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

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

17 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8424-8428
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

Name2010 Ieee International Conference On Acoustics, Speech, And Signal Processing
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/201917/05/2019

Keywords

  • Deep Reinforcement Learning
  • Multi-Agent
  • NB-IoT
  • Random Access
  • Resource Configuration

Fingerprint

Dive into the research topics of 'Cooperative Deep Reinforcement Learning for Multiple-group NB-IoT Networks Optimization'. Together they form a unique fingerprint.

Cite this