18274320. METHOD AND NETWORK NODE FOR APPLYING MACHINE LEARNING IN A WIRELESS COMMUNICATIONS NETWORK simplified abstract (Telefonaktiebolaget LM Ericsson (publ))

METHOD AND NETWORK NODE FOR APPLYING MACHINE LEARNING IN A WIRELESS COMMUNICATIONS NETWORK

Organization Name

Telefonaktiebolaget LM Ericsson (publ)

Inventor(s)

[[:Category:Géza Szab� of Kecskemét (HU)|Géza Szab� of Kecskemét (HU)]][[Category:Géza Szab� of Kecskemét (HU)]]

[[:Category:Levente N�meth of Malé Dvorníky (SK)|Levente N�meth of Malé Dvorníky (SK)]][[Category:Levente N�meth of Malé Dvorníky (SK)]]

METHOD AND NETWORK NODE FOR APPLYING MACHINE LEARNING IN A WIRELESS COMMUNICATIONS NETWORK - A simplified explanation of the abstract

This abstract first appeared for US patent application 18274320 titled 'METHOD AND NETWORK NODE FOR APPLYING MACHINE LEARNING IN A WIRELESS COMMUNICATIONS NETWORK

Simplified Explanation

The abstract describes a method and network node for applying machine learning to train a communication policy controlling radio resources for message communication between a network node and a control node operating a remotely controlled device.

• The network node obtains messages during communication phases when an initial communication policy is applied for controlling Quality of Service (QoS).
• The network node trains a machine learning model based on the messages and the initial communication policy.
• The network node produces a second communication policy with adjusted QoS modes for communication phases.
• The network node determines a performance score for the second communication policy based on radio resources used during communication.

Potential Applications

This technology can be applied in various industries such as telecommunications, IoT, and network management systems.

Problems Solved

1. Efficient utilization of radio resources for communication. 2. Improved Quality of Service (QoS) management during communication phases.

Benefits

1. Enhanced communication performance. 2. Automated adjustment of communication policies based on machine learning. 3. Optimal allocation of radio resources for message communication.

Potential Commercial Applications

Optimizing network performance in telecommunications companies. Enhancing IoT device communication in smart home systems.

Possible Prior Art

Prior art may include existing methods for QoS management in communication networks and machine learning applications in network optimization.

Unanswered Questions

How does the machine learning model adapt to changing network conditions?

The article does not provide details on how the machine learning model dynamically adjusts to varying network parameters.

What are the potential limitations of the second communication policy in real-world scenarios?

The article does not address the potential constraints or drawbacks of implementing the second communication policy in practical applications.

Original Abstract Submitted

A method and a network node for applying machine learning for training a communication policy controlling radio resources for communication of messages between the network node and a control node operating a remotely controlled device is provided. The network node obtains said messages during one or more communication phases communicated when an initial first communication policy is applied for controlling a Quality of Service, QoS, mode. The network node trains a machine learning model based on said messages and the first communication policy. The network node produces a second communication policy including at least one adjusted QoS mode for at least one communication phase. The network node determines a performance score for the second communication policy in the communication phase(s) based on the radio resources used when communicating using the second communication policy.