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

作     者：Zhu, Si-feng Liu, Fang Qi, Yu-tao Chai, Zheng-yi Wu, Jian-she 

作者机构：Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Zhoukou Normal Univ Dept Math & Informat Sci Zhoukou 466001 Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China 

出 版 物：《ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE》 (人工智能的工程应用)

年 卷 期：2012年第25卷第7期

页      面：1395-1402页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Fundamental Research Funds for the Central Universities [JY72105476] China Postdoctoral Science Foundation National Science Basic Research Plan in Henan Province of China [112102210221, 12A520055] 

主　　题：Joint call admission control Next-generation wireless network Dynamic pricing Load balance User's preference Immune optimization algorithm 

摘      要：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. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.