Experience Replay-Based Power Control for Sum-Rate Maximization in Multi-Cell Networks
Author:
Affiliation:
1. Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali, Mexico
Funder
National Council of Science and Technology (CONACyT, Mexico) through the Fondo Sectorial de Investigación para la Educación
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/5962382/9942447/09870169.pdf?arnumber=9870169
Reference11 articles.
1. Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
2. Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches
3. An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel
4. Playing atari with deep reinforcement learning;mnih;arXiv 1312 5602,2013
5. Centralized and Distributed Deep Reinforcement Learning Methods for Downlink Sum-Rate Optimization
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1. Accelerated resource allocation based on experience retention for B5G networks;Journal of Network and Computer Applications;2023-04
2. Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey;Energies;2023-02-06
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