自动故障排除中的统计学习：应用于LTE干扰缓解
Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation
摘要：
本文提出了一种方法，自动愈合的一部分，离线自动故障排除。该方法结合了统计学习和约束优化。自动修复旨在以迭代的方式局部优化具有较差性能的小区的无线电资源管理（RRM）或系统参数。统计学习使用逻辑回归（LR）处理数据以提取关键性能指标（KPI）和无线电资源管理（RRM）参数之间的封闭形式（函数）关系。这些函数关系然后由优化引擎处理，该优化引擎提出新的参数值。所提出的配方的优点是由自动愈合方法收敛所需的迭代次数少，使其适合离线实施。该方法被应用到愈合的小区间干扰协调（ICIC）过程中的3G长期演进（LTE）网络，这是基于软频率复用方案。数值模拟说明了所提出的方法的好处。
英文摘要：
This paper presents a method for automated healing as part of off-line automated troubleshooting. The method combines statistical learning with constraint optimization. The automated healing aims at locally optimizing radio resource management (RRM) or system parameters of cells with poor performance in an iterative manner. The statistical learning processes the data using Logistic Regression (LR) to extract closed form (functional) relations between Key Performance Indicators (KPIs) and Radio Resource Management (RRM) parameters. These functional relations are then processed by an optimization engine which proposes new parameter values. The advantage of the proposed formulation is the small number of iterations required by the automated healing method to converge, making it suitable for off-line implementation. The proposed method is applied to heal an Inter-Cell Interference Coordination (ICIC) process in a 3G Long Term Evolution (LTE) network which is based on soft-frequency reuse scheme. Numerical simulations illustrate the benefits of the proposed approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
