Deep Learning-based Framework for Multi-Fault Diagnosis in Self-Healing Cellular Networks

被引:6
|
作者
Riaz, Muhammad Sajid [1 ]
Qureshi, Haneya Naeem [1 ]
Masood, Usama [1 ]
Rizwan, Ali [2 ]
Abu-Dayya, Adnan [3 ]
Imran, Ali [1 ]
机构
[1] Univ Oklahoma, AI4Networks Res Ctr, Sch Elect & Comp Engn, Tulsa, OK 74135 USA
[2] Qatar Univ, Qatar Mobil Innovat Ctr, Doha, Qatar
[3] Qatar Univ, Dept Elect Engn, Doha, Qatar
来源
2022 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) | 2022年
基金
美国国家科学基金会;
关键词
Root cause analysis; multi-fault diagnosis; cellular data sparsity; minimization of drive tests; convolutional neural networks; radio environment map inpainting; network automation; self-healing;
D O I
10.1109/WCNC51071.2022.9771947
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fault diagnosis is turning out to be an intense challenge due to the increasing complexity of the emerging cellular networks. The root-cause analysis of coverage-related network anomalies is traditionally carried out by human experts. However, due to the vast complexity and the increasing cell density of the emerging cellular networks, it is neither practical nor financially viable. To address this, many studies are proposing artificial intelligence (AI)-based solutions using minimization of drive test (MDT) reports. Nowadays, the focus of existing studies is either on diagnosing faults in a single base station (BS) only or diagnosing a single fault in multiple BS scenarios. Moreover, they do not take into account training data sparsity (varying user equipment (UE) densities). Inspired by the emergence of convolutional neural networks (CNN), in this paper, we propose a framework combining CNN and image inpainting techniques for root-cause analysis of multiple faults in multiple base stations in the network that is robust to the sparse MDT reports, BS locations and types of faults. The results demonstrate that the proposed solution outperforms several other machine learning models on highly sparse UE density training data, which makes it a robust and scalable solution for self-healing in a real cellular network.
引用
收藏
页码:746 / 751
页数:6
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