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Deep Reinforcement Learning for Downlink Power Control in Dense 5G Networks
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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 Rainbowextensions of DQN. The performance of each agent is tested on 5G UrbanMacro simulation scenarios, and is benchmarked against a fixed power allocationapproach. Our test results show that the DQN models are successful atimproving data rates at cell-edge, while generalizing well to previously unseensimulation scenarios. In addition, the agents induce throughput balancing effects,i.e., achieve fairness among users, in networks with full-downlink-buffertraffic by properly designing the reward signal.

Abstract [sv]

Det här examensarbetet undersöker kraftallokering i nedlänksriktning för täta5G-nätverk och försöker utveckla en datadriven lösning genom användning avdeep reinforcement learning. Vi tränar och testar flera reinforcement learningagentermed deep Q-networks (DQN) algoritmen, och de så kallade ”Rainbowextensions” av DQN. Prestandan av varje agent testas på storskaliga tätortsscenarionför 5G, och jämförs med en fast kraftallokeringsmetod.Våra testresultatvisar att DQN-modellerna leverar högre överföringshastigheter vid cellkanten,samtidigt som metoden fungerar väl för okända simuleringsscenarion. Utöverhastighetsökningen så balanserar agenterna dataflödet, vilket leder till rättvisallokering bland användarna i nätverk med ”full-downlink-buffer”-trafik genomatt korrekt designa belöningssignalen.

Place, publisher, year, edition, pages
2019. , p. 50
Series
TRITA-EECS-EX ; 2019:603
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-265675OAI: oai:DiVA.org:kth-265675DiVA, id: diva2:1380818
External cooperation
Ericsson AB
Educational program
Master of Science - Information and Network Engineering
Examiners
Available from: 2019-12-19 Created: 2019-12-19 Last updated: 2019-12-19Bibliographically approved

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