Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (5): 84-88.

• 网络、通信、安全 • Previous Articles     Next Articles

ACO-based network selection algorithm

XU Minghai, ZI Yuan   

  1. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2012-02-11 Published:2012-02-11

基于蚁群优化的网络选择算法

徐名海,訾 源   

  1. 南京邮电大学 通信与信息工程学院,南京 210003

Abstract: With the advancing telecommunication technology, more and more standards for wireless communication network are developed. The heterogeneous wireless network is becoming a destined trend of mobile communication in order to protect investment and preserve smooth migration path. When user equipment is in such a heterogeneous environment, how to select the appropriate access network has become a research hotspot. However, all the existing network selection algorithms have some deficiencies, such as attribute redundancy, excessive consumption of equipment power and process capacity, or lack of feedback mechanism. Survey of existing network selection and ant-system theories are given in brief. A brand new network selection algorithm, Ant Colony Optimization(ACO) based Network Selection Algorithm(ANSA) is proposed. ANSA is trying to combine positive feedback mechanism with network selection. The performance of ANSA and TOPSIS is compared with MATLAB. It is shown that ANSA reduces the consumption of equipment electricity and has a better performance than TOPSIS on load-balancing.

Key words: network selection, Ant Colony Optimization(ACO), heterogeneous network, load balancing, feedback

摘要: 随着通信技术的不断发展,越来越多的无线通信网络标准被制定出来。为了保护投资,平滑过渡,各种不同的无线通信网络必然将相互融合。终端在这样一个多网络覆盖的区域中如何选择所使用的网络就成为了一个研究的热点。然而,在已有的诸多网络算法中,无一不存在着参加判决的参数过多、算法过于复杂而导致终端的电力和处理能力消耗过多、没有较好考虑网络负载均衡的缺陷并且没有考虑终端的反馈机制。简要介绍异构融合网络场景下网络选择的相关内容,包括异构融合网络场景,已有的网络选择算法,蚁群优化及其特点。在此基础上,提出了一种全新的基于蚁群模型的网络选择算法(ANSA)。利用Matlab对所提出的ANSA的性能进行了仿真分析,与TOPSIS算法进行对比,证明了ANSA比已有的网络选择算法具有更好的负载均衡性能并且降低了终端的复杂度。

关键词: 网络选择, 蚁群优化, 异构网络, 负载均衡, 反馈