Enhancing Network Slicing Efficiency in 6G Networks With a Hybrid Deep Learning Approach Leveraging Crisscross Harris Hawks Optimization
Megha Jain, Ravi Verma, Sunil Kumar, Gyanendra Kumar, Shakila Basheer · IEEE Communications Standards Magazine · 2025
The evolution toward 6G networks introduces unprecedented opportunities for high-speed, ultra-reliable, and low-latency communication, largely enabled through intelligent network slicing. This study proposes a novel hybrid deep learning framework (CHHO-CNN+LSTM) that leverages Convolutional Neural Networks (CNN) for spatial feature extraction, Long Short-Term Memory (LSTM) networks for temporal learning, and Crisscross Harris Hawks Optimization (CHHO) for robust hyperparameter tuning. Using the Unicauca IP Flow Version 2 dataset, the proposed model significantly enhances network slice classification accuracy and efficiency. Evaluation results demonstrate the model’s superiority over conventional techniques with an accuracy of 95.48%, a precision of 94.11%, recall of 87.45%, and an F1-score of 93.87%. The integration of CHHO improves convergence speed and learning stability. These findings confirm the effectiveness of the proposed hybrid model in enabling intelligent, adaptive network slicing within 6G environments.