Reinforcement-Learning-Based Resource Allocation for Energy-Harvesting-Aided D2D Communications in IoT Networks
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada
2. ECE Department, University of Tehran, Tehran, Iran
3. Department of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
Funder
Discovery Grant from the Natural Sciences and Engineering Research Council of Canada
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6488907/9866069/09709888.pdf?arnumber=9709888
Reference38 articles.
1. Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach
2. Antenna Selection Strategy for Energy Efficiency Maximization in Uplink OFDMA Networks: A Multi-Objective Approach
3. A Model-Based Reinforcement Learning Algorithm for Routing in Energy Harvesting Mobile Ad-Hoc Networks
4. Energy-Efficient D2D Communications Underlaying NOMA-Based Networks With Energy Harvesting
5. Performance Trade-off Between Uplink and Downlink in Full-Duplex Communications
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