Machine learning will play a major role in handling the complexity of future mobile wireless networks by improving network management and orchestration capabilities. Due to the large number of parameters that can be monitored and configured in the network, collecting and processing high volumes of data is often unfeasible or too expensive at network runtime, which calls for taking resource management and service orchestration decisions with only a partial view of the network status. Motivated by this fact, this paper proposes a transfer learning framework for reconstructing the radio map corresponding to a target antenna tilt configuration by transferring the knowledge acquired from another tilt configuration of the same antenna, when no or very limited measurements are available from the target. The performance of the framework is validated against standard machine learning techniques on a data set collected from a 4G commercial base stations. In most of the tested scenarios, the proposed framework achieves notable prediction accuracy with respect to classical machine learning approaches, with a mean absolute percentage error below 8%.

中文翻译：

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倾斜相关无线电地图预测的迁移学习

机器学习将通过改进网络管理和编排能力，在处理未来移动无线网络的复杂性方面发挥重要作用。由于网络中可以监控和配置的参数数量众多，在网络运行时收集和处理大量数据通常是不可行的或成本太高，这需要仅在局部视图中进行资源管理和服务编排决策。网络状态。受这一事实的启发，本文提出了一种迁移学习框架，用于在目标没有或非常有限的测量可用时，通过迁移从同一天线的另一个倾斜配置获得的知识来重建与目标天线倾斜配置对应的无线电地图。该框架的性能在从 4G 商业基站收集的数据集上根据标准机器学习技术进行了验证。在大多数测试场景中，所提出的框架相对于经典机器学习方法实现了显着的预测精度，平均绝对百分比误差低于 8%。