Telecommunications Network Planning and Maintenance

Telecommunications network operators are on a constant challenge to provide new services which require ubiquitous broadband access. In an attempt to do so, they are faced with many problems such as the network coverage or providing the guaranteed Quality of Service (QoS). Network planning is a multi-objective optimization problem which involves clustering the area of interest by minimizing a cost function which includes relevant parameters, such as installation cost, distance between user and base station, supported traffic, quality of received signal, etc. On the other hand, service assurance deals with the disorders that occur in hardware or software of the managed network. This paper presents a large number of multicriteria techniques that have been developed to deal with different kinds of problems regarding network planning and service assurance. The state of the art presented will help the reader to develop a broader understanding of the problems in the domain

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

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

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

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

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