GA and ACO Algorithms Applied to Optimizing Location of Controllers in Wireless Networks

Optimizing location of controllers in wireless networks is an important problem in the cellular mobile networks designing. In this paper, I present two algorithms based on Genetic Algorithm (GA) and Ant Colony Optimization (ACO) to solve it. In the first algorithm, my objective function is determined by the total distance based on finding maximum flow in a bipartite graph using Ford-Fulkerson algorithm. In the second algorithm, I generate pheromone matrix of ants and calculate the pheromone content of the path from controller i to base station j using the neighborhood includes only locations that have not been visited by ant k when it is at controller i. At each step of iterations, I choose good solutions satisfying capacity constraints and update step by step to find the best solution depending on my cost functions. I evaluate the performance of my algorithms to optimize location of controllers in wireless networks by comparing to SA, SA-Greedy, LB-Greedy algorithm. Numerical results show that my algorithms proposed have achieved much better more than other algorithms.DOI:http://dx.doi.org/10.11591/ijece.v3i2.229

A Heuristics Based Approach for Cellular Mobile Network Planning

ABSTRACT Designing and planning of the switching, signaling and support network is a fairly complex process in cellular mobile network. In this paper, the problem of assigning cells to switches in cellular mobile network, which is considered a planning problem, is addressed. The cell to switch assignment problem which falls under the category of the Quadratic Assignment Problem (QAP) is a proven NP– hard problem. Further, the problem is modelled to include an additional constraint in the formulation. The additional constraint is of the maximum number of switch ports that are used for a cell's Base Station Transceiver System (BTS) connectivity to the switch. The addition of the constraint on the number of ports on a switch has immense practical signicance. This paper presents a non– deterministic heuristic based on Simulated Evolution (SimE) iterative algorithm to provide solutions. The methods adopted in this paper are a completely innovative formulation of the problem and involve application of Evolutionary Computing for this complex problem that may be extended to solutions of similar problems in VLSI design, distributed computing and many other applications

Designing Cellular Mobile Networks Using Non{Deterministic Iterative Heuristics

Abstract Network planning in the highly competitive, demand-adaptive and rapidly growing cellular telecommunications industry is a fairly complex and crucial issue. It comprises collective optimization of the supporting, switching, signaling and interconnection networks to minimize costs while observing imposed infrastructure constraints. This work focuses on the problem of assigning cells to switches, which comprise the Base Station Controller and Mobile Switching Center, in a cellular mobile network. As a classic instance of the NP-hard Quadratic Assignment Problem (QAP), deterministic algorithms are incapable of nding optimal solutions in the vast complex search space in polynomial time. Hence, a randomized, heuristic algorithm, such as Simulated Evolution is used in this work to optimize the transmission costs in cellular networks. The results achieved are compared with existing methods available in literature. Key words: Network planning, Cellular Mobile Network, Assignment, Quadratic Assignment Problem, Heuristics, Evolutionary Heuristics, Soft Computing

Network configuration improvement and design aid using artificial intelligence

This dissertation investigates the development of new Global system for mobile communications (GSM) improvement algorithms used to solve the nondeterministic polynomial-time hard (NP-hard) problem of assigning cells to switches. The departure of this project from previous projects is in the area of the GSM network being optimised. Most previous projects tried minimising the signalling load on the network. The main aim in this project is to reduce the operational expenditure as much as possible while still adhering to network element constraints. This is achieved by generating new network configurations with a reduced transmission cost. Since assigning cells to switches in cellular mobile networks is a NP-hard problem, exact methods cannot be used to solve it for real-size networks. In this context, heuristic approaches, evolutionary search algorithms and clustering techniques can, however, be used. This dissertation presents a comprehensive and comparative study of the above-mentioned categories of search techniques adopted specifically for GSM network improvement. The evolutionary search technique evaluated is a genetic algorithm (GA) while the unsupervised learning technique is a Gaussian mixture model (GMM). A number of custom-developed heuristic search techniques with differing goals were also experimented with. The implementation of these algorithms was tested in order to measure the quality of the solutions. Results obtained confirmed the ability of the search techniques to produce network configurations with a reduced operational expenditure while still adhering to network element constraints. The best results found were using the Gaussian mixture model where savings of up to 17% were achieved. The heuristic searches produced promising results in the form of the characteristics they portray, for example, load-balancing. Due to the massive problem space and a suboptimal chromosome representation, the genetic algorithm struggled to find high quality viable solutions. The objective of reducing network cost was achieved by performing cell-to-switch optimisation taking traffic distributions, transmission costs and network element constraints into account. These criteria cannot be divorced from each other since they are all interdependent, omitting any one of them will lead to inefficient and infeasible configurations. Results obtained further indicated that the search space consists out of two components namely, traffic and transmission cost. When optimising, it is very important to consider both components simultaneously, if not, infeasible or suboptimum solutions are generated. It was also found that pre-processing has a major impact on the cluster-forming ability of the GMM. Depending on how the pre-processing technique is set up, it is possible to bias the cluster-formation process in such a way that either transmission cost savings or a reduction in inter base station controller/switching centre traffic volume is given preference. Two of the difficult questions to answer when performing network capacity expansions are where to install the remote base station controllers (BSCs) and how to alter the existing BSC boundaries to accommodate the new BSCs being introduced. Using the techniques developed in this dissertation, these questions can now be answered with confidence.Dissertation (MEng)--University of Pretoria, 2008.Electrical, Electronic and Computer Engineeringunrestricte

