Deep Reinforcement Learning for Random Access in Machine-Type Communication

TL;DR

This paper explores the use of deep reinforcement learning to develop adaptive transmission policies for random access in machine-type communication, improving throughput and fairness over traditional backoff methods.

Contribution

It introduces a DRL-based transmission policy that adapts to traffic conditions and balances throughput with fairness, outperforming exponential backoff schemes.

Findings

DRL-based policy achieves higher throughput.

The policy improves fairness among users.

Adaptive to different traffic rates.

Abstract

Random access (RA) schemes are a topic of high interest in machine-type communication (MTC). In RA protocols, backoff techniques such as exponential backoff (EB) are used to stabilize the system to avoid low throughput and excessive delays. However, these backoff techniques show varying performance for different underlying assumptions and analytical models. Therefore, finding a better transmission policy for slotted ALOHA RA is still a challenge. In this paper, we show the potential of deep reinforcement learning (DRL) for RA. We learn a transmission policy that balances between throughput and fairness. The proposed algorithm learns transmission probabilities using previous action and binary feedback signal, and it is adaptive to different traffic arrival rates. Moreover, we propose average age of packet (AoP) as a metric to measure fairness among users. Our results show that the proposed policy outperforms the baseline EB transmission schemes in terms of throughput and fairness.