Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G systems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixedvariable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using {\alpha}-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.

翻译：数字孪生在支持无线网络发展方面展现出巨大潜力。它们是5G/6G系统的虚拟表示，能够支撑基于机器学习和优化技术的设计。现场数据复制是构建仿真孪生的关键环节之一，其目标是对仿真进行校准以匹配现场性能测量结果。由于无线网络涉及多种关键性能指标（KPI），复制过程成为一个多目标优化问题，旨在最小化仿真数据与现场数据KPI之间的误差。与现有工作不同，我们专注于设计一种数据驱动搜索方法，用于校准仿真器并实现对现场性能的准确可靠复现。本研究提出一种基于混合变量粒子群优化（PSO）的搜索算法，以寻找最优仿真参数。此外，我们利用α-公平概念扩展该解决方案，以应对KPI之间的潜在冲突，从而在搜索过程中调整每个KPI的重要性权重。现场数据实验证明了我们方法的有效性：（i）提高了复制的准确性，（ii）增强了不同KPI之间的公平性，并（iii）相比其他方法保证了更快的收敛速度。