Enabling Critical Remote Services Through Intelligent AI-Based Traffic Management In 5G Satellite Networks

Abstract

The adoption of 5G satellite networks is central to the implementation of ubiquitous connectivity and the provision of critical remote services, including autonomous systems, telemedicine, and industrial IoT, which have very strict and heterogeneous Quality of Service (QoS) requirements. Nevertheless, due to the volatile nature of the satellite space, which is associated with changing topologies and limited resources, conventional traffic management schemes are no longer effective, which causes congestion, spikes in latency, and compromised reliability. This paper is meant to discuss a new, smart system of traffic management based on a hybrid Artificial Intelligence (AI) architecture to overcome the challenge. This fundamental innovation is a load-balancing algorithm that is synergistic (combining a Long Short-Term Memory (LSTM) network to predictive congestion forecasting with a Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) agent to perform adaptive, real-time distribution of traffic. This proactive-adaptive core is complemented by a service-aware orchestration module that implements differentiated QoS policies of Ultra-Reliable Low-Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communication (mMTC) traffic classes. The proposed framework achieves a significant improvement in performance over state-of-the-art criteria through the extensive simulation of a Low Earth Orbit (LEO) satellite constellation. These findings demonstrate that the end-to-end latency is reduced by 42.3 percent, the rate of packet loss is reduced by 67.8 percent, and the network throughput is also increased by 31.5 percent during a high-load condition, and also the SLA boundaries are met in all the critical service classes. This article confirms that a hybrid, service-conscious AI model is needed to transform satellite connections into an additional fabric that is deterministic and high-performance to support the next generation of remote services.