Deep Reinforcement Learning for cell on/off energy saving on Wireless Networks
Author:
Affiliation:
1. Samsung Electronics R&D Institute UK,Surrey,UK,TW18 4QE
2. Samsung Research, Seoul R&D Campus, Umyeon dong,Seoul,Republic of Korea
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9685019/9685006/09685279.pdf?arnumber=9685279
Reference23 articles.
1. A Game Theory Based Strategy for Reducing Energy Consumption in Cognitive WSN
2. BOOST: Base station ON-OFF switching strategy for energy efficient massive MIMO HetNets
3. GreenBSN: Enabling Energy-Proportional Cellular Base Station Networks
4. Load-Based On/Off Scheduling for Energy-Efficient Delay-Tolerant 5G Networks
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1. Optimizing Energy Saving for Wireless Networks Via Offline Decision Transformer;ICC 2024 - IEEE International Conference on Communications;2024-06-09
2. Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching;2024 IEEE International Conference on Communications Workshops (ICC Workshops);2024-06-09
3. Deep Reinforcement Learning for Sleep Control in 5G and Beyond Radio Access Networks: An Overview;2024 International Wireless Communications and Mobile Computing (IWCMC);2024-05-27
4. QoE-aware network energy savings in 5G;2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN);2024-05-05
5. Energy Saving in Cellular Wireless Networks via Transfer Deep Reinforcement Learning;GLOBECOM 2023 - 2023 IEEE Global Communications Conference;2023-12-04
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