Amuah Ebenezer Ackah, Wu Mingxiao, Zhu Xiaorong
Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Sensors (Basel). 2023 Aug 9;23(16):7042. doi: 10.3390/s23167042.
The efficient and accurate diagnosis of faults in cellular networks is crucial for ensuring smooth and uninterrupted communication services. In this paper, we propose an improved 4G/5G network fault diagnosis with a few effective labeled samples. Our solution is a heterogeneous wireless network fault diagnosis algorithm based on Graph Convolutional Neural Network (GCN). First, the common failure types of 4G/5G networks are analyzed, and then the graph structure is constructed with the data in the network parameter, given data sets as nodes and similarities as edges. GCN is used to extract features from the graph data, complete the classification task for nodes, and finally predict the fault types of cells. A large number of experiments are carried out based on the real data set, which is achieved by driving tests. The results show that, compared with a variety of traditional algorithms, the proposed method can effectively improve the performance of network fault diagnosis with a small number of labeled samples.
蜂窝网络中故障的高效准确诊断对于确保通信服务的顺畅和不间断至关重要。在本文中，我们提出了一种利用少量有效标记样本的改进型4G/5G网络故障诊断方法。我们的解决方案是一种基于图卷积神经网络（GCN）的异构无线网络故障诊断算法。首先，分析4G/5G网络的常见故障类型，然后以网络参数中的数据构建图结构，将给定数据集作为节点，相似度作为边。使用GCN从图数据中提取特征，完成节点的分类任务，最后预测小区的故障类型。基于通过路测获得的真实数据集进行了大量实验。结果表明，与多种传统算法相比，该方法在少量标记样本的情况下能够有效提高网络故障诊断的性能。
