18401096. MACHINE-LEARNING ARCHITECTURES FOR BROADCAST AND MULTICAST COMMUNICATIONS simplified abstract (GOOGLE LLC)
Contents
- 1 MACHINE-LEARNING ARCHITECTURES FOR BROADCAST AND MULTICAST COMMUNICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE-LEARNING ARCHITECTURES FOR BROADCAST AND MULTICAST COMMUNICATIONS - 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 Original Abstract Submitted
MACHINE-LEARNING ARCHITECTURES FOR BROADCAST AND MULTICAST COMMUNICATIONS
Organization Name
Inventor(s)
Jibing Wang of San Jose CA (US)
Erik Stauffer of Sunnyvale CA (US)
MACHINE-LEARNING ARCHITECTURES FOR BROADCAST AND MULTICAST COMMUNICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18401096 titled 'MACHINE-LEARNING ARCHITECTURES FOR BROADCAST AND MULTICAST COMMUNICATIONS
Simplified Explanation
The patent application describes machine-learning architectures for broadcast and multicast communications using a deep neural network (DNN) to direct communications to targeted user equipments (UEs) in a wireless communication system. The network entity processes feedback from UEs, modifies the DNN based on the feedback, transmits the modification to the UEs, updates the DNN, and directs communications to the targeted UEs using the modified DNN.
- Machine-learning architecture for broadcast and multicast communications
- Utilizes a deep neural network (DNN) to direct communications to targeted user equipments (UEs)
- Network entity processes feedback from UEs and modifies the DNN accordingly
- Transmits the modification to the UEs and updates the DNN
- Directs communications to the targeted UEs using the modified DNN
Potential Applications
This technology could be applied in:
- Optimizing broadcast and multicast communications in wireless networks
- Improving targeted content delivery to specific groups of users
Problems Solved
This technology helps solve:
- Efficiently directing broadcast and multicast communications to targeted UEs
- Enhancing the overall user experience by delivering relevant content
Benefits
The benefits of this technology include:
- Increased efficiency in delivering broadcast and multicast communications
- Enhanced user satisfaction by providing personalized content
- Improved network performance through targeted communication delivery
Potential Commercial Applications
Potential commercial applications of this technology include:
- Mobile network operators optimizing content delivery to subscribers
- Content providers improving targeted advertising strategies
Possible Prior Art
One possible prior art could be the use of machine learning algorithms in wireless communication systems to optimize content delivery to users.
Unanswered Questions
How does this technology impact network efficiency?
This technology can potentially improve network efficiency by directing communications more effectively to targeted user equipments, reducing unnecessary traffic and optimizing resource allocation.
What are the implications of modifying the DNN based on user feedback?
Modifying the DNN based on user feedback can lead to more personalized and relevant content delivery, but it also raises questions about data privacy and security.
Original Abstract Submitted
Techniques and apparatuses are described for machine-learning architectures for broadcast and multicast communications. A network entity processes broadcast or multicast communications using a deep neural network (DNN) to direct the one or more broadcast or multicast communications to a targeted group of user equipments (UEs) using the wireless communication system. The network entity receives feedback from at least one user equipment (UE) of the targeted group of UEs. The network entity determines a modification to the DNN based on the feedback. The network entity transmits an indication of the modification to the targeted group of UEs. The network entity updates the DNN with the modification to form a modified DNN. The network entity processes the broadcast or multicast communications using the modified DNN to direct the broadcast or multicast communications to the targeted group of UEs using the wireless communication system.
