Particle swarm optimization for Mobility Load Balancing SON in LTE networks

Altman, Zwi; Sallem, Soumaya; Nasri, Ridha; Sayraç, Berna; Clerc, Maurice

doi:10.1109/wcncw.2014.6934881

Cited by 9 publications

(11 citation statements)

References 11 publications

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“…This would allow load balancing mechanisms to be implemented early, thus resulting in a reduction in latency. A wide range of approaches has been used for load balancing; the reader is referred to a few [70][71][72].…”

Section: Deep Learning-based Mobility Load Balancingmentioning

confidence: 99%

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

et al. 2019

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In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of autoencoders (AE) for cell outage detection. First, we briefly introduce deep learning (DL) and also shed light on why it is a promising technique to make self organizing networks intelligent, cognitive, and intuitive so that they behave as fully self-configured, self-optimized, and self-healed cellular networks. The concept of SON is then explained with applications of intrusion detection and mobility load balancing. Our empirical study presents a framework for cell outage detection based on an autoencoder using simulated data obtained from a SON simulator. Finally, we provide a comparative analysis of the proposed framework with the existing frameworks.

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Section: Deep Learning-based Mobility Load Balancingmentioning

confidence: 99%

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

et al. 2019

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“…If we denote CIO between a cell and a cell by CIO , , by reducing CIO , , the coverage of a cell is reduced while that of cell increases correspondingly. Various methods are proposed to adjust CIOs between a congested cell and its neighboring cells [13,14].…”

Section: Introductionmentioning

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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“…From that research also shown that PRB utilization is not a sufficient indicator of cell load for MLB, even in the case of constant bit rate traffic. The method in [5] combines apriori expert knowledge with Multi-Objective Particle Swarm Optimization (MO-PSO), which allows to considerably reduce the search space and the computational time required for executing the MLB algorithm, this paper use offset parameters as its primary MLB consideration variable.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of this paper is to give literature contribution in LTE predictive MLB method based on previous research [3][4][5][6][7]. RSRQ is one of LTE measurement parameters that will be analyzed as the primary parameter because its formula ingredients consist of SINR and network load value ( ) , this presented in equation 7.…”

Section: Introductionmentioning

confidence: 99%

Analysis Of User Throughput Based Predictive Mobility Load Balancing Using Logarithmic Regression Of Reference Signal Received Quality In Long Term Evolution Radio Access Network

Danisya¹,

Munadi

Hertiana

2018

INFOTEL

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The improvement of Long Term Evolution (LTE) radio access network services is affecting the increased value of traffic load in its network, which is causing traffic unbalance between cells in LTE Radio Access Network (RAN). Users will be served with ineffective resource block allocation which will make the total of gained throughput are not optimal. A method is required to move network load from overloaded cells to underloaded cells in order to balance the resource block allocation optimally. By using NS-3.26 simulation, User Throughput Based (UTB) predictive Mobility Load Balancing (MLB) method is tested with RandomWalkMobilityModel for each user. This method produces an improvement of 2,29 % in average of total throughput of 63,33 % successful optimization.

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Particle swarm optimization for Mobility Load Balancing SON in LTE networks

Cited by 9 publications

References 11 publications

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

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

Analysis Of User Throughput Based Predictive Mobility Load Balancing Using Logarithmic Regression Of Reference Signal Received Quality In Long Term Evolution Radio Access Network

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