Device Selection for the Coexistence of URLLC and Distributed Learning Services

by   Milad Ganjalizadeh, et al.

Recent advances in distributed artificial intelligence (AI) have led to tremendous breakthroughs in various communication services, from fault-tolerant factory automation to smart cities. When distributed learning is run over a set of wirelessly connected devices, random channel fluctuations and the incumbent services running on the same network impact the performance of both distributed learning and the coexisting service. In this paper, we investigate a mixed service scenario where distributed AI workflow and ultra-reliable low latency communication (URLLC) services run concurrently over a network. Consequently, we propose a risk sensitivity-based formulation for device selection to minimize the AI training delays during its convergence period while ensuring that the operational requirements of the URLLC service are met. To address this challenging coexistence problem, we transform it into a deep reinforcement learning problem and address it via a framework based on soft actor-critic algorithm. We evaluate our solution with a realistic and 3GPP-compliant simulator for factory automation use cases. Our simulation results confirm that our solution can significantly decrease the training delay of the distributed AI service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all network resources.


page 1

page 5

page 12


Interplay between Distributed AI Workflow and URLLC

Distributed artificial intelligence (AI) has recently accomplished treme...

Slicing-Based AI Service Provisioning on Network Edge

Edge intelligence leverages computing resources on network edge to provi...

Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning

The evolution of the future beyond-5G/6G networks towards a service-awar...

A Collaborative Statistical Actor-Critic Learning Approach for 6G Network Slicing Control

Artificial intelligence (AI)-driven zero-touch massive network slicing i...

SliceOps: Explainable MLOps for Streamlined Automation-Native 6G Networks

Sixth-generation (6G) network slicing is the backbone of future communic...

AI-assisted Improved Service Provisioning for Low-latency XR over 5G NR

Extended Reality (XR) is one of the most important 5G/6G media applicati...

To each route its own ETA: A generative modeling framework for ETA prediction

Accurate expected time of arrival (ETA) information is crucial in mainta...

Please sign up or login with your details

Forgot password? Click here to reset