View source for Telefonaktiebolaget lm ericsson (publ) (20240292236). METHODS AND APPARATUSES FOR PROVISIONING A WIRELESS DEVICE WITH PREDICTION INFORMATION simplified abstract

=METHODS AND APPARATUSES FOR PROVISIONING A WIRELESS DEVICE WITH PREDICTION INFORMATION=


==Organization Name==

[[:Category:telefonaktiebolaget lm ericsson (publ)|telefonaktiebolaget lm ericsson (publ)]]


[[Category:telefonaktiebolaget lm ericsson (publ)]]

==Inventor(s)==

[[:Category:Henrik Ryd�n of Stockholm (SE)|Henrik Ryd�n of Stockholm (SE)]][[Category:Henrik Ryd�n of Stockholm (SE)]]

[[:Category:Andres Reial of Höllviken (SE)|Andres Reial of Höllviken (SE)]][[Category:Andres Reial of Höllviken (SE)]]

[[:Category:Sina Maleki of Malmö (SE)|Sina Maleki of Malmö (SE)]][[Category:Sina Maleki of Malmö (SE)]]

[[:Category:Ali Nader of Malmö (SE)|Ali Nader of Malmö (SE)]][[Category:Ali Nader of Malmö (SE)]]

==METHODS AND APPARATUSES FOR PROVISIONING A WIRELESS DEVICE WITH PREDICTION INFORMATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240292236 titled 'METHODS AND APPARATUSES FOR PROVISIONING A WIRELESS DEVICE WITH PREDICTION INFORMATION

The present disclosure pertains to a method performed by a network node to provide prediction information to a wireless device for predicting radio signal measurements between the device and a base station. The prediction information includes a denoising autoencoder and at least one candidate noising pattern. 

* Receiving wireless device capability information from the device
* Obtaining the denoising autoencoder and/or candidate noising pattern based on the capability information
* Transmitting an indication of the prediction information to the wireless device

Key Features and Innovation:
- Utilizes denoising autoencoder and candidate noising patterns for predicting radio signal measurements
- Tailors prediction information based on wireless device capability
- Enhances communication between wireless devices and base stations

Potential Applications:
- Improving signal prediction in wireless communication systems
- Enhancing network performance and reliability
- Optimizing resource allocation in wireless networks

Problems Solved:
- Inaccurate radio signal measurement predictions
- Inefficient use of network resources
- Limited communication reliability between devices and base stations

Benefits:
- Enhanced prediction accuracy
- Improved network efficiency
- Increased communication reliability

Commercial Applications:
Predictive maintenance in IoT devices
Optimizing network performance in 5G technology
Enhancing signal prediction in smart cities

Prior Art:
Readers can explore prior research on denoising autoencoders in wireless communication systems and signal prediction technologies.

Frequently Updated Research:
Stay updated on advancements in denoising autoencoders for wireless communication systems and signal prediction techniques.

Questions about Wireless Signal Prediction:
1. How does the denoising autoencoder improve signal prediction accuracy?
2. What are the potential challenges in implementing candidate noising patterns for signal prediction?


==Original Abstract Submitted==

the present disclosure relates to a computer-implemented method (), performed by a first network node, for provisioning a wireless device with prediction information for allowing the wireless device to predict a radio signal measurement between the wireless device and a base station, wherein the prediction information comprises a denoising autoencoder and at least one candidate noising pattern. the method comprises: receiving (), from the wireless device, wireless device capability information: obtaining (), based on the wireless device capability information, the denoising autoencoder and/or the at least one candidate noising pattern for predicting a radio signal measurement; and transmitting () an indication of the prediction information to the wireless device. the present disclosure also relates to a first network node, a wireless device and a computer program.



[[Category:H04W24/02]]
[[Category:G06N3/0455]]
[[Category:CPC_H04W24/02]]