DocumentCode :
617932
Title :
Broadband wireless network planning using evolutionary algorithms
Author :
Ali, H.M. ; Ashrafinia, S. ; Jiangchuan Liu ; Lee, Daewoo
Author_Institution :
Sch. of Eng. Sci., Simon Fraser Univ., Burnaby, BC, Canada
fYear :
2013
fDate :
20-23 June 2013
Firstpage :
1045
Lastpage :
1052
Abstract :
In this paper, we present a simultaneous planning of Base Stations (BSs) and Relay Stations (RSs) with link flow for a broadband wireless network. Infrastructure costs (BS cost, RS cost and their operational costs) of a wireless network is a key factor for network service providers while planning a network. The objective of this problem is to determine a set of BSs and RSs that can serve all users and fulfill their demands at the lowest cost. This problem settings is equally important for planning networks from scratch or enhancements in existing networks. This combinatorial optimization problem is NP-hard in nature. Evolutionary Algorithms (EAs) are intelligent tools that can provide high quality solution to this type of problems. Usually, efficiency of EAs depends on the problem. The aim is to find effective EAs with minimum resources such as low computational complexity, processing time and number of fitness functions evaluations. We formulate this problem as a non-linear discrete optimization and introduce four recent EAs that are motivated by natural intelligent behaviors. The objective function of this planning problem is computationally costly, and there exist a tradeoff between resources and quality of solution. These algorithms include Biogeography-based Optimization (BBO) that is inspired by the natural migration phenomenon of species between different islands, Artificial Bee Colony (ABC) based on the intelligent behavior of honey bee swarms, Quantum-inspired Evolutionary Algorithm (QEA) from the idea of quantum computing, and Immune Quantum Evolutionary Algorithm (IQEA) motivated by both the immune theory and quantum computing. Simulation results demonstrate insights of EAs´ and present tradeoff between resources and quality of solutions.
Keywords :
broadband networks; combinatorial mathematics; computational complexity; evolutionary computation; optimisation; radio networks; telecommunication network planning; artificial bee colony; base stations; biogeography-based optimization; broadband wireless network planning; combinatorial optimization; computational complexity; evolutionary algorithms; functions evaluations; honey bee swarms; immune quantum evolutionary algorithm; immune theory; natural intelligent behaviors; natural migration phenomenon; nonlinear discrete optimization; planning networks; processing time; quality of solution; quantum computing; quantum-inspired evolutionary algorithm; relay stations; simultaneous planning; Evolutionary computation; Immune system; Optimization; Planning; Relays; Sociology; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2013 IEEE Congress on
Conference_Location :
Cancun
Print_ISBN :
978-1-4799-0453-2
Electronic_ISBN :
978-1-4799-0452-5
Type :
conf
DOI :
10.1109/CEC.2013.6557682
Filename :
6557682
Link To Document :
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