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Conference Papers Year : 2021

Deep-IRSA: A Deep Reinforcement Learning Approach to Irregular Repetition Slotted ALOHA

Abstract

The Internet of Things (IoT) aims to connect billions of devices, most of which are power and memory-constrained. Such constraints require efficient network access. "Irregular Repetition Slotted Aloha'" (IRSA) meets such requirements. In this paper, we optimize IRSA using Deep Reinforcement Learning to obtain Deep-IRSA, and introduce variants that allow re-transmission and user priority classes. We observe the learned degree distribution and throughput, showing that Deep-IRSA performs excellently, is generic, and could well replace known approaches for smaller frame sizes and IRSA variants.
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Dates and versions

hal-03533523 , version 1 (18-01-2022)

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Cite

Ibrahim Ayoub, Iman Hmedoush, Cedric Adjih, Kinda Khawam, Samer Lahoud. Deep-IRSA: A Deep Reinforcement Learning Approach to Irregular Repetition Slotted ALOHA. PEMWN 2021 - 10th IFIP International Conference on Performance Evaluation and Modeling in Wireless and Wired Networks, Nov 2021, Ottawa / Virtual, Canada. pp.1-6, ⟨10.23919/PEMWN53042.2021.9664720⟩. ⟨hal-03533523⟩
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