Enhanced Mobility Load Balancing Optimisation in LTE

Szilágyi, Péter; Vincze, Zoltán; Vulkan, Csaba

doi:10.1109/pimrc.2012.6362930

Cited by 18 publications

(6 citation statements)

References 6 publications

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“…The load of a cell can be defined in a few ways [33,34]. However, to focus on the cell reselection process, we use the intuitive definition of a cell load which is the RB utilization ratio.…”

Section: System Modelmentioning

confidence: 99%

UE‐Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

Park

2018

Wireless Communications and Mobile Computing

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A user changes its serving cell if the quality of experience (QoE) provided by the current serving cell is not satisfactory. Since users reselect cells to increase their QoEs selfishly, the system resource efficiency can be deteriorated and a system can be unstable if users are not driven to cooperate appropriately. In this paper, inspired by the minority game (MG) model, we design a UE-initiated cell reselection policy. The MG has a salient characteristic that the number of players who win the game converges to a prespecified value even though players act selfishly without knowing the actions taken by the other players. Using the MG model, we devise a rule by which each UE plays a cell reselection game. We also design a criterion that a system controller uses to determine the result of a game and public information sent by a system controller to induce implicit cooperation among UEs. The simulation results show that compared with noncooperative method the proposed method increases not only the system performance, such as cell load balance index and system utility, but also the performance of UEs in terms of a downlink data rate and an outage probability received from a system.

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Section: System Modelmentioning

confidence: 99%

UE‐Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

Park

2018

Wireless Communications and Mobile Computing

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“…This is achieved by partially distributing the traffic of highly loaded cells to less heavily loaded neighbors cells to make better usage of the radio resources [17] [18]. The 3GPP documents define only the framework of LB; design and implementation details are left open [19].…”

Section: Mobility Load Balancingmentioning

confidence: 99%

Mobility robustness self-organizing network handover scheme for LTE-Advanced

Shukry

Fahmy

2016

2016 33rd National Radio Science Conference (NRSC)

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In this paper, we present a new adaptive handover algorithm for LTE-Advanced system. This algorithm ensures the User Equipment (UE) requirements and the Quality of Service (QoS) expectations. The proposed algorithm relies on the dynamic adjustment of the handover parameters settings based on the velocity of the UE. In order to reduce the conflict between mobility robustness optimization and load balancing the handover decision takes into consideration the channel conditions and the load of the neighbor Evolved Nodes B (eNBs). The simulation results show the superiority of our proposed algorithm in terms of throughput and handover failure rate.

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“…Existing studies on mobility load balancing adopts cell-specific handover parameters, e.g. handover margin, for distributing the traffic load from one cell to another [5][6][7][8][9][10][11][12]. However, the cell-specific handover parameter control may incur unnecessary handover for some mobile stations, reducing the effect of load balancing.…”

Section: Introductionmentioning

confidence: 99%

A distributed implementation of mobility load balancing with power control and cell reselection in 4G network

Kang

2014

Int J Network Mgmt

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SUMMARY We consider a self‐organizing network (SON) capability of mobility load balancing in a 4G network, which determines the transmission power level for individual base stations and cell reselection for individual mobile stations such that the network‐wide load is minimized while satisfying the minimum signal‐to‐noise and interference ratio (SINR) requirement of individual users. Both centralized and distributed schemes are proposed. The centralized scheme is based on the greedy algorithm, serving as a performance bound to the distributed scheme. The distributed scheme is to solve the system‐wide optimization problem in the flat network model, i.e. no central control node. Furthermore, it requires relatively low inter‐cell exchange information among neighboring cells over an inter‐cell channel, e.g. X2 interface in the LTE network. The proposed design objective is to minimize the number of mobile users that do not satisfy the specified average throughput, while distributing the user traffic load as uniformly as possible among the neighboring cells. Our simulation results for a uniform user distribution demonstrate that the proposed scheme can achieve up to almost 80% of a load balancing gain that has been achieved by a greedy algorithm in the centralized optimization. Copyright © 2014 John Wiley & Sons, Ltd.

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Enhanced Mobility Load Balancing Optimisation in LTE

Cited by 18 publications

References 6 publications

UE‐Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

UE‐Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

Mobility robustness self-organizing network handover scheme for LTE-Advanced

A distributed implementation of mobility load balancing with power control and cell reselection in 4G network

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