Sökning: "antenna tilt"

Visar resultat 1 - 5 av 15 uppsatser innehållade orden antenna tilt.

1. 1. Decreasing Training Time of Reinforcement Learning Agents for Remote Tilt Optimization using a Surrogate Neural Network Approximator

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Jiaming Huang; [2023]
   Nyckelord :;

   Sammanfattning : One possible application of reinforcement learning in the telecommunication field is antenna tilt optimization. However, one of key challenges we face is that the use of handcrafted simulators as environments to provide information for agents is often time-consuming regarding training reinforcement learning agents. LÄS MER

3. 2. Explainable AI for Multi-Agent Control Problem

   Master-uppsats, Mälardalens universitet/Akademin för innovation, design och teknik

   Författare :Hanna Prokopova; [2023]
   Nyckelord :;

   Sammanfattning : This report presents research on the application of policy explanation techniques in the context of coordinated reinforcement learning (CRL) for mobile network optimization. The goal was to improve the interpretability and comprehensibility of decision-making processes in multi-agent environments, with a particular focus on the Remote Antenna Tilt (RET) problem. LÄS MER

4. 3. Model-based Residual Policy Learning for Sample Efficient Mobile Network Optimization

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Viktor Eriksson Möllerstedt; [2022]
   Nyckelord :Reinforcement Learning; Sample Efficiency; Model-based; Expert Policy; Remote Electrical Tilt; Telecommunication; Förstärkande inlärning; dataeffektivitet; modell-baserad; expert-policy; fjärrstyrning av antenners nedåtlutning; telekommunikation;

   Sammanfattning : Reinforcement learning is a powerful tool which enables an agent to learn how to control complex systems. However, during the early phases of training, the performance is often poor. LÄS MER

5. 4. Bridging Sim-to-Real Gap in Offline Reinforcement Learning for Antenna Tilt Control in Cellular Networks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Mayank Gulati; [2021]
   Nyckelord :reinforcement learning; transfer learning; simulation-to-reality; simulator; realworld; real-world network data; remote electrical tilt optimization; cellular networks; antenna tilt; network optimization.;

   Sammanfattning : Antenna tilt is the angle subtended by the radiation beam and horizontal plane. This angle plays a vital role in determining the coverage and the interference of the network with neighbouring cells and adjacent base stations. LÄS MER

7. 5. Bayesian Off-policy Sim-to-Real Transfer for Antenna Tilt Optimization

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Albin Larsson Forsberg; [2021]
   Nyckelord :Simulation to Reality; Coverage and Capacity Optimization; Remote Electrical Tilt; Reinforcement Learning; Bayesian Optimization; Domain Randomization; Off- policy Estimation; Simulering till Verklighet; Täckning och Kapacitetsoptimering; Fjärrstyrning av Elektrisk Lutning; Förstärkningsinlärning; Bayesiansk Optimering; Domänrandomisering; Off- policyskattning;

   Sammanfattning : Choosing the correct angle of electrical tilt in a radio base station is essential when optimizing for coverage and capacity. A reinforcement learning agent can be trained to make this choice. If the training of the agent in the real world is restricted or even impossible, alternative methods can be used. LÄS MER