AI for cell shaping in mobile networks
Cell shaping is used to configure radio antenna parameters (in this case, electrical downtilt) to improve the service quality.
Julien Forgeat explores an RL agent that automates the cell shaping. More precisely, the agent aims at tuning the tilt of multiple antennas to be generalized and adaptive to the random traffic demand and hotspots in an urban map. The training has been accelerated by leveraging RLlib’s scalability, an open source distributed RL library driven by UC Berkeley’s RISELab. RLlib’s support for distributed execution is critical, since this radio simulator is very compute intensive. Julien demonstrates that the RL agent’s tuning is well adapted to any scenario and outperforms the baseline algorithms.
What you'll learn
- Learn how RL can be used in cell shaping
Julien Forgeat is a senior specialist in artificial intelligence (AI) infrastructure at Ericsson. He joined Ericsson after spending several years working on network analysis and optimization. At Ericsson, Julien has worked on mobile learning, internet of things, and big data analytics, and now specializes in AI infrastructure. His research focuses on the software components required to run AI and machine learning workloads at scale. He holds an MEng in computer science from the National Institute of Applied Sciences in Lyon, France.
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