Smart ECN and RSVP Protocols for QOS and Congestion Optimization in 5G Networks Using Advanced AI Techniques

In the rapidly evolving landscape of 5G networks, ensuring Quality of Service (QoS) and efficient congestion management are paramount to meet the high demands of modern applications. This paper presents a comprehensive study on the integration of Smart Explicit Congestion Notification (ECN) and Resource Reservation Protocol (RSVP) protocols, enhanced by advanced Artificial Intelligence (AI) techniques, to optimize QoS and manage congestion in 5G networks. We propose a novel framework that combines AI-driven algorithms with traditional congestion control mechanisms to dynamically adjust network parameters based on real-time traffic conditions and user demands. The Smart ECN protocol utilizes machine learning to predict and mitigate congestion before it impacts network performance, while the enhanced RSVP protocol leverages AI for intelligent resource allocation and QoS management. Through simulations and theoretical analysis, we demonstrate the effectiveness of our approach in improving network efficiency, reducing latency, and ensuring reliable service delivery across diverse 5G scenarios. This study underscores the potential of AI to revolutionize congestion control and QoS optimization, paving the way for more resilient and adaptive 5G networks. KEYWORDS: artificial intelligence (AI), machine learning (ML), Explicit Congestion Notification (ECN) and Resource Reservation Protocol (RSVP)