Wireless Power Control via Meta-Reinforcement Learning

Ziyang Lu, M. Cenk Gursoy

Research output: Chapter in Book/Entry/PoemConference contribution


In this paper, the power control problem is addressed in a wireless interference network in which there exist multiple transmitter-receiver pairs sharing the same bandwidth for information exchange. The goal is to train a common deep neural network (DNN) for power allocation at each transmitter. Recent studies in the literature have addressed this problem via deep reinforcement learning (DRL). However, training DRL algorithms can become costly in wireless networks since the DRL algorithm may converge slowly in specific problems and hence require a large amount of training data. Besides, the converged model may fail in a new environment, which is not preferable in a wireless network due to its dynamic and time-varying nature. In this work, we address these considerations by proposing a meta-DRL framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). Within the proposed framework, a common initialization is trained for similar power control tasks. From the initialization, we show that only a few gradient descent steps are required for adapting to an unseen task. Simulation results demonstrate that the proposed framework can outperform conventional DRL and joint-learning (which trains a global model for similar tasks) for power control in wireless interference networks.

Original languageEnglish (US)
Title of host publicationICC 2022 - IEEE International Conference on Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538683477
StatePublished - 2022
Event2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of
Duration: May 16 2022May 20 2022

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2022 IEEE International Conference on Communications, ICC 2022
Country/TerritoryKorea, Republic of


  • meta-reinforcement learning
  • model-agnostic meta-learning
  • power control
  • wireless interference networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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