公开(公告)号：US20250159499A1

公开(公告)日：2025-05-15

申请号：US18838346

申请日：2023-02-10

Applicant: Telecom Italia S.p.A.

Inventor： Lorenzo Mario AMOROSA ,  Giorgio GHINAMO ,  Davide MICHELI ,  Giuliano MURATORE ,  Marco SKOCAJ ,  Roberto VERDONE ,  Flavio ZABINI

IPC: H04W24/02 ,  H04W24/06

Abstract: A method for adjusting network cell parameters in a self-organizing cellular mobile network using a data processing system. The method involves creating an Environment that simulates the network based on radio measurement data, network performance data, and electromagnetic field simulation. A Deep Reinforcement Learning (DRL) Agent interacts with this Environment to simulate the effects of parameter changes on network performance. The Environment calculates and returns a Reward to the DRL Agent, which is used to train the Agent and estimate Q-values. The DRL Agent selects actions based on a policy that balances greedy actions, random actions, and constrained random actions. If the DRL Agent violates a predetermined constraint, the Environment returns a penalizing Reward.