Attention-based Open RAN Slice Management using Deep Reinforcement
Learning
- URL: http://arxiv.org/abs/2306.09490v1
- Date: Thu, 15 Jun 2023 20:37:19 GMT
- Title: Attention-based Open RAN Slice Management using Deep Reinforcement
Learning
- Authors: Fatemeh Lotfi, Fatemeh Afghah, Jonathan Ashdown
- Abstract summary: This paper introduces an innovative attention-based deep RL (ADRL) technique that leverages the O-RAN disaggregated modules and distributed agent cooperation.
Simulation results demonstrate significant improvements in network performance compared to other DRL baseline methods.
- Score: 6.177038245239758
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As emerging networks such as Open Radio Access Networks (O-RAN) and 5G
continue to grow, the demand for various services with different requirements
is increasing. Network slicing has emerged as a potential solution to address
the different service requirements. However, managing network slices while
maintaining quality of services (QoS) in dynamic environments is a challenging
task. Utilizing machine learning (ML) approaches for optimal control of dynamic
networks can enhance network performance by preventing Service Level Agreement
(SLA) violations. This is critical for dependable decision-making and
satisfying the needs of emerging networks. Although RL-based control methods
are effective for real-time monitoring and controlling network QoS,
generalization is necessary to improve decision-making reliability. This paper
introduces an innovative attention-based deep RL (ADRL) technique that
leverages the O-RAN disaggregated modules and distributed agent cooperation to
achieve better performance through effective information extraction and
implementing generalization. The proposed method introduces a value-attention
network between distributed agents to enable reliable and optimal
decision-making. Simulation results demonstrate significant improvements in
network performance compared to other DRL baseline methods.
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