An Intelligent E2e Network Slicing Framework Using Transformer-Enhanced Drl

The 5G/6G era has introduced a wide variety of services, including enhanced Mobile Broadband (eMBB), UltraReliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). Each service presents unique, highly diversified, and often conflicting requirements, driving the need for more flexible and intelligent solutions. In this context, Network Slicing (NS) has emerged as a prominent technology that allows multiple virtual networks to operate over a shared physical infrastructure, thereby accommodating these diverse service demands. Supported by technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), network slicing requires the efficient placement of slices to optimize resource utilization and ensure Quality of Service (QoS). We propose a native artificial intelligence (AI) architecture for end-to-end (E2E) slicing that leverages Transformer-based Deep Reinforcement Learning (DRL) to enable zero-touch, automated slice placement in future networks, such as 5 G -and-beyond systems. Our system embeds AI directly into the network fabric, supporting native AI for real-time data processing and decision-making. Results show that integrating the Transformer model with DRL effectively addresses complex optimization challenges in network slicing, outperforming other state-of-the-art learning algorithms by better balancing slice acceptance ratio and energy efficiency. This supports the sustainable management of future networks, aligns with the vision of the Next Generation Mobile Networks (NGMN) Alliance, and illustrates the evolving role of AI in next-generation communication systems.