A near optimal call admission control with genetic algorithm for multimedia services in wireless/mobile networks

Proceedings of the IEEE 2000 National Aerospace and Electronics Conference. NAECON 2000. Engineering Tomorrow (Cat. No.00CH37093) Pub Date : 2000-10-10 DOI:10.1109/NAECON.2000.894994

Yang Xiao, Clark Chen, Yan Wang

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引用次数: 27

Abstract

In this paper, we treat a cell as an M/M/C/C queuing system with m class users. Semi-Markov decision process (SMDP) can be used to provide an optimal call admission control (CAC). The optimization is in the use of optimizing the channel utilization for service providers and satisfying the quality of service (QoS) requirements for service users, which are the upper bounds of handoff blocking probabilities. However, such methods fail when the state space and the action space are too large. We apply genetic algorithm approach to address such problems where the SMDP approach fails. We code the call admission control decisions as binary strings, where the value of "l" in the position i of the string stands for the decision of accepting a call in class-i; whereas, the value of "0" in the position i of the string stands for the decision of rejecting a call in class-i. The resulting binary strings from the genetic algorithm are the near optimal CAC decisions. Simulation results from the genetic algorithm are compared with the optimal solution obtained from linear programming for SMDP. The results reveal that the genetic algorithm approximates the optimal solution very well.

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基于遗传算法的无线/移动网络多媒体业务近最优呼叫接纳控制

在本文中，我们把一个单元看作一个M/M/C/C排队系统，有M类用户。半马尔可夫决策过程(SMDP)可用于提供最优的呼叫允许控制(CAC)。优化是利用优化服务提供商的信道利用率和满足业务用户的服务质量要求，这是切换阻塞概率的上界。然而，当状态空间和动作空间太大时，这种方法就会失败。我们采用遗传算法来解决SMDP方法无法解决的问题。我们将调用接纳控制决策编码为二进制字符串，其中字符串位置i中的“l”的值代表在类i中接受调用的决策;然而，字符串位置I中的“0”值表示拒绝类I中的调用的决定。遗传算法得到的二进制字符串是接近最优的CAC决策。将遗传算法的仿真结果与线性规划方法的最优解进行了比较。结果表明，遗传算法能很好地逼近最优解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the IEEE 2000 National Aerospace and Electronics Conference. NAECON 2000. Engineering Tomorrow (Cat. No.00CH37093)

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