Research Article Open Access

Combining Q-Learning and Multi-Layer Perceptron Models on Wireless Channel Quality Prediction

Andrea L. Piroddi1 and Maurizio Torregiani2
  • 1 Università di Bologna, Italy
  • 2 University of the People, United States

Abstract

One of the most complex challenges that wireless communication systems will face in the coming years is the management of the radio resource. In the next years, the growth of mobile devices, forecast (CISCO, 2020), will lead to the coexistence of about 8.8 billion mobile devices with a growing trend for the following years. This scenario makes the reuse of the radio resource particularly critical, which for its part will not undergo significant changes in terms of bandwidth availability. One of the biggest problems to be faced will be to identify solutions that optimize its use. This work shows how a combined approach of a Reinforcement Learning model and a Supervised Learning model (Multi-Layer Perceptron) can provide good performance in the prediction of the channel behavior and on the overall performance of the transmission chain, even for Cognitive Radio with limited computational power, such as NB-IoT, LoRaWan, Sigfox.

American Journal of Engineering and Applied Sciences
Volume 14 No. 1, 2021, 139-151

DOI: https://doi.org/10.3844/ajeassp.2021.139.151

Submitted On: 17 January 2021 Published On: 10 March 2021

How to Cite: Piroddi, A. L. & Torregiani, M. (2021). Combining Q-Learning and Multi-Layer Perceptron Models on Wireless Channel Quality Prediction. American Journal of Engineering and Applied Sciences, 14(1), 139-151. https://doi.org/10.3844/ajeassp.2021.139.151

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Keywords

  • Q-Learning
  • Network Research
  • OpenaAI Gym
  • Network Simulator
  • ns-3
  • Supervised Learning
  • Multi-Layer Perceptron