Deep Reinforcement Learning for Downlink Power Control in Dense 5G Networks
This thesis examines the problem of downlink power allocation in dense 5Gnetworks, and attempts to develop a data-driven solution by employing deepreinforcement learning. We train and test multiple reinforcement learningagents using the deep Q-networks (DQN) algorithm, and the so-called Rainbowexten...
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| Format: | Others |
| Language: | English |
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KTH, Skolan för elektroteknik och datavetenskap (EECS)
2019
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265675 |