A graph attention learning approach to antenna tilt optimization
6G will move mobile networks towards increasing levels of complexity. To deal with this
complexity, optimization of network parameters is key to ensure high performance and timely …
complexity, optimization of network parameters is key to ensure high performance and timely …
Agent-time attention for sparse rewards multi-agent reinforcement learning
Sparse and delayed rewards pose a challenge to single agent reinforcement learning. This
challenge is amplified in multi-agent reinforcement learning (MARL) where credit …
challenge is amplified in multi-agent reinforcement learning (MARL) where credit …
BEERL: Both ends explanations for reinforcement learning
Deep Reinforcement Learning (RL) is a black-box method and is hard to understand
because the agent employs a neural network (NN). To explain the behavior and decisions …
because the agent employs a neural network (NN). To explain the behavior and decisions …
Off-policy learning in contextual bandits for remote electrical tilt optimization
We investigate the problem of Remote Electrical Tilt (RET) optimization using off-policy
learning techniques devised for Contextual Bandits (CBs). The goal in RET optimization is to …
learning techniques devised for Contextual Bandits (CBs). The goal in RET optimization is to …
Model Based Residual Policy Learning with Applications to Antenna Control
Non-differentiable controllers and rule-based policies are widely used for controlling real
systems such as telecommunication networks and robots. Specifically, parameters of mobile …
systems such as telecommunication networks and robots. Specifically, parameters of mobile …
Uplink-downlink joint antenna optimization in cellular systems with sample-efficient learning
In this paper, we jointly optimize the capacity and coverage of both uplink and downlink
transmissions by tuning the downtilt angle, vertical half-power beamwidth (HPBW), and …
transmissions by tuning the downtilt angle, vertical half-power beamwidth (HPBW), and …
Explainable Reinforcement Learning for Remote Electrical Tilt Optimization
A Mirzaian - 2022 - diva-portal.org
Controlling antennas' vertical tilt through Remote Electrical Tilt (RET) is an effective method
to optimize network performance. Reinforcement Learning (RL) algorithms such as Deep …
to optimize network performance. Reinforcement Learning (RL) algorithms such as Deep …
Model-based Residual Policy Learning for Sample Efficient Mobile Network Optimization
V Eriksson Möllerstedt - 2022 - diva-portal.org
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 …
complex systems. However, during the early phases of training, the performance is often …