Attention-Based Open RAN Slice Management Using Deep Reinforcement Learning

Lotfi, Fatemeh; Afghah, Fatemeh; Ashdown, Jonathan

doi:10.1109/globecom54140.2023.10436850

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Cited by 3 publications

References 12 publications

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Open RAN LSTM Traffic Prediction and Slice Management Using Deep Reinforcement Learning

Lotfi,

Afghah

2023

2023 57th Asilomar Conference on Signals, Systems, and Computers

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Open RAN LSTM Traffic Prediction and Slice Management Using Deep Reinforcement Learning

Lotfi,

Afghah

2023

2023 57th Asilomar Conference on Signals, Systems, and Computers

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FlameFinder: Illuminating Obscured Fire Through Smoke With Attentive Deep Metric Learning

Rajoli,

Khoshdel,

Afghah

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Stepwise Optimal Inter-Slices Radio Resource Scheduling for Service-Level Agreement Assurance

Campos,

Almeida,

Junior

et al. 2024

Anais Do XLII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2024)

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In 5G networks and beyond, radio access networks (RANs) must be able to support multiple services with different service level agreements (SLAs). Network slicing is a critical concept in this context and it depends on an efficient approach for radio resource scheduling (RRS). Inter-slices RRS is responsible for allocating resource block groups (RBGs) to the slices to ensure their SLAs. Mainly motivated by the O-RAN initiative, several works in the literature have presented proposals based on machine learning (ML) to solve this problem. However, there is still a lack of problem formalization and an optimal strategy, which are both introduced in this work. Through simulations, we compare our approach with a state-of-the-art deep reinforcement learning (DRL) agent. The results show the excess resources employed by the agent when they are plentiful, suggesting an unnecessary increase in energy consumption. Additionally, we show the relevant gap between solutions when the resources are scarce. Finally, we discuss guidelines on how to improve ML-based approaches to the inter-slices RRS problem.

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Attention-Based Open RAN Slice Management Using Deep Reinforcement Learning

Cited by 3 publications

References 12 publications

Open RAN LSTM Traffic Prediction and Slice Management Using Deep Reinforcement Learning

Open RAN LSTM Traffic Prediction and Slice Management Using Deep Reinforcement Learning

FlameFinder: Illuminating Obscured Fire Through Smoke With Attentive Deep Metric Learning

Stepwise Optimal Inter-Slices Radio Resource Scheduling for Service-Level Agreement Assurance

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