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An Effective Search Algorithm for Hyper-Dense Deployment Problem of 5G

机译:一种有效的超密集部署问题5G的搜索算法

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Deploying cells in a 5G mobile communication environment has been a difficult optimization problem because many conflict factors, such as coverage ratio and interference, have to be taken into account at the same time. The hyper-dense deployment problem (HDDP) for the 5G environment has attracted the attention of researchers from different disciplines, because it provides a way to depict the deployment problem we have to confront in a 5G environment. In this paper, an effective search algorithm is presented for solving the HDDP. The proposed algorithm is based on a novel metaheuristic algorithm, called search economics for hyper-dense deployment problem (SE-HDDP); the main difference is in that it will divide the solution space into a set of subspaces and determine the investment of searches based on the expected value (i.e., potential) of each subspace. The simulation results show that the proposed algorithm is able to find a better solution than the other metaheuristic and rule-based algorithms compared in this paper.
机译:在5G移动通信环境中部署单元格已经是一个困难的优化问题,因为必须同时考虑许多冲突因素,例如覆盖率和干扰。 5G环境的超密集部署问题(HDDP)引起了来自不同学科的研究人员的注意,因为它提供了一种描述我们必须在5G环境中面对的部署问题的方法。在本文中,提出了一种用于解决HDDP的有效搜索算法。该算法基于一种新的成群质算法,称为搜索经济学,用于超密集部署问题(SE-HDDP);主要区别在于它将将解决方案空间划分为一组子空间,并根据每个子空间的预期值(即潜在)确定搜索的投资。仿真结果表明,在本文中,该算法能够找到比其他基于成形和规则的算法更好的解决方案。

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