Cooperative Deep Reinforcement Learning for Multiple-Group NB-IoT Networks Optimization

TL;DR

This paper introduces a cooperative deep reinforcement learning approach to optimize NB-IoT network configurations in real-time, significantly improving device access and data delivery without prior traffic knowledge.

Contribution

It proposes a novel multi-agent deep Q-learning method for online NB-IoT configuration optimization, outperforming traditional heuristic strategies.

Findings

CMA-DQN outperforms heuristic load estimation methods.

The approach adapts effectively without traffic statistics.

Significant improvement in device access and data transmission.

Abstract

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resources allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, the problem is to determine, in an online fashion at each Transmission Time Interval (TTI), the configurations that maximizes the long-term average number of IoT devices that are able to both access and deliver data. Given the complexity of optimal algorithms, a Cooperative Multi-Agent Deep Neural Network based Q-learning (CMA-DQN) approach is developed, whereby each DQN agent independently control a configuration variable for each group. The DQN agents are cooperatively trained in the same environment based on feedback regarding transmission outcomes. CMA-DQN is seen to considerably outperform conventional heuristic approaches based on load estimation.