Assignment of Cells to Switches in a Cellular Mobile Environment Using Swarm Intelligence

S. Udgata, U. Anuradha, G. Kumar, Gauri K. Udgata
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引用次数: 9

Abstract

The problem of assigning cells to switches in a mobile cellular network is a NP-Hard problem. It is therefore necessary to use a heuristic method to solve it in a reasonable amount of time with acceptable accuracy particularly for large sized problems. The assignment of cells to switches problem is characterized by minimization of the cabling cost, hand-off cost and switching costs in the whole network. We propose a swarm intelligence based technique to solve this problem. Ant colony optimization (ACO) and Particle swarm optimization (PSO) are typical swarm intelligence techniques. ACO technique was used for cell assignment problem in the recent past and shown to be better in comparison to the other schemes. In this paper, we propose a modified binary Particle Swarm Optimization (MBPSO) technique for this cell assignment problem. Our experimental results reveal better results in terms of accuracy and execution time compared to ACO for a large combination of parameters.
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基于群体智能的蜂窝移动环境中蜂窝到交换机的分配
在移动蜂窝网络中将蜂窝分配给交换机的问题是一个NP-Hard问题。因此,有必要使用启发式方法在合理的时间内以可接受的精度解决它,特别是对于大型问题。小区交换机分配问题的特点是使全网的布线成本、切换成本和交换成本最小。我们提出了一种基于群体智能的技术来解决这一问题。蚁群优化和粒子群优化是典型的群体智能技术。近年来,蚁群算法被用于解决小区分配问题,并被证明优于其他方案。本文提出了一种改进的二元粒子群优化(MBPSO)方法来解决该单元分配问题。我们的实验结果表明,在大量参数组合的情况下,与蚁群算法相比,在精度和执行时间方面取得了更好的结果。
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