Deep Reinforcement Learning-based Video-Haptic Radio Resource Slicing in Tactile Internet

Georgios Kokkinis; Alexandros Iosifidis; Qi Zhang

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arXiv · 2503.14066

Deep Reinforcement Learning-based Video-Haptic Radio Resource Slicing in Tactile Internet

Georgios Kokkinis·Alexandros Iosifidis·Qi Zhang

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Abstract

Enabling video-haptic radio resource slicing in the Tactile Internet requires a sophisticated strategy to meet the distinct requirements of video and haptic data, ensure their synchronized transmission, and address the stringent latency demands of haptic feedback. This paper introduces a Deep Reinforcement Learning-based radio resource slicing framework that addresses video-haptic teleoperation challenges by dynamically balancing radio resources between the video and haptic modalities. The proposed framework employs a refined reward function that considers latency, packet loss, data rate, and the synchronization requirements of both modalities to optimize resource allocation. By catering to the specific service requirements of video-haptic teleoperation, the proposed framework achieves up to a 25% increase in user satisfaction over existing methods, while maintaining effective resource slicing with execution intervals up to 50 ms.

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BibTeX RIS

Georgios Kokkinis, Alexandros Iosifidis, Qi Zhang. 2025-03-18. Deep Reinforcement Learning-based Video-Haptic Radio Resource Slicing in Tactile Internet. https://arxiv.org/abs/2503.14066

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