A Self-Organized Network for Load Balancing Using Intelligent Distributed Antenna System [4pt]Un réseau auto-organisé pour l’équilibrage de charge utilisant un système intelligent d’antennes distribuéesby Seyed Amin Hejazi, Shawn P. Stapleton

Can. J. Electr. Comput. Eng.

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Year
2015
DOI
10.1109/CJECE.2014.2366454
Subject
Hardware and Architecture / Electrical and Electronic Engineering

Text

CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING, VOL. 38, NO. 2, SPRING 2015 89

A Self-Organized Network for Load Balancing

Using Intelligent Distributed Antenna System

Un réseau auto-organisé pour l’équilibrage de charge utilisant un système intelligent d’antennes distribuées

Seyed Amin Hejazi and Shawn P. Stapleton

Abstract— A high call-blocking rate is a consequence of an inefficient utilization of system resources, which is often caused by a load imbalance in the network. Load imbalances are common in wireless networks with a large number of cellular users. This paper investigates a load-balancing scheme for mobile networks that optimizes cellular performance with constraints of physical resource limits and users’ quality of service demands.

In order to efficiently utilize the system resources, an intelligent distributed antenna system (IDAS) fed by a multibase transceiver station (BTS) has the ability to distribute the system resources over a given geographic area. To enable load balancing among distributed antenna modules, we dynamically allocate the remote antenna modules to the BTSs using an intelligent algorithm. A self-organized network (SON) for an IDAS is formulated as an integer-based linear-constrained optimization problem, which tries to balance the load among the BTSs.

An estimation distribution algorithm (EDA) as an evolutionary algorithm is proposed to solve the optimization problem. The computational results of the EDA algorithm demonstrate optimum performance for small-scale networks and near-optimum performance for large-scale networks. The EDA algorithm is faster with marginally less complexity than an exhaustive search algorithm.

Résumé— Un nombre d’appels bloqués élevé est une conséquence d’une utilisation inefficace des ressources du système et elle est souvent causée par un débalancement de charge dans le réseau. Les déséquilibres de charge sont courants dans les réseaux sans fil, surtout avec un grand nombre d’utilisateurs de téléphones cellulaires.

Cet article étudie en profondeur un système d’équilibrage de charge pour les réseaux mobiles qui optimise les performances cellulaires en tenant compte des contraintes de ressources physiques limitées et des demandes de service de qualité de la part des utilisateurs. Afin d’utiliser efficacement les ressources du système, un système d’antennes distribuées intelligent (SADI) alimenté par une station émettrice-réceptrice de base (BTS) a la capacité de distribuer les ressources du système à travers une zone géographique donnée. Pour activer l’équilibrage de charge entre les modules d’antennes distribuées, nous allouons les modules d’antennes à distance au BTS de façon dynamique en utilisant un algorithme intelligent. Un réseau auto-organisé (RAO) pour un SADI est formulé comme un problème d’optimisation linéaire limitée de nombre entier qui tente d’équilibrer la charge entre les

BTS. Un algorithme d’estimation de distribution (AED) est proposé afin résoudre le problème d’optimisation en tant qu’algorithme évolutionnaire. Les résultats de calcul de l’AED démontrent une performance optimale pour les réseaux de petite taille et des performances quasi-optimale pour les réseaux de plus grande envergure.

L’AED est plus rapide, mais un peu moins complexe que l’algorithme de recherche exhaustive.

Index Terms— Distributed antenna system (DAS), evolutionary algorithm (EA), load balancing, self-optimization network (SON).

I. INTRODUCTION

THERE has been a substantial growth in mobile broadbandcommunication systems with the introduction of smartphones and tablets. With the substantial increase in cellular users, traffic hot spots and unbalanced call distributions are common in wireless networks. This degrades the quality of service (QoS) and increases call blocking and call drops.

As traffic environments change, the network performance

Manuscript received February 3, 2014; revised July 18, 2014; accepted

October 22, 2014. Date of current version April 29, 2015.

The authors are with the School of Engineering Science, Simon

Fraser University, Burnaby, BC V5A 1S6, Canada (e-mail: shejazi@sfu.ca; shawn@sfu.ca).

Associate Editor managing this paper’s review: Amin Mobasher.

Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/CJECE.2014.2366454 will be suboptimum. It is therefore necessary to perform self-optimization of the network dynamically according to the traffic environment, especially when cell traffic loads are not uniformly distributed. This is one of the important optimization issues in self-organized network (SON) for 3GPP long-term evolution (LTE) [1]. When the traffic loads among cells are not balanced, the blocking probability of heavily loaded cells may be higher, while their neighboring cells may have resources not fully utilized. In this case, load balancing can be conducted to alleviate and even to avoid this problem. All studies about load balancing can be classified into two categories: 1) block probability-triggered load balancing’ [2]–[4] and 2) utility-based load balancing [5]–[7].

Block probability-triggered load-balancing schemes have been proposed for efficient use of limited resources to increase the capacity of hot spots in wireless networks. Decreasing block probability is the main goal of these load-balancing schemes, 0840-8688 © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 90 CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING, VOL. 38, NO. 2, SPRING 2015 no matter that proportional fairness is applied or not. Utilitybased load-balancing schemes have been proposed to balance system throughput while serving users in a fair manner, these schemes result a utility maximization problem with networkwide proportional fairness as an objective in a network.