Immune optimization algorithm for solving joint call admission control problem in next-generation wireless network

Authors:
Si-Feng Zhu;Fang Liu;Yu-Tao Qi;Zheng-Yi Chai;Jian-She Wu
Affiliations:
School of Computer Science and Technology, Xidian University, Xi an 710071, China and Department of Mathematics and Information Science, Zhoukou Normal University, Zhoukou 466001, China;School of Computer Science and Technology, Xidian University, Xi an 710071, China and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian Uni ...;School of Computer Science and Technology, Xidian University, Xi an 710071, China and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian Uni ...;School of Computer Science and Technology, Xidian University, Xi an 710071, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xían 710071, China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2012

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Abstract

The integration of radio access networks with different radio access technologies (RATs) is one of the remarkable characteristics of the next-generation wireless networks (NGWNs). In NGWN, the users with multi-network interface terminals should be able to select independently radio access network to obtain the best service. Therefore, joint call admission control (JCAC) schemes are required to select the most appropriate radio access network (RAN) for incoming calls. We propose an immune algorithm-based JCAC (IA-JCAC) scheme with users centric in order to enhance user's satisfaction. However, JCAC algorithms with users centric can lead to highly unbalanced traffic load among the available RANs in NGWN because users act independently, and most of them may prefer to be connected through a particular RAN. Highly unbalanced traffic load in NGWN will result in high overall call blocking/dropping probability and poor radio result utilization. To solve this problem, we employ dynamic pricing for balancing traffic load among available RANs in heterogeneous wireless networks where users' preferences are considered in decision-making on RAT selection. The proposed IA-based JCAC scheme is compared with another scheme that does not use the dynamic pricing on the performance. The simulation result shows the effectiveness of the proposed IA-JCAC scheme is improved significantly.