Application-Aware Slicing for FRMCS: A Deep Reinforcement Learning Approach

doi:10.1109/tnsm.2026.3710830

Application-Aware Slicing for FRMCS: A Deep Reinforcement Learning Approach

2026-07-07

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DOI：10.1109/tnsm.2026.3710830

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The Future Railway Mobile Communication System (FRMCS) will replace GSM-R to support safety-critical and high-throughput applications over a limited 5–10 MHz spectrum. Railway services range from ultra-reliable train control, such as European Train Control System and Automatic Train Operation, to bandwidth-intensive video surveillance and best-effort passenger Wi-Fi, each with distinct requirements. Existing network slicing solutions designed for public 5G networks focus on aggregate slice-level guarantees, neglecting heterogeneous application requirements and the strong channel fluctuations induced by high-speed train mobility. To overcome this limitation, we propose in this paper an Application-Driven Slice Scheduling (ADSS) approach tailored for railway communications. ADSS leverages Deep Reinforcement Learning combined with channel-aware resource allocation to dynamically assign Resource Blocks, ensuring application-level Service Level Agreement (SLA) fulfillment. Evaluations on real Signal-to-Noise Ratio traces from trains traveling at speeds up to 350 km/h, demonstrate that ADSS achieves superior application-level SLA satisfaction, reduces violation gaps, and improves spectral efficiency compared to heuristic and state-of-the-art schedulers.

Keywords:

Network slicing

FRMCS

5G railway networks

deep reinforcement learning (DRL)

resource allocation

QoS assurance

Application-Aware Slicing for FRMCS: A Deep Reinforcement Learning Approach

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