Prediction and Prioritization of Hardware Failures in 6G Small Cell Networks

The emergence of 6G technology promises to revolutionize telecommunications by significantly improving speed, connectivity, and overall network capacity. However, the complexity and density of 6G small-cell deployments increase the likelihood of hardware-related failures. Also, it is difficult to detect and prioritize these failures with traditional methodologies. At that point, this paper proposes a novel prediction and prioritization approach for hardware failures in 6G small cell networks. In this approach, we integrate the Knowledge Defined Networking (KDN) concept with Digital Twin (DT) technology to address these challenges. Here, the KDN-based prediction model leverages the Random Forest Algorithm to enhance hardware failure prediction. Also, the DT-enabled prioritization model simulates potential hardware failures in a virtual environment for real-time impact assessment. Therefore, this proposed approach aims to minimize network disruptions and maintain high performance even under increased failure rates and small-cell densities.