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DRASTIC: A Dynamic Resource Allocation Framework over 6G Network Slicing in Task-aware Closed-Loop Tactile Internet Applications

Narges Golmohammadi, Madan Mohan Rayguru, Sabur Baidya

Abstract

This work proposes a novel learning driven bandwidth optimization framework called DRASTIC (Dynamic Resource Allocation for Slicing in Task aware Closed loop tactile Internet applications). The proposed framework dynamically allocates resources among network slices supporting both enhanced Mobile Broadband (eMBB) and high reliable low latency communication (HRLLC) users. The algorithm ensures queue stability and meets delay targets with high probability under a Markov-modulated Poisson traffic, exploiting a Lyapunov guided advantage actor critic reinforcement learning technique. The proposed network model includes an open-loop eMBB queue whose arrival and departure are mainly driven by throughput demand, as well as a closed loop HRLLC queue that captures feedback and task execution effects. A task execution dependent dexterity index adjusts the effective arrival rate, creating a feedback aware interaction between the network and the task. A probabilistic delay constraint is incorporated into the objective via Lagrangian relaxation, yielding a min_max optimization framework that enforces latency guarantees while maximizing throughput for both types of users. Simulation results demonstrate that the proposed framework meets diverse Quality of Service (QoS) requirements, maintains queue stability under dynamic wireless and robotic task variation conditions, and outperforms other approaches.

DRASTIC: A Dynamic Resource Allocation Framework over 6G Network Slicing in Task-aware Closed-Loop Tactile Internet Applications

Abstract

This work proposes a novel learning driven bandwidth optimization framework called DRASTIC (Dynamic Resource Allocation for Slicing in Task aware Closed loop tactile Internet applications). The proposed framework dynamically allocates resources among network slices supporting both enhanced Mobile Broadband (eMBB) and high reliable low latency communication (HRLLC) users. The algorithm ensures queue stability and meets delay targets with high probability under a Markov-modulated Poisson traffic, exploiting a Lyapunov guided advantage actor critic reinforcement learning technique. The proposed network model includes an open-loop eMBB queue whose arrival and departure are mainly driven by throughput demand, as well as a closed loop HRLLC queue that captures feedback and task execution effects. A task execution dependent dexterity index adjusts the effective arrival rate, creating a feedback aware interaction between the network and the task. A probabilistic delay constraint is incorporated into the objective via Lagrangian relaxation, yielding a min_max optimization framework that enforces latency guarantees while maximizing throughput for both types of users. Simulation results demonstrate that the proposed framework meets diverse Quality of Service (QoS) requirements, maintains queue stability under dynamic wireless and robotic task variation conditions, and outperforms other approaches.

Paper Structure

This paper contains 24 sections, 27 equations, 11 figures.

Table of Contents

  1. Introduction
  2. Contributions
  3. Background and Related Work
  4. System Description and Problem Formulation
  5. Network Model
  6. HRLLC Traffic
  7. eMBB Traffic
  8. Channel Model and Achievable Rate
  9. Problem Formulation
  10. Proposed DRASTIC Framework
  11. Lyapunov Drift-Plus-Penalty Reformulation
  12. Deep Reinforcement Learning Realization
  13. eMBB state.
  14. HRLLC state.
  15. Action Space
  16. ...and 9 more sections

Figures (11)

  • Figure 1: 6G network slicing for tactile teleoperation: an HRLLC slice carries control/feedback traffic and an eMBB slice carries video traffic, coexisting with background mobile eMBB users under a shared PRB budget
  • Figure 2: O-RAN deployment of DRASTIC
  • Figure 3: DRASTIC architecture and learning loop.
  • Figure 4: Training convergence of DRASTIC (A2C): episodic return versus training episode (with smoothing).
  • Figure 5: Queue evolution during training: eMBB and HRLLC queue backlogs decrease and remain bounded as the learned scheduler converges.
  • ...and 6 more figures