• Corpus ID: 232270102

Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services

@article{Martiradonna2021DeepRL,
  title={Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services},
  author={Sergio Martiradonna and Andrea Abrardo and Marco Moretti and Giuseppe Piro and Gennaro Boggia},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.10277}
}
The combination of cloud computing capabilities at the network edge and artificial intelligence promise to turn future mobile networks into serviceand radio-aware entities, able to address the requirements of upcoming latency-sensitive applications. In this context, a challenging research goal is to exploit edge intelligence to dynamically and optimally manage the Radio Access Network Slicing (that is a less mature and more complex technology than fifth-generation Network Slicing) and Radio… 

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