TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement Learning

TL;DR

TinyQMIX introduces a lightweight multi-agent reinforcement learning approach for distributed access control in mMTC, enabling quick adaptation to dynamic traffic and reducing access delays without centralized coordination.

Contribution

The paper proposes TinyQMIX, a novel lightweight multi-agent deep reinforcement learning model for distributed resource selection in mMTC, capable of rapid adaptation to traffic changes.

Findings

TinyQMIX achieves low access delay in dynamic traffic scenarios.

The model adapts quickly to traffic changes without centralized control.

Numerical results validate the effectiveness of TinyQMIX.

Abstract

Distributed access control is a crucial component for massive machine type communication (mMTC). In this communication scenario, centralized resource allocation is not scalable because resource configurations have to be sent frequently from the base station to a massive number of devices. We investigate distributed reinforcement learning for resource selection without relying on centralized control. Another important feature of mMTC is the sporadic and dynamic change of traffic. Existing studies on distributed access control assume that traffic load is static or they are able to gradually adapt to the dynamic traffic. We minimize the adaptation period by training TinyQMIX, which is a lightweight multi-agent deep reinforcement learning model, to learn a distributed wireless resource selection policy under various traffic patterns before deployment. Therefore, the trained agents are able to quickly adapt to dynamic traffic and provide low access delay. Numerical results are presented to support our claims.