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

2025-03-18 · Georgios Kokkinis, Alexandros Iosifidis, Qi Zhang

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.

📄 PDF Abstract BibTeX arXiv:2503.14066

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learning

Methods 이 논문이 사용한 방법론

Golden Queue Managers 설명 없음

Intent-aware Radio Resource Scheduling in a RAN Slicing Scenario using Reinforcement Learning

2023-08-02 · IEEE Transactions on Wireless Communications 2023 8 · Cleverson V. Nahum, Victor Hugo Lopes, Ryan M. Dreifuerst, Pedro Batista 외

Network slicing at the radio access network (RAN) domain, called RAN slicing, requires elasticity, efficient resource sharing, and customization. In this scenario, radio resource scheduling (RRS) is responsible for deali…

Management Scheduling

Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services

2021-03-18 · Sergio Martiradonna, Andrea Abrardo, Marco Moretti, Giuseppe Piro 외

The combination of cloud computing capabilities at the network edge and artificial intelligence promise to turn future mobile networks into service- and radio-aware entities, able to address the requirements of upcoming …

Autonomous Driving Cloud Computing Deep Reinforcement Learning Management+2

Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing

2021-09-16 · Hao Zhou, Melike Erol-Kantarci, Vincent Poor

Network slicing is a critical technique for 5G communications that covers radio access network (RAN), edge, transport and core slicing.The evolving network architecture requires the orchestration of multiple network reso…

Fairness Management Q-Learning reinforcement-learning+4

Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing

2020-09-14 · Yi Shi, Yalin E. Sagduyu, Tugba Erpek

The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing. Available communication resources (frequency-time blocks and transmit powers) and computational res…

Q-Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Deep Reinforcement Learning for Resource Management in Network Slicing

2018-05-17 · Rongpeng Li, Zhifeng Zhao, Qi Sun, Chi-Lin I 외

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, netw…

Deep Reinforcement Learning Management reinforcement-learning Reinforcement Learning+1