Planification d'un réseau de quatrième génération à partir d'un réseau de troisième génération

RÉSUMÉ Avec l'arrivée des technologies 3G, les réseaux de télécommunications ont connu une grande expansion. Ces réseaux ont permis l'intégration de nouveaux services et un débit adéquat, permettant ainsi aux opérateurs de répondre à la demande croissante des utilisateurs. Cette rapide évolution a porté les opérateurs à adapter leurs méthodes de planification aux nouvelles technologies qui, augmentent la complexité au niveau du réseau. Cette complexité devient plus importante quand ces réseaux regroupent plusieurs technologies d'accès différents en un réseau hétérogène, comme dans le cas des réseaux mobiles de prochaine génération ou réseaux 4G. La planification fait alors intervenir de nouveaux défis tels que: l'augmentation considérable des demandes de services, la compatibilité avec les réseaux actuels, la gestion de la mobilité intercellulaire des utilisateurs et l'offre d'une qualité de services les plus flexibles. Ainsi, pour créer un réseau flexible aux ajouts et aux retraits d'équipements, une bonne méthode de planification s'impose. C'est dans ce contexte que se situe ce mémoire, qui vise à faire la planification d'un réseau 4G à partir d'un réseau 3G existant. De façon générale, le problème de planification fait intervenir plusieurs sous-problèmes avec chacun un niveau de complexité différent. Dans ce travail, le sous-problème qui est traité concerne l'affectation des cellules aux commutateurs. Ce problème consiste à déterminer un patron d'affectation qui permet de minimiser le coût d'investissement des équipements du réseau 4G, tout en maximisant l'utilisation faite des équipements du réseau 3G déjà en place. Ainsi, la solution proposée est un modèle mathématique dont l’expression prend la forme d'un problème de minimisation de fonction, assujetti à un ensemble de contraintes. Il s’agit d’une fonction de coût qui regroupe: l’affectation des cellules (eNode B) aux MME et aux SGW, et l’affectation des SGSN aux MME et aux SGW. Puisque les MME et SGW peuvent être rassemblés dans une seule passerelle, une entité nommée SGM a été défini. Ainsi, la fonction prend en compte les coûts des affectations des eNode B et des SGSN aux SGM. Ce modèle est sujet aux contraintes de capacités des SGM et aux contraintes d'unicité sur les affectations des eNode B et SGSN aux SGM. Le modèle mathématique proposé est constitué des coûts de liaisons des équipements du réseau 4G, des coûts de liaisons inter-réseaux, des coûts de relèves horizontales (intra réseau 4G) et des coûts de relèves verticales (inter-réseau 3G-4G). Le problème étant prouvé NP-difficile, la performance du modèle sera évaluée au moyen d'une méthode heuristique basée sur la recherche taboue. Pour adapter l'heuristique au problème d'affectation dans les réseaux 4G, des mouvements de réaffectation et de déplacement des nœuds eNode B et SGSN ont été définis. De même, un mécanisme de calcul de gain a été proposé, permettant d'évaluer l'apport de chaque mouvement sur le coût de la solution courante. Ainsi, les résultats numériques obtenus de l'implémentation de cette méthode, montrent que la méthode taboue accuse un écart moyen ne dépassant pas 30\% par rapport à la solution optimale. Alors que pour certains réseaux, l'heuristique a été en mesure de trouver des résultats ayant un écart moyen ne dépassant pas 1\% par rapport à la solution optimale trouvée dans les simulations.----------ABSTRACT With the advent of 3G technologies, mobile networks have expanded greatly. These networks have enabled the integration of new services and an enough bandwidth, allowing operators to meet the growing demand of users. This rapid evolution has led the operators to adapt their planning approach that come with new challenges. Those challenges become more important when these networks are designed to support different radio access technologies within a heterogeneous mobile network, like 4G networks. In this case, planning those networks involves other challenges, such as the considerable increase in services requests, compatibility with existing networks, management of intercellular mobility of users and a good quality of offered services. Thus, in order to create a network that allows to add or to remove components, good planning approach is needed. It is in this context, this paper aims to address the problem that occurs when the planning of a 4G network is based on an existing 3G network. The planning issue involves several sub-issue with a different level of complexity for each of them. This work mainly addresses the cell assignment problem regarding the 4G networks. Thus, the proposed solution is a mathematical model. This model has mainly two objectives: the assignment between 4G nodes, and the assignment between 3G and 4G nodes. Since the MME and SGW can be aggregated into a single gateway, an entity named SGM has been set. Thus, the model becomes a cost function involving assignments eNode B and SGSN to SGM. This model is subject to capacity constraints of SGM, and unique constraints on assignments eNode B and SGSN to SGM. The proposed model includes: the link's costs of 4G-network equipment, the link's costs between 3G and 4G equipment, the horizontal handoff costs (intra 4G network) and the vertical handover costs (inter-3G-4G). The problem is NP-hard, a tabu search algorithm will be used. To adapt this heuristic, movements have been defined to reallocate and move nodes eNode B and SGSN in order to improve the cost of the current solution. The results of the implementation show a gap which is less then 30\% between the TS results and left bound value. For others networks size, the gap is sometimes less then 1\% compare to the left bound value