A Graph Attention Learning Approach to Antenna Tilt Optimization

no code implementations • 27 Dec 2021 • Yifei Jin, Filippo Vannella, Maxime Bouton, Jaeseong Jeong, Ezeddin Al Hakim

GAQ relies on a graph attention mechanism to select relevant neighbors information, improve the agent state representation, and update the tilt control policy based on a history of observations using a Deep Q-Network (DQN).

Graph Attention Q-Learning +1

Dynamic Clustering in Federated Learning

no code implementations • 7 Dec 2020 • Yeongwoo Kim, Ezeddin Al Hakim, Johan Haraldson, Henrik Eriksson, José Mairton B. da Silva Jr., Carlo Fischione

To overcome these limitations, in this paper, we propose a three-phased data clustering algorithm, namely: generative adversarial network-based clustering, cluster calibration, and cluster division.

Clustering Federated Learning +4

A Safe Reinforcement Learning Architecture for Antenna Tilt Optimisation

no code implementations • 2 Dec 2020 • Erik Aumayr, Saman Feghhi, Filippo Vannella, Ezeddin Al Hakim, Grigorios Iakovidis

In the context of network management operations, Remote Electrical Tilt (RET) optimisation is a safety-critical application in which exploratory modifications of antenna tilt angles of base stations can cause significant performance degradation in the network.

Management reinforcement-learning +2

Remote Electrical Tilt Optimization via Safe Reinforcement Learning

no code implementations • 12 Oct 2020 • Filippo Vannella, Grigorios Iakovidis, Ezeddin Al Hakim, Erik Aumayr, Saman Feghhi

In this work, we model the RET optimization problem in the Safe Reinforcement Learning (SRL) framework with the goal of learning a tilt control strategy providing performance improvement guarantees with respect to a safe baseline.

reinforcement-learning Reinforcement Learning (RL) +2