Adaptive Network Slicing and LSTM-Based Resource Allocation for Real-Time Industrial Robot Control in 6G Networks

Abstract

The deployment of industrial robots in time-critical applications demands ultra-low latency and high reliability in communication systems. This study presents a novel delay optimisation framework for industrial robot control systems using 6G network slicing technologies. A Gale–Shapley (GS)-based elastic switching model is proposed to dynamically match robot controllers to optimised network slices and base stations under latency-sensitive conditions. To enhance resource adaptability, a long short-term memory (LSTM)-based encoder-decoder structure is developed for predictive resource allocation across slices. The proposed integrated matching mechanism achieves a success rate of 91.16% for slice access and a base station access rate of 90.83%, outperforming conventional integrated and two-stage schemes. The LSTM-based resource allocation achieves a mean absolute error of 0.04 and a violation rate below 10%, with over 92% utilisation of both node and link resources. Experimental simulations demonstrate a consistent end-to-end latency below 7 ms and a throughput of 18.4 Mbit/s, validating the proposed models' effectiveness in ensuring robust, real-time communication for industrial robot operations. This research contributes a scalable solution for dynamic 6G network resource management, providing a foundation for advanced industrial automation and intelligent manufacturing.