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    用户需求驱动的5G基站选址方法

    A User Demand Driven Approach for 5G Base Station Location Selection

    • 摘要: 随着5G网络的不断发展和相关应用的快速普及,用户设备数量及潜在需求急剧增加. 然而,5G信号的高频特性导致其传播损耗较大,为实现5G网络对用户设备更好的覆盖,需要以低成本、高效率为目标对已建5G基站站址进行优化或指导新建基站选址. 现有选址方法大多采用启发式算法进行站址优化,当候选5G基站站址数量增加时,算法的收敛时间会呈指数级上升,为站址优化带来了诸多挑战. 因此,从用户的通信需求出发,提出了一种用户需求驱动的5G基站选址方法. 利用规划区域网格化方法来降低基站所覆盖用户需求点的计算时间复杂度,提出基站间分离度的概念、并使用基站所覆盖的需求点数对其进行度量,进而给出满足子模性的目标函数,利用贪心算法得到基站最优选址方案. 实验结果表明,用户需求驱动的选址方法在各项评价指标上均优于其他对比算法,在相同的基站规划区域内,能用最少的基站数量达到最大覆盖率.

       

      Abstract: With the continuous development and rapid popularization of 5G networks, the number of user devices and potential demand is increasing sharply. However, the high frequency of 5G signals leads to significant propagation losses. In order to achieve broader coverage of user devices, it is necessary to optimize existing 5G base station sites or guide the selection of new base station sites with low cost and high efficiency. The state-of-the-art methods for site selection mostly use heuristic algorithms to optimize the sites. However, the convergence time increases exponentially with the increase of the number of possible 5G base station sites, bringing many challenges for the site optimization. Therefore, we propose the method of selecting 5G base station sites based on user demand points to sufficiently consider the communications among users. Specifically, the planning area gridding method is proposed to reduce the time complexity of computation for user demand points covered by base stations. Then, the concept of separate degree among base stations is proposed and measured based on the number of user demand points covered by the base station. We give the objective function that satisfies the submodularity and the greedy algorithm to obtain the optimal scheme of base station site selection. Experimental results show that the proposed method outperforms the comparative algorithms on all evaluation metrics, and can effectively improve the coverage of 5G base station signals. In the same base station planning area, our proposed method achieves the maximum coverage rate with the minimum number of 5G base stations, thereby effectively reducing the construction cost of 5G base stations.

       

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