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arXiv 2103.08408cs.NIcs.AIcs.LGcs.NE
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一种5G超密集网络无线回传中的快速网关定位算法

A Fast Heuristic for Gateway Location in Wireless Backhaul of 5G Ultra-Dense Networks

Carleton University(卡尔顿大学)

Mital Raithatha, Aizaz U. Chaudhry, Roshdy H. M. Hafez, John W. Chinneck

摘要:

在5G超密集网络中,分布式无线回程是将流量转发到核心的有吸引力的解决方案。宏小区覆盖区域被划分为许多小小区。其中一些单元被指定为网关,并通过高容量光纤链路与核心相连。每个小小区与一个网关相关联,并且所有小小区通过多跳网状网络将它们的业务转发到它们各自的网关。我们调查的网关位置的问题,发现接近最佳的网关位置提高了回程网络的容量。一个精确的p-中位数整数线性规划制定我们的新的K-GA启发式,结合了遗传算法(GA)与K-均值聚类找到接近最佳的网关位置进行比较。我们比较KGA的性能与其他六种方法在不同的节点密度的平均跳数和回程网络容量,通过广泛的蒙特卡罗模拟。所有方法都在各种用户分布场景中进行了测试,包括均匀分布,二元高斯分布和聚类分布。在所有情况下,K-GA都能提供接近最优的结果,平均跳数和回程网络容量在最优值的2%以内,同时平均节省95%的执行时间。

英文摘要:

In 5G Ultra-Dense Networks, a distributed wireless backhaul is an attractive solution for forwarding traffic to the core. The macro-cell coverage area is divided into many small cells. A few of these cells are designated as gateways and are linked to the core by high-capacity fiber optic links. Each small cell is associated with one gateway and all small cells forward their traffic to their respective gateway through multi-hop mesh networks. We investigate the gateway location problem and show that finding near-optimal gateway locations improves the backhaul network capacity. An exact p-median integer linear program is formulated for comparison with our novel K-GA heuristic that combines a Genetic Algorithm (GA) with K-means clustering to find near-optimal gateway locations. We compare the performance of KGA with six other approaches in terms of average number of hops and backhaul network capacity at different node densities through extensive Monte Carlo simulations. All approaches are tested in various user distribution scenarios, including uniform distribution, bivariate Gaussian distribution, and cluster distribution. In all cases K-GA provides near-optimal results, achieving average number of hops and backhaul network capacity within 2% of optimal while saving an average of 95% of the execution time.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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