Mean-Field Approximation based Scheduling for Broadcast Channels with Massive Receivers

TL;DR

This paper proposes a low-complexity, asymptotically optimal scheduling protocol for massive IoT devices in 6G networks using mean field approximation, balancing channel conditions and queue states.

Contribution

It introduces a novel buffer-aware multi-user diversity scheduling method based on mean field approximation for large-scale wireless networks.

Findings

The proposed protocol is of low complexity.

It is asymptotically optimal for large numbers of devices.

Numerical results validate the effectiveness of the approach.

Abstract

The emerging Industrial Internet of Things (IIoT) is driving an ever increasing demand for providing low latency services to massive devices over wireless channels. As a result, how to assure the quality-of-service (QoS) for a large amount of mobile users is becoming a challenging issue in the envisioned sixth-generation (6G) network. In such networks, the delay-optimal wireless access will require a joint channel and queue aware scheduling, whose complexity increases exponentially with the number of users. In this paper, we adopt the mean field approximation to conceive a buffer-aware multi-user diversity or opportunistic access protocol, which serves all backlogged packets of a user if its channel gain is beyond a threshold. A theoretical analysis and numerical results will demonstrate that not only the cross-layer scheduling policy is of low complexity but is also asymptotically optimal for a huge number of devices.