18273818. TRAINING AND USING A NEURAL NETWORK FOR MANAGING AN ENVIRONMENT IN A COMMUNICATION NETWORK simplified abstract (Telefonaktiebolaget LM Ericsson (publ))
Contents
- 1 TRAINING AND USING A NEURAL NETWORK FOR MANAGING AN ENVIRONMENT IN A COMMUNICATION NETWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING AND USING A NEURAL NETWORK FOR MANAGING AN ENVIRONMENT IN A COMMUNICATION NETWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
TRAINING AND USING A NEURAL NETWORK FOR MANAGING AN ENVIRONMENT IN A COMMUNICATION NETWORK
Organization Name
Telefonaktiebolaget LM Ericsson (publ)
Inventor(s)
David Sandberg of Sundbyberg (SE)
TRAINING AND USING A NEURAL NETWORK FOR MANAGING AN ENVIRONMENT IN A COMMUNICATION NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 18273818 titled 'TRAINING AND USING A NEURAL NETWORK FOR MANAGING AN ENVIRONMENT IN A COMMUNICATION NETWORK
Simplified Explanation
The abstract describes computer-implemented methods for training a Student Neural Network (SNN) and managing a communication network environment using the trained SNN. The SNN generates an action prediction matrix for the network environment, with action predictions for multiple nodes or resources. The training method involves using Reinforcement Learning to train a Teacher Neural Network (TNN) to generate action predictions, creating training data sets, and updating the parameters of the SNN.
- Training a Student Neural Network (SNN) for managing a communication network environment
- Using Reinforcement Learning to train a Teacher Neural Network (TNN) to generate action predictions
- Generating training data sets and updating parameters of the SNN
Potential Applications
The technology described in the patent application could be applied in various fields such as telecommunications, network management, and artificial intelligence.
Problems Solved
This technology helps in efficiently managing and optimizing communication network environments by predicting actions for nodes or resources, leading to improved performance and resource allocation.
Benefits
The benefits of this technology include enhanced network management, increased efficiency, optimized resource allocation, and improved overall performance of communication networks.
Potential Commercial Applications
Potential commercial applications of this technology include network management software, telecommunications services, and AI-driven network optimization tools.
Possible Prior Art
One possible prior art could be the use of neural networks in network management and optimization, but the specific approach described in the patent application may be novel.
Unanswered Questions
1. How does the training process of the Teacher Neural Network (TNN) differ from traditional neural network training methods? 2. What specific types of communication networks or environments can benefit the most from the action prediction matrix generated by the Student Neural Network (SNN)?
Original Abstract Submitted
Computer implemented methods for training a Student Neural Network, SNN, and for managing an environment of a communication network using a trained SNN are disclosed. The SNN is for generating an action prediction matrix for an environment in a communication network, the action prediction matrix comprising action predictions for a plurality of nodes or resources in the environment. The training method comprises using a Reinforcement Learning process to train a Teacher Neural Network, TNN, to generate an action prediction for a resource or node in the environment, and using the trained TNN to generate a first training data set including action predictions for individual nodes or resources. The training method further comprises generating a second training data set from the first training data set such that the second training data set includes action prediction matrices, and using the second training data set to update values of the parameters of the SNN.
