Deep Learning-Based Throughput Prediction in 5G Cellular Networks
Iqra Batool, Mostafa M. Fouda, Zubair Md. Fadlullah
Abstract
Ahstract-5G technology has ushered in a new era of cellular networks characterized by unprecedented speeds and connectivity. However, these networks' dynamic and complex nature presents significant challenges in network management and Quality of Service (QoS) assurance. In this context, accurate throughput prediction is essential for optimizing net-work resources, improving traffic management, and enhancing user experiences. This study presents novel deep learning approaches utilizing Long Short-Term Memory (LSTM), Bi-directional LSTM (BiLSTM), and Artificial Neural Networks (ANN) to predict the throughput. The methodology achieves exceptional performance, surpassing existing methods. The motive behind leveraging deep learning algorithms is their exceptional ability to capture temporal dependencies and patterns within time-series data, which is intrinsic to network traffic. By employing these models, we can forecast network throughput with high precision, facilitating proactive resource allocation and congestion avoidance. Our approach maintains high QoS and supports cost efficiency and adaptive network maintenance. The BiLSTM and LSTM model's adaptability and learning capabilities make it well-suited for the ever-evolving 5G landscape, where user demands and network conditions fluctuate rapidly. This study demonstrates the technical feasibility and benefits of using BiLSTM and LSTM for overall throughput prediction. It highlights the broader implications for the future of 5G network management and optimization.