Wireless Mesh Network Planning Using Quantum Inspired Evolutionary Algorithm

Hafiz Munsub Ali, Saeed Ashrafinia, Jiangchuan Liu, Daniel Chonghwan Lee

Abstract: The latest increase in mobile data usage and emergence of new applications such as Multimedia Online Gaming (MMOG), mobile TV and streaming contents have motivated advances in wireless broadband systems. Recently, the Long-Term Evolution (LTE) technology, which is based on the Universal Mobile Telecommunications System (UMTS) specifications, joins WiMAX as a competitor to achieve increasing demands of the broadband wireless access. Careful deployment of such a network is required to fulfill the high data rate demands with minimal cost of infrastructure and comprehensive coverage of the subscribers. In this paper, a multi-objective network planning problem is defined as utilizing the minimum number of infrastructure sites (i.e. Base Stations or eNode B in UMTS systems) while maximum number of users in service. We proposed a Quantum Inspired Evolutionary Algorithm (QIEA) in order to achieve optimized solution for this problem. The QIEA can be viewed as a probabilistic evolutionary algorithm and thus it is plausible to expect a reasonably good performance in solving combinatorial optimization problems. In this algorithm, each individual is represented by a string of Q-bits, where a Q-bit is the probabilistic representation inspired by the qubit concept in the quantum computing. Computational experiments show that our algorithm is fairly efficient to different scenarios of the network planning problem and performs better than the Genetic Algorithm (GA).

External IDs:dblp:conf/vtc/AliALL11