Unsupervised Root Cause Analysis Using Twin Attentional‐Generative Adversarial and Walrus‐Based Deep Coupling Recurrent Autoencoder

The increasing complexity of long‐term evolution (LTE) networks presents challenges in fault detection and automated diagnosis. These traditional supervised learning approaches need extensive labeled datasets, which are challenging to obtain in live network environments. In order to address this issue, this paper proposes an automatic root cause analysis model based on an unsupervised learning mechanism (ARCA‐ULM) for self‐healing LTE networks. The proposed ARCA‐ULM adopts the twin attentional‐generative adversarial network (TA‐GAN) model for synthetic data generation, addressing class imbalance issues in training datasets. Then, the root cause pattern is identified using a lightweight walrus‐based deep coupling gated recurrent autoencoder (WDCGRA) model, which incorporates coupling autoencoders (AEs), gated recurrent units (GRUs), and the walrus optimization algorithm (WOA) to distinguish hardware faults from software misconfigurations. The radio link control (RLC) layer optimization scheme is employed to improve network performance. The ARCA‐ULM model is implemented in Python and evaluated in terms of different performance indicators. Simulation outcomes demonstrate superior performance, achieving 99.17% accuracy, a diagnosis error rate (DER) of 0.14, and an undetected error rate (UER) of 2.29, outperforming existing methods. The outcomes confirm the effectiveness of the ARCA‐ULM model in enhancing the self‐healing abilities of LTE networks, allowing more reliable fault detection and resolution.