Load Balancing in a Realistic Urban Scenario for LTE Networks

Rodríguez, Javier Fernández; Bandera, Isabel de la; Muñoz, Pablo; Barco, Raquel

doi:10.1109/vetecs.2011.5956149

Cited by 27 publications

(25 citation statements)

References 3 publications

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“…The authors are emphasizing the need for human intuition to solve such nonlinear problem. In [7], the authors proposed to use fuzzy logic controller (FCL) for handover parameters optimization. The controller makes use of the reference symbol received power of the serving cell and the adjacent cell.…”

Section: Literature Reviewmentioning

confidence: 99%

System Selection Algorithm for Heterogenous Radio Access Networks

Abuhaija¹

2014

IJCA

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Deployment of new radio technologies in the mobile world has been steadily gaining momentum as an alternative to meet the ever increasing demands of customers. This has especially been motivated by the existence of applications that make available to ordinary users a host of services that require higher spectral efficiency and data rates. LTE (Long Term Evolution) and LTE Advanced (LTE-A) with an enhanced air interface and optimized packet data architecture, an all IP network, is envisioned to provide enhanced data rates, reduced latency and cost efficient operations. The deployment of LTE concurrently with the existing legacy cellular systems, such as UMTS in the same cell sites, has proven to enhance the network resources available to the operators. Such deployment, however, is accompanied with many challenges such as mobility and load balancing. The diversity and availability of services which can be supported by several radio access networks, such as legacy 3G and LTE, make the management of radio access selection rather complex. In this paper, we propose a multi-criterion cell selection algorithm for multi radio environment (3G and LTE) based on service request, user profile and user equipment capabilities.

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Section: Literature Reviewmentioning

confidence: 99%

System Selection Algorithm for Heterogenous Radio Access Networks

Abuhaija¹

2014

IJCA

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“…It has been shown in [12], [18], [19] and [20] that Fuzzy Logic Controller (FLC) rules, that are optimized using Q-learning (QL), can be used for automatic network parameter optimization. FLC has the ability to model a controller as a set of 'IF-THEN' rules.…”

Section: Introductionmentioning

confidence: 99%

“…Especially in the case, when we do not have a priori knowledge about the behaviour of network Key Performance Indicators (KPIs). In [19] the concept of load-balancing using FQL is extended to LTE networks. However, the paper lacks some important details like how FQL algorithm is applied (its various problem specific components e.g., the reward/utility function e.t.c.).…”

Section: Introductionmentioning

confidence: 99%

Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters

Tiwana

2014

Automatika

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Original scientific paperWith the evolution of broadband mobile networks towards LTE and beyond, the support for the Internet and Internet based services is growing. Self Organizing Network (SON) functionalities intend to optimize the network performance for the improved user experience while at the same time reducing the network operational cost. This paper proposes a Reinforcement Learning (RL) based framework to improve throughput of the mobile users. The problem of spectral efficiency maximization is modeled as co-operative Multi-Agent control problem between the neighbouring eNodeBs (eNBs). Each eNB has an associated agent that dynamically changes the outgoing Handover Margin (HM) to its neighbouring cells. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal mobility parameter i.e., HM value. The learning framework is designed to operate in an environment with the variations in traffic, user positions and propagation conditions. Simulation results have shown the proposed approach improves the network capacity and user experiences in terms of throughput. Key words: Handover Margin, LTE, Reinforcement Learning, Fuzzy Q-Learning, SONSamoorganizirajuće mreže: Podržano učenje za optimizaciju LTE mobilnosti. Razvoj širokopojasne mobilne mreže prema LTE mrežama uvjetuje pojačani rast internetskih servisa i usluga. Samoorganizirajuće mreže namijenjene su optimizaciji performansi mreže s ciljem poboljšanja korisnikovog zadovoljstva i smanjenja troškova rada. U radu se predlaže pristup zasnovan na podržanom učenju kako bi se popravila propusnost mobilnog korisnika. Problem maksimizacije spektralne učinkovitosti modelira se kao kooperativni više agentski problem upravljanje izme u susjednihčvorova (eNBs). Svakičvor ima pridruženog agenta koji dinamički mijenja marginu primopredaje prema susjednimćelijama. Agent koristi tehniku neizrazitog Q učenja (FQL) kako bi naučio optimizirati parametre mreže. Učenje je organizirano za rad u uvjetima raznovrsnog prometa, korisničkih položaja i uvjeta propagacije. Simulacijski rezultati pokazuju kako predloženi pristup poboljšava kapacitet mreže i korisnički doživljaj u smislu propusnosti mreže.

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“…An algorithm to find the suitable handover offset value between the overloaded cell and a possible target cell was proposed. In [5], fuzzy logic controller was envisioned for optimizing handover parameters for adaptive load balancing. Hao Wang et al, proposed a load balancing algorithm that considers heterogeneous services in [6].…”

Section: Introductionmentioning

confidence: 99%

Load Balancing Algorithm based Adaptive Handovers and Target Cell Load Estimation

AliAl-Juboori¹,

AL-Dahwi²

2013

IJCA

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The purpose of load balancing in cellular network is to deal with unequal distribution of traffic load over multiple cells. Load balancing is a major issue in LTE (long term evolution) system. A load balancing problem occurs when available wireless resources are not enough to support the requirements of users. Such a problem may lead to blocked or dropped calls and degrade the quality of service. In this paper, we propose a general load -balancing algorithm to help congested cells handle traffic dynamically.

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Load Balancing in a Realistic Urban Scenario for LTE Networks

Cited by 27 publications

References 3 publications

System Selection Algorithm for Heterogenous Radio Access Networks

System Selection Algorithm for Heterogenous Radio Access Networks

Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters

Load Balancing Algorithm based Adaptive Handovers and Target Cell Load Estimation

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