18261928. MULTI-AGENT POLICY MACHINE LEARNING simplified abstract (Telefonaktiebolaget LM Ericsson (publ))
Contents
- 1 MULTI-AGENT POLICY MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTI-AGENT POLICY MACHINE LEARNING - 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
MULTI-AGENT POLICY MACHINE LEARNING
Organization Name
Telefonaktiebolaget LM Ericsson (publ)
Inventor(s)
MULTI-AGENT POLICY MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18261928 titled 'MULTI-AGENT POLICY MACHINE LEARNING
Simplified Explanation
The abstract describes a method for operating a beam-forming wireless communication system with multiple radio nodes, each associated with an actor neural network and a critic network. The actor neural network controls the radio node based on feedback from the critic network.
- Actor neural networks are trained to control radio nodes based on feedback from associated critic networks.
- The method optimizes the operation of the wireless communication system by using neural networks to adjust beam-forming parameters.
- The system improves communication performance by learning from feedback provided by the critic networks.
- This innovation allows for more efficient and effective wireless communication by dynamically adjusting beam-forming parameters.
Potential Applications
This technology can be applied in:
- 5G networks
- Internet of Things (IoT) devices
- Smart city infrastructure
Problems Solved
- Optimizing beam-forming parameters in wireless communication systems
- Improving communication performance and efficiency
- Enhancing network coverage and reliability
Benefits
- Increased data transmission speeds
- Enhanced network capacity
- Improved signal quality and reliability
Potential Commercial Applications
Optimizing Wireless Communication Systems for Enhanced Performance
Possible Prior Art
One possible prior art in this field is the use of machine learning algorithms to optimize wireless communication systems. However, the specific combination of actor neural networks and critic networks for controlling beam-forming parameters may be a novel approach.
Unanswered Questions
1. How does the training process for the actor neural networks impact the overall performance of the wireless communication system? 2. Are there any limitations or challenges associated with implementing this method in real-world wireless communication systems?
Original Abstract Submitted
There is disclosed a method of operating a beam-forming wireless communication system, the system has a plurality of radio nodes, an actor neural network being associated to each radio node, wherein further to each actor neural network, there is associated a critic network. The method includes training each actor neural network, for controlling at least one associated radio node, based on learning feedback provided by its associated critic network, the learning feedback being based on operation information provided be the actor neural network for the critic network. The disclosure also pertains to related devices and methods.
