A Graph Attention Learning Approach to Antenna Tilt Optimization

@article{Jin2022AGA,
  title={A Graph Attention Learning Approach to Antenna Tilt Optimization},
  author={Yifei Jin and Filippo Vannella and Maxime Bouton and Jaeseong Jeong and Ezeddin Al Hakim},
  journal={2022 1st International Conference on 6G Networking (6GNet)},
  year={2022},
  pages={1-5}
}
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 adaptivity to dynamic network environments. The optimization of the antenna tilt provides a practical and cost-efficient method to improve coverage and capacity in the network. Previous methods based on Reinforcement Learning (RL) have shown effectiveness for tilt optimization by learning adaptive policies… 
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References

SHOWING 1-10 OF 28 REFERENCES

Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case.

TLDR
This paper proposes to use Graph Neural Networks (GNN) in combination with DRL, and its novel DRL+GNN architecture is able to learn, operate and generalize over arbitrary network topologies.

Reinforcement learning strategies for self-organized coverage and capacity optimization

TLDR
This paper presents reinforcement learning strategies for self-organized coverage and capacity optimization through antenna downtilt adaptation through Fuzzy Q-Learning based solution and proposes a cluster based strategy that tries to combine the benefits of both.

Is Machine Learning Ready for Traffic Engineering Optimization?

TLDR
This paper presents a novel distributed system for TE that implements a novel architecture that combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN) to minimize network congestion and compares it with DEFO, a network optimizer based on Constraint Programming.

Graph Attention Network-Based Multi-Agent Reinforcement Learning for Slicing Resource Management in Dense Cellular Network

TLDR
This paper proposes to formulate this challenge as a multi-agent reinforcement learning (MARL) problem in which each BS represents an agent and leverages graph attention network (GAT) to strengthen the temporal and spatial cooperation between agents and verifies the superiority of the GAT-based MARL algorithms through extensive simulations.

GAMA: Graph Attention Multi-agent reinforcement learning algorithm for cooperation

TLDR
This paper succeeded in extending the existing algorithm and obtaining a new algorithm called GAMA, which obtained the highest mean episode rewards compared to the baselines as well as excellent scalability through the integration with the attention mechanism.

A Fuzzy reinforcement learning approach for self-optimization of coverage in LTE networks

TLDR
An algorithm based on the combination of fuzzy logic and reinforcement learning is proposed and applied to the downtilt optimization problem to achieve the self-configuration, self-optimization, and self-healing functionalities required for future communication networks.

Spectral- and energy-efficient antenna tilting in a HetNet using reinforcement learning

TLDR
This paper investigates how the down-tilt of base-station (BS) antennas can be adjusted to maximize the user throughput fairness in a heterogeneous network, considering the impact of both a dynamic user distribution and capacity saturation of different transmission techniques.

Multi-Agent Actor-Critic with Hierarchical Graph Attention Network

TLDR
This work proposes a model that conducts both representation learning for multiple agents using hierarchical graph attention network and policy learning using multi-agent actor-critic, and demonstrates that the proposed model outperforms existing methods in several mixed cooperative and competitive tasks.

Graph Convolutional Reinforcement Learning

TLDR
Graph convolutional reinforcement learning is proposed, where graph convolution adapts to the dynamics of the underlying graph of the multi-agent environment, and relation kernels capture the interplay between agents by their relation representations.

Understanding Attention and Generalization in Graph Neural Networks

TLDR
This work proposes an alternative recipe and train attention in a weakly-supervised fashion that approaches the performance of supervised models, and, compared to unsupervised models, improves results on several synthetic as well as real datasets.