无线网络中多目标域数据公平复制的混合变量PSO
Mixed-Variable PSO with Fairness on Multi-Objective Field Data Replication in Wireless Networks
摘要：
数字孪生在支持无线网络发展方面显示出巨大的潜力。它们是5G/6 G系统的虚拟表示，支持机器学习和基于优化的技术的设计。现场数据复制是构建基于模拟的孪生模型的关键方面之一，其目标是校准模拟以匹配现场性能测量。由于无线网络涉及各种关键性能指标（KPI），复制过程成为一个多目标优化问题，其目的是最大限度地减少模拟和现场数据KPI之间的误差。与以前的作品不同，我们专注于设计一个数据驱动的搜索方法来校准模拟器，并实现准确和可靠的再现现场性能。本文提出了一种基于混合变量粒子群优化算法的最优仿真参数搜索算法。此外，我们使用α-公平性概念来调整搜索过程中每个KPI的重要性，从而扩展了该解决方案以解决KPI之间的潜在冲突。现场数据的实验表明，我们的方法的有效性（i）提高复制的准确性，（ii）提高不同KPI之间的公平性，（iii）保证更快的收敛速度相比，其他方法。
英文摘要：
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 α-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.
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