Semi-Contention-Free Access in IoT NOMA Networks: A Reinforcement Learning Framework

TL;DR

This paper introduces a reinforcement learning-based, model-free multiple access framework for IoT NOMA networks, improving data transmission efficiency and fairness while reducing detection failures and energy use.

Contribution

It proposes a novel semi-contention-free access scheme using policy gradient algorithms for power-domain NOMA IoT networks, enabling adaptive, data-driven resource management.

Findings

Enhanced system throughput and fairness.

Reduced data detection failures.

Lower energy consumption and access delay.

Abstract

The unprecedented surge of massive Internet of things (mIoT) traffic in beyond fifth generation (B5G) communication systems calls for transformative approaches for multiple access and data transmission. While classical model-based tools have been proven to be powerful and precise, an imminent trend for resource management in B5G networks is promoting solutions towards data-driven design. Considering an IoT network with devices spread in clusters covered by a base station, we present in this paper a novel model-free multiple access and data transmission framework empowered by reinforcement learning, designed for power-domain non-orthogonal multiple access networks to facilitate uplink traffic of small data packets. The framework supports two access modes referred to as contention-based and semi-contention-free, with its core component being a policy gradient algorithm executed at the base station. The base station performs access control and optimal radio resource allocation by periodically broadcasting two control parameters to each cluster of devices that considerably reduce data detection failures with a minimum computation requirement on devices. Numerical results, in terms of system and cluster throughput, throughput fairness, access delay, and energy consumption, demonstrate the efficiency and scalability of the framework as network size and traffic load vary.