面向覆盖和成本效益5G网络部署的小蜂窝优化定位:一种智能模拟退火方法

V. Nikam, Anuj Arora, Deeplaxmi Lambture, Jash Zaveri, Prathamesh Shinde, Mayur M. More
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引用次数: 3

摘要

随着当前4G网络的负载不断升级,升级到5G的需求已经出现。5G网络的部署将为最终用户提供广泛的技术。但是,为了获得更好的覆盖而随机部署5G小型蜂窝塔,会导致成本的大幅增加,干扰的增加,以及资源利用率的降低。为了解决这一超密集部署问题(HDDP),采用了一种智能模拟退火算法,该算法采用启发式算法去除过多的小单元,并采用启发式算法置换小单元以实现更大的覆盖。考虑了两种不同的位移方法:随机位移法和探测位移法。该方法在考虑植被、建筑物、道路网络等地理空间实体的同时,增强和优化了部署。新设计的策略最大限度地降低了成本,优化了覆盖范围,从而实现了更大的资源利用。对上述方法的两种方法进行了评价和比较。实现结果表明,与随机位移法相比,探测位移法得到的结果更好,且变化更小。
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Optimal Positioning of Small Cells for Coverage and Cost Efficient 5G Network Deployment: A Smart Simulated Annealing Approach
With the escalating load on the current 4G network, the requirement for moving up to 5G has shown up. The deployment of a 5G network would deliver the end-users a wide spectrum of technologies. But a random deployment of 5G small cell towers to attain better coverage leads to a high increase in cost, an increase in interference, and also a decrease in resource utilization. To solve this Hyper Dense Deployment Problem (HDDP), a Smart Simulated Annealing algorithm with a heuristic to remove excessive small cells and a heuristic to displace the small cells to achieve greater coverage, is adopted. Two different approaches of displacement are considered, Random Displacement Approach and Probed Displacement Approach. The methodology enhances and optimizes the deployment while taking geospatial entities like vegetation, buildings, road networks, etc. into consideration. The newly devised strategy minimizes the cost, optimizes coverage, and thus enables greater resource utilization. The two approaches to the above methodology are evaluated and compared. The implementation results show that the results in the Probed Displacement Approach are better and have less variation than the results generated in Random Displacement Approach.
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