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arXiv · 2410.03398

Reinforcenment Learning-Aided NOMA Random Access: An AoI-Based Timeliness Perspective

Felippe Moraes Pereira·Jamil de Araujo Farhat·João Luiz Rebelatto·Glauber Brante·Richard Demo Souza
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Abstract

In this paper, we investigate the age-of-information (AoI) of a power domain non-orthogonal multiple access (NOMA) network, where multiple internet-of-things (IoT) devices transmit to a common gateway in a grant-free random fashion. More specifically, we consider a framed setup composed of multiple time slots, and resort to the $Q$-learning algorithm to properly define, in a distributed manner, the time slot and the power level each IoT device transmits within a frame. In the proposed AoI-QL-NOMA scheme, the $Q$-learning reward is adapted with the aim of minimizing the average AoI of the network, while only requiring a single feedback bit per time slot, in a frame basis. Our results show that AoI-QL-NOMA significantly improves the AoI performance compared to some recently proposed schemes, without significantly reducing the network throughput.

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BibTeXRIS

Felippe Moraes Pereira, Jamil de Araujo Farhat, João Luiz Rebelatto, Glauber Brante, Richard Demo Souza. 2024-10-04. Reinforcenment Learning-Aided NOMA Random Access: An AoI-Based Timeliness Perspective. https://doi.org/10.1109/jiot.2024.3524177

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