WiMAX Network Planning Using Adaptive-Population-Size Genetic Algorithm

Abstract

Abstract. IEEE 802.16, also known as WiMAX, is a new wireless access technology for currently increasing demand of wireless high-speed broadband service. Efficient and effective deployment of such a network to service an area of users with certain traffic demands is an important network planning problem. In this article, we resort to a Genetic Algorithm in order to yield good approximation solutions. In our method, individual representation and genetic variation operations are specifically designed to incorporate the feature of this application problem. Moreover, an adaptive population size approach inspired by neutral theory in molecular biology is applied in our algorithm to enhance its search ability. Simulation results show that our algorithm is fairly effective and robust to different scenarios of the network planning problem. By comparing to a conventional fixed population size scheme, our method is further verified to be able to accelerate the search process.

A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks

“…The use of mathematical optimization techniques in the context of wireless network design is not new and can be traced back to at least twenty years ago, as highlighted and surveyed for quite a wide range of problems in [13] and in [14]. Also, the adoption of bio-inspired and genetic heuristics is not new: if we focus on genetic and evolutionary algorithms, we can report the remarkable cases of: [15], which addresses the problem of the optimal positioning of base stations in a mobile network, encoding the location of base stations in the chromosomes of the genetic algorithm; [16], which addresses the decision problem of how establishing the optimal assignment of users to deployed transmitters, in particular in the context of WiMAX networks, encoding the assignment in the chromosomes; [17], which focuses on the frequency assignment problem (FAP), proposing a permutation-based genetic algorithm to solve minimum span and fixed spectrum variants of the FAP; [18], which focuses on the problem of setting the power emissions of base stations, proposing two distributed power control algorithms that are based on evolutionary computation techniques to fast solve the linear equation systems associated with power updates of the stations; [19], which proposes a genetic algorithm for addressing the joint problem of power, frequency and modulation scheme assignment in fixed networks based on the WiMAX technology. Moreover, it is interesting to note that other works have also tried to tackle sources of data uncertainty within wireless network design problems, such as [20,21], which adopt a stochastic optimization approach to find a robust location plan of base stations to deal with fluctuations in traffic demand, [22], which deals with the stochastic scheduling of 5G multimedia services and [23][24][25], which propose stochastic programming and robust optimization approaches to deal with signal propagation uncertainty of wireless technologies in real-world environments.…”

On Improving the Capacity of Solving Large-scale Wireless Network Design Problems by Genetic Algorithms

“…However, to our best knowledge, no GA has been yet developed to solve the PFMAP and the algorithm that we propose is the first for solving this level of generalization of the WND. Until now, GAs were indeed developed to solve just single aspects of the PFMAP: (i) the transmitter location problem (e.g., [4]); (ii) the service assigment problem (e.g., [9]); (iii) the frequency assignment problem (e.g., [5]); (iv) the power assignment problem (e.g., [15]). Moreover, we remark that our algorithm is the first to be designed with the specific aim of improving the capacity of solving instances, while tackling the numerical problems pointed out in Section 2.…”