@inbook{7e22a1c35f124708ad77c21696981908,
title = "Cooperative Deep Reinforcement Learning for Multiple-group NB-IoT Networks Optimization",
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.",
keywords = "Deep Reinforcement Learning, Multi-Agent, NB-IoT, Random Access, Resource Configuration",
author = "Nan Jiang and Yansha Deng and Osvaldo Simeone and Arumugam Nallanathan",
year = "2019",
doi = "10.1109/ICASSP.2019.8682697",
language = "English",
series = "2010 Ieee International Conference On Acoustics, Speech, And Signal Processing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "8424--8428",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
address = "United States",
note = "44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
}