The indoor scene has the characteristics of complexity and Non-Line of Sight (NLOS). Therefore, in the application of cellular network positioning, the layout of the base station has a significant influence on the positioning accuracy. In three-dimensional indoor positioning, the layout of the base station only focuses on the network capacity and the quality of positioning signal. At present, the influence of the coverage and positioning accuracy has not been considered. Therefore, a network element layout optimization algorithm based on improved Adaptive Simulated Annealing and Genetic Algorithm (ASA-GA) is proposed in this paper. Firstly, a three-dimensional positioning signal coverage model and a base station layout model are established. Then, the ASA-GA algorithm is proposed for optimizing the base station layout scheme. Experimental results show that the proposed ASA-GA algorithm has a faster convergence speed, which is 16.7% higher than the AG-AC (Adaptive Genetic Combining Ant Colony) algorithm. It takes about 25 generations to achieve full coverage. At the same time, the proposed algorithm has better coverage capability. After optimization of the layout of the network element, the effective coverage rate is increased from 89.77 to 100% and the average location error decreased from 2.874 to 0.983 m, which is about 16% lower than the AG-AC algorithm and 22% lower than the AGA (Adaptive Genetic Algorithm) algorithm.

中文翻译：

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室内定位网元布局融合优化算法

室内场景具有复杂性和非视线（NLOS）的特征。因此，在蜂窝网络定位的应用中，基站的布局对定位精度有很大的影响。在三维室内定位中，基站的布局仅关注网络容量和定位信号的质量。目前，尚未考虑覆盖范围和定位精度的影响。因此，本文提出了一种基于改进的自适应模拟退火遗传算法的网元布局优化算法。首先，建立了三维定位信号覆盖模型和基站布局模型。然后，提出了ASA-GA算法，用于优化基站布局方案。实验结果表明，所提出的ASA-GA算法具有更快的收敛速度，比AG-AC（自适应遗传组合蚁群）算法高16.7％。完全覆盖大约需要25代。同时，该算法具有更好的覆盖能力。优化网元布局后，有效覆盖率从89.77提高到100％，平均定位误差从2.874降低到0.983 m，比AG-AC算法低16％，比AG-AC算法低22％。 AGA（自适应遗传算法）算法。该算法具有更好的覆盖能力。优化网元布局后，有效覆盖率从89.77提高到100％，平均定位误差从2.874降低到0.983 m，比AG-AC算法低16％，比AG-AC算法低22％。 AGA（自适应遗传算法）算法。该算法具有更好的覆盖能力。优化网元布局后，有效覆盖率从89.77提高到100％，平均定位误差从2.874降低到0.983 m，比AG-AC算法低16％，比AG-AC算法低22％。 AGA（自适应遗传算法）算法。