8 research outputs found

    Context-Aware Handover Policies in HetNets

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    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

    Performance evaluation of handover in LTE-Advanced systems with pico cell Range Expansion

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    Interference management and system optimisation for Femtocells technology in LTE and future 4G/5G networks

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    Femtocells are seen to be the future of Long Term Evaluation (LTE) networks to improve the performance of indoor, outdoor and cell edge User Equipments (UEs). These small cells work efficiently in areas that suffer from high penetration loss and path-loss to improve the coverage area. It is said that 30% of total served UEs in LTE networks are vehicular, which poses challenges in LTE networks due to their high mobility, high vehicular penetration loss (VPL), high path loss and high interference. Therefore, self-optimising and dynamic solutions are required to incorporate more intelligence into the current standard of LTE system. This makes the network more adaptive, able to handle peak data demands and cope with the increasing capacity for vehicular UEs. This research has drawn a performance comparison between vehicular UEs who are served by Mobile-Femto, Fixed-Femto and eNB under different VPL scales that range between highs and lows e.g. 0dB, 25dB and 40dB. Deploying Mobile-Femto under high VPLs has improved the vehicular UE Ergodic capacity by 1% and 5% under 25dB and 40dB VPL respectively as compared to other eNB technologies. A noticeable improvement is also seen in signal strength, throughput and spectral efficiency. Furthermore, this research discusses the co-channel interference between the eNB and the Mobile-Femto as both share the same resources and bandwidth. This has created an interference issue from the downlink signals of each other to their UEs. There were no previous solutions that worked efficiently in cases where UEs and base stations are mobile. Therefore, this research has adapted an efficient frequency reuse scheme that worked dynamically over distance and achieved improved results in the signal strength and throughput of Macro and Mobile-Femto UE as compared to previous interference management schemes e.g. Fractional Frequency Reuse factor1 (NoFFR-3) and Fractional Frequency Reuse factor3 (FFR-3). Also, the achieved results show that implementing the proposed handover scheme together with the Mobile-Femto deployment has reduced the dropped calls probability by 7% and the blocked calls probability by 14% compared to the direct transmission from the eNB. Furthermore, the outage signal probabilities under different VPLs have been reduced by 1.8% and 2% when the VPLs are 25dB and 40dB respectively compared to other eNB technologies

    A Study about Heterogeneous Network Issues Management based on Enhanced Inter-cell Interference Coordination and Machine Learning Algorithms

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    Under the circumstance of fast growing demands for mobile data, Heterogeneous Networks (HetNets) has been considered as one of the key technologies to solve 1000 times mobile data challenge in the coming decade. Although the unique multi-tier topology of HetNets has achieved high spectrum efficiency and enhanced Quality of Service (QoS), it also brings a series of critical issues. In this thesis, we present an investigation on understanding the cause of HetNets challenges and provide a research on state of arts techniques to solve three major issues: interference, offloading and handover. The first issue addressed in the thesis is the cross-tier interference of HetNets. We introduce Almost Blank Subframes (ABS) to free small cell UEs from cross-tier interference, which is the key technique of enhanced Inter-Cell Interference Coordination (eICIC). Nash Bargain Solution (NBS) is applied to optimize ABS ratio and UE partition. Furthermore, we propose a power based multi-layer NBS Algorithm to obtain optimal parameters of Further enhanced Inter-cell Interference Coordination (FeICIC), which significantly improve macrocell efficiency compared to eICIC. This algorithm not only introduces dynamic power ratio but also defined opportunity cost for each layer instead of conventional zero-cost partial fairness. Simulation results show the performance of proposed algorithm may achieve up to 31.4% user throughput gain compared to eICIC and fixed power ratio FeICIC. This thesis’ second focusing issue is offloading problem of HetNets. This includes (1) UE offloading from macro cell and (2) small cell backhaul offloading. For first aspect, we have discussed the capability of machine learning algorithms tackling this challenge and propose the User-Based K-means Algorithm (UBKCA). The proposed algorithm establishes a closed loop Self-Organization system on our HetNets scenario to maintain desired offloading factor of 50%, with cell edge user factor 17.5% and CRE bias of 8dB. For second part, we further apply machine learning clustering method to establish cache system, which may achieve up to 70.27% hit-ratio and reduce request latency by 60.21% for Youtube scenario. K-Nearest Neighbouring (KNN) is then applied to predict new users’ content preference and prove our cache system’s suitability. Besides that, we have also proposed a system to predict users’ content preference even if the collected data is not complete. The third part focuses on offloading phase within HetNets. This part detailed discusses CRE’s positive effect on mitigating ping-pong handover during UE offloading, and CRE’s negative effect on increasing cross-tier interference. And then a modified Markov Chain Process is established to map the handover phases for UE to offload from macro cell to small cell and vice versa. The transition probability of MCP has considered both effects of CRE so that the optimal CRE value for HetNets can be achieved, and result for our scenario is 7dB. The combination of CRE and Handover Margin is also discussed

    Efficient Spectrum Management as an Enabler Towards 5G Cellular Systems

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    Advanced spectrum sharing and resource management techniques are needed in future wireless cellular networks to ensure high data rates to the end users. New system architec- tures will be required, taking into account aspects such as like spectrum resources availabil- ity, deployment and operational costs, as well as power consumption. Thus, it becomes key for the development of the fifth generation of cellular networks (5G) to pursue an efficient exploitation of the wireless medium, in the sense of both using advanced physical (PHY) layer techniques, and also seeking coordination among operators. In this thesis, we analyze the problem of spectrum management within the next generation of cellular networks and we propose new algorithms for spectrum sharing and for interference coordination. In the first part of the thesis, we focus on the spectrum sharing between operators. Firstly, we develop a Long Term Evolution (LTE) standard compliant simulation environ- ment extending the open-source network simulator ns3 to support multi-input multi-output (MIMO) systems and advanced beamforming systems. Then, we present a mathematical analysis for the network performance of non-orthogonal spectrum sharing, connecting it directly with the statistics of the radio channel and we develop some spectrum sharing al- gorithms considering different aspects of the operators coexistence. The analysis is further extended to the performance evaluation of more complex digital beamforming techniques developed in a multi-input-single-output (MISO) system allowing to reach a Pareto equi- librium between the operators. Finally, we consider also an orthogonal spectrum sharing scenario investigating the impact of asymmetries and dynamics of the user demands on the implementation of spectrum sharing techniques. In the second part of the thesis, we extend the concept of spectrum management to two different scenarios. In the first scenario, we consider coordination between multiple cells, e.g. coordinated multipoint (CoMP). In particular, thanks to the exploitation of digital beamforming techniques, we present a novel distributed clustering algorithm that adapts the cluster configuration according to the users distribution and the average cluster size. In the second scenario, we extend the concept of spectrum sharing to the coexistence between different communications system in order to study the feasibility of the deployment of the cellular systems within the mmWave spectrum. In particular, we analyze the impact of the novel cellular networks on the fixed satellite system (FSS). In the last part of the thesis, we focus on the mobility management of the users in a het- erogeneous network. Firstly, we focus on the average performance experienced by a mobile user while crossing a pico/femtocell, as a function of the handover parameters to provide an approximate expression of the average Shannon capacity experienced by a mobile user when crossing the femtocell. Then, we propose a Markov-based framework to model the user state during the handover process and, based on such a model, we derive an optimal context-dependent handover criterion
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