Deep Reinforcement Learning-Aided Random Access

TL;DR

This paper introduces a deep reinforcement learning-based random access scheme for IoT networks, enabling the access point to predict active nodes and allocate resources efficiently, significantly improving packet rates over traditional methods.

Contribution

The paper proposes a novel DRL-aided random access scheme that predicts IoT node activity and allocates resources, enhancing network performance compared to existing standard schemes.

Findings

Significant increase in average packet rate with the proposed scheme.

Effective prediction of IoT node activity using DRL.

Faster training achieved through expert knowledge integration.

Abstract

We consider a system model comprised of an access point (AP) and K Internet of Things (IoT) nodes that sporadically become active in order to send data to the AP. The AP is assumed to have N time-frequency resource blocks that it can allocate to the IoT nodes that wish to send data, where N < K. The main problem is how to allocate the N time-frequency resource blocks to the IoT nodes in each time slot such that the average packet rate is maximized. For this problem, we propose a deep reinforcement learning (DRL)-aided random access (RA) scheme, where an intelligent DRL agent at the AP learns to predict the activity of the IoT nodes in each time slot and grants time-frequency resource blocks to the IoT nodes predicted as active. Next, the IoT nodes that are missclassified as non-active by the DRL agent, as well as unseen or newly arrived nodes in the cell, employ the standard RA scheme in order to obtain time-frequency resource blocks. We leverage expert knowledge for faster training of the DRL agent. Our numerical results show significant improvements in terms of average packet rate when the proposed DRL-aided RA scheme is implemented compared to the existing solution used in practice, the standard RA scheme.