Reinforcement-Learning-Aided NOMA Random Access: An AoI-Based Timeliness Perspective
Felippe Moraes Pereira, Jamil Farhat, João Luiz Rebelatto, Glauber Gomes de Oliveira Brante, Richard Demo Souza · IEEE Internet of Things Journal · 2024
In this letter, we investigate the Age of Information (AoI) of a power domain nonorthogonal 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.