Adaptive Artificial Intelligence Techniques for QoS and Congestion Management in 5G Wireless Networks: A Literature

In the rapidly evolving landscape of 5G wireless networks, ensuring Quality of Service (QoS) and managing congestion effectively are critical challenges that must be addressed to meet the stringent performance requirements of modern applications. This paper presents an in-depth study on the application of adaptive artificial intelligence (AI) techniques for QoS and congestion management in 5G wireless networks. By leveraging machine learning (ML) and deep learning (DL) algorithms, we propose an intelligent framework that dynamically adjusts network parameters in real-time to optimize performance and mitigate congestion. The proposed approach integrates reinforcement learning for adaptive decision-making, convolutional neural networks (CNNs) for traffic pattern recognition, and long short-term memory (LSTM) networks for predictive analysis of network congestion trends. Through extensive simulations and real-world testing, our framework demonstrates significant improvements in QoS metrics such as latency, throughput, and packet loss, while efficiently managing congestion across diverse network scenarios. The results indicate that adaptive AI techniques hold immense potential in enhancing the robustness and efficiency of 5G wireless networks, paving the way for more reliable and high-performance communication systems. KEYWORDS: artificial intelligence (AI), machine learning (ML), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks