Using Reinforcement Learning to Reduce Energy Consumption of Ultra-Dense Networks With 5G Use Cases Requirements
Author:
Affiliation:
1. Applied Digital Transformation Laboratory (ADiT-LAB), Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal
2. AtlanTTic Research Center, University of Vigo, Vigo, Spain
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10017253.pdf?arnumber=10017253
Reference28 articles.
1. Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management
2. A Slice Admission Policy Based on Reinforcement Learning for a 5G Flexible RAN
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