Attention-based Open RAN Slice Management using Deep Reinforcement
  Learning
Fatemeh Lotfi Fatemeh Afghah Jonathan Ashdown
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.