A Comprehensive Analysis on Network Slicing for Resource Allocation of 5G

Abstract

Optimizing resource allocation in 5G networks involves reconciling the conflicting requirements of enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and Ultra-Reliable Low Latency Communications (URLLC). eMBB demands high data rates and substantial bandwidth to support applications such as high-definition video streaming and virtual reality. In contrast, mMTC requires the network to support a massive number of low-power, low-data-rate devices, essential for the Internet of Things (IoT). URLLC poses the additional challenge of requiring ultra-low latency and high reliability for critical applications such as autonomous driving and remote surgery.

Advanced machine learning techniques, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, were utilized to develop prediction models. The CNN model achieved an impressive accuracy of 97% in predicting network slice allocations, while the LSTM model demonstrated a remarkable 97-98% accuracy in time series forecasting. Key achievements include enhanced model performance through meticulous hyperparameter tuning and data augmentation, which improved the model's robustness and generalization across diverse data scenarios.

Processing time was significantly reduced by implementing early stopping and batch normalization techniques, accelerating model convergence and deployment. Additionally, optimized load scheduling ensured balanced workload distribution across the network, enhancing overall system performance and reducing latency. This comprehensive approach addresses the diverse and stringent demands of 5G services, demonstrating a robust, efficient, and scalable framework for 5G network resource allocation. This research ensures improved network performance and reliability, effectively meeting the varied requirements of eMBB, mMTC, and URLLC, thereby contributing to the advancement of next-generation wireless communications.