in different scenarios. Finally, we show the impact of the handover regulation on the users performance and how it is possible to improve the users capacity exploiting context information
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
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Radio network management in cognitive LTE-Femtocell Systems
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.There is a strong uptake of femtocell deployment as small cell application
platforms in the upcoming LTE networks. In such two-tier networks of LTEfemtocell
base stations, a large portion of the assigned spectrum is used
sporadically leading to underutilisation of valuable frequency resources.
Novel spectrum access techniques are necessary to solve these current spectrum
inefficiency problems. Therefore, spectrum management solutions should have
the features to improve spectrum access in both temporal and spatial manner.
Cognitive Radio (CR) with the Dynamic Spectrum Access (DSA) is considered
to be the key technology in this research in order to increase the spectrum
efficiency. This is an effective solution to allow a group of Secondary Users
(SUs) to share the radio spectrum initially allocated to the Primary User (PUs) at
no interference.
The core aim of this thesis is to develop new cognitive LTE-femtocell systems
that offer a 4G vision, to facilitate the radio network management in order to
increase the network capacity and further improve spectrum access probabilities.
In this thesis, a new spectrum management model for cognitive radio networks is
considered to enable a seamless integration of multi-access technology with
existing networks. This involves the design of efficient resource allocation
algorithms that are able to respond to the rapid changes in the dynamic wireless
environment and primary users activities. Throughout this thesis a variety of
network upgraded functions are developed using application simulation
scenarios. Therefore, the proposed algorithms, mechanisms, methods, and system
models are not restricted in the considered networks, but rather have a wider
applicability to be used in other technologies.
This thesis mainly investigates three aspects of research issues relating to the
efficient management of cognitive networks: First, novel spectrum resource
management modules are proposed to maximise the spectrum access by rapidly
detecting the available transmission opportunities. Secondly, a developed pilot
power controlling algorithm is introduced to minimise the power consumption by
considering mobile position and application requirements. Also, there is
investigation on the impact of deploying different numbers of femtocell base
stations in LTE domain to identify the optimum cell size for future networks.
Finally, a novel call admission control mechanism for mobility management is
proposed to support seamless handover between LTE and femtocell domains.
This is performed by assigning high speed mobile users to the LTE system to
avoid unnecessary handovers.
The proposed solutions were examined by simulation and numerical analysis to
show the strength of cognitive femtocell deployment for the required
applications. The results show that the new system design based on cognitive
radio configuration enable an efficient resource management in terms of
spectrum allocation, adaptive pilot power control, and mobile handover. The
proposed framework and algorithms offer a novel spectrum management for self organised LTE-femtocell architecture.
Eventually, this research shows that certain architectures fulfilling spectrum
management requirements are implementable in practice and display good
performance in dynamic wireless environments which recommends the
consideration of CR systems in LTE and femtocell networks
Cell Selection Mechanism Based on Q-learning Environment in Femtocell LTE-A Networks
Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased
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