Device Selection for the Coexistence of URLLC and Distributed Learning Services

• URL: http://arxiv.org/abs/2212.11805v1
• Date: Thu, 22 Dec 2022 15:36:15 GMT
• Title: Device Selection for the Coexistence of URLLC and Distributed Learning Services
• Authors: Milad Ganjalizadeh, Hossein Shokri Ghadikolaei, Deniz G\"und\"uz, Marina Petrova
• Abstract summary: We investigate a mixed service scenario where distributed AI workflow and ultra-reliable low latency communication (URLLC) services run concurrently over a network. We propose a risk-based formulation for device selection to minimize the AI training delays during its convergence period. We transform it into a deep reinforcement learning problem and address it via a framework based on soft actor-critic algorithm.
• Score: 12.093278114651524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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