A Hierarchical Optimization Framework Using Deep Reinforcement Learning for Task-Driven Bandwidth Allocation in 5G Teleoperation

TL;DR

This paper presents a hierarchical deep reinforcement learning framework for optimizing bandwidth allocation in 5G teleoperation, balancing URLLC and eMBB requirements for improved network and control performance.

Contribution

It introduces a novel two-level DRL-based hierarchical optimization framework combining network resource management and control tuning for 5G teleoperation.

Findings

Enhanced resource allocation efficiency in 5G networks.

Improved stability and responsiveness of teleoperation control.

Effective balancing of URLLC and eMBB traffic demands.

Abstract

The evolution of 5G wireless technology has revolutionized connectivity, enabling a diverse range of applications. Among these are critical use cases such as real time teleoperation, which demands ultra reliable low latency communications (URLLC) to ensure precise and uninterrupted control, and enhanced mobile broadband (eMBB) services, which cater to data-intensive applications requiring high throughput and bandwidth. In our scenario, there are two queues, one for eMBB users and one for URLLC users. In teleoperation tasks, control commands are received in the URLLC queue, where communication delays occur. The dynamic index (DI) controls the service rate, affecting the telerobotic (URLLC) queue. A separate queue models eMBB data traffic. Both queues are managed through network slicing and application delay constraints, leading to a unified Lagrangian-based Lyapunov optimization for efficient resource allocation. We propose a DRL based hierarchical optimization framework that consists of two levels. At the first level, network optimization dynamically allocates resources for eMBB and URLLC users using a Lagrangian functional and an actor critic network to balance competing objectives. At the second level, control optimization finetunes the best gains for robots, ensuring stability and responsiveness in network conditions. This hierarchical approach enhances both communication and control processes, ensuring efficient resource utilization and optimized performance across the network.