Google llc (20240135175). MACHINE-LEARNING ARCHITECTURES FOR BROADCAST AND MULTICAST COMMUNICATIONS simplified abstract
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 20240135175 titled 'MACHINE-LEARNING ARCHITECTURES FOR BROADCAST AND MULTICAST COMMUNICATIONS
Simplified Explanation
In this patent application, a network entity utilizes a deep neural network (DNN) to direct broadcast or multicast communications to a targeted group of user equipments (UEs) in a wireless communication system. The network entity receives feedback from at least one UE in the targeted group, determines modifications to the DNN based on the feedback, transmits indications of the modifications to the UEs, updates the DNN with the modifications, and processes the communications using the modified DNN.
- Machine-learning architecture for broadcast and multicast communications
- Utilization of a deep neural network (DNN) to direct communications to targeted UEs
- Feedback mechanism from UEs to modify the DNN
- Transmission of modifications to UEs and updating the DNN accordingly
- Processing of communications using the modified DNN
Potential Applications
This technology can be applied in various industries such as telecommunications, broadcasting, and content delivery networks to efficiently target specific groups of users with broadcast or multicast communications.
Problems Solved
1. Efficiently directing broadcast or multicast communications to targeted groups of UEs 2. Improving the overall performance and effectiveness of wireless communication systems
Benefits
1. Enhanced user experience with personalized and targeted communications 2. Increased efficiency in delivering broadcast or multicast content 3. Real-time adaptation to user feedback for improved communication delivery
Potential Commercial Applications
Optimized Targeted Communication Delivery in Wireless Networks
Possible Prior Art
There may be prior art related to machine learning in wireless communication systems, feedback mechanisms for network optimization, and targeted content delivery to user groups.
What are the potential security implications of using machine learning in wireless communication systems?
Using machine learning in wireless communication systems can introduce security risks such as data breaches, unauthorized access, and manipulation of the DNN models. It is crucial to implement robust security measures to protect sensitive user data and ensure the integrity of the communication system.
How can the network entity ensure the accuracy and reliability of the modifications made to the DNN based on user feedback?
The network entity can implement validation mechanisms, conduct thorough testing, and monitor the performance of the modified DNN in real-world scenarios to ensure the accuracy and reliability of the modifications. Regular updates and refinement of the DNN based on user feedback can also help improve the overall effectiveness of the communication system.
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.
