18348039. EMBEDDING NEURAL NETWORKS AS A MATRIX FOR NETWORK DEVICE IN WIRELESS NETWORK simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
EMBEDDING NEURAL NETWORKS AS A MATRIX FOR NETWORK DEVICE IN WIRELESS NETWORK
Organization Name
Inventor(s)
Vishal Murgai of Bangalore (IN)
Swaraj Kumar of Bangalore (IN)
Sukhdeep Singh of Bangalore (IN)
EMBEDDING NEURAL NETWORKS AS A MATRIX FOR NETWORK DEVICE IN WIRELESS NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 18348039 titled 'EMBEDDING NEURAL NETWORKS AS A MATRIX FOR NETWORK DEVICE IN WIRELESS NETWORK
Simplified Explanation
Embodiments of this patent application disclose a method and device for embedding neural networks as a matrix in a network device for wireless networks. The method involves receiving data from the network device and determining the Key Performance Indicators (KPIs) related to network anomalies using a Machine Learning (ML) model. The method further includes determining the correlation of the target KPI with the plurality of KPIs and generating a matrix indicating the relation of the target KPI with the plurality of KPIs. Additionally, the method involves optimizing the resource of the network device by embedding the matrix in the network device.
- The method involves receiving data from a network device.
- The ML model is used to determine the KPIs related to network anomalies.
- The correlation between the target KPI and the plurality of KPIs is determined using the ML model.
- A matrix is generated to indicate the relation of the target KPI with the plurality of KPIs.
- The network device's resource is optimized by embedding the matrix in the device.
Potential Applications
This technology can be applied in various wireless network devices, such as routers, access points, and network switches. It can be used in network management systems to improve anomaly detection and network performance optimization.
Problems Solved
1. Improved anomaly detection: By using ML models and embedding the matrix, this technology can help identify network anomalies more accurately and efficiently. 2. Network performance optimization: The optimization of network device resources through the embedded matrix can lead to improved overall network performance and resource allocation.
Benefits
1. Enhanced network security: Accurate anomaly detection can help identify potential security threats and prevent network breaches. 2. Improved network performance: By optimizing network device resources, this technology can enhance network performance, reduce latency, and improve user experience. 3. Efficient resource allocation: The embedded matrix allows for better resource allocation within the network device, leading to improved efficiency and cost-effectiveness.
Original Abstract Submitted
Embodiments herein disclose a method and a device for embedding neural networks as a matrix for a network device in wireless networks. The method includes receiving s from the network device. Further, the method also includes determining the KPI among the plurality of KPIs as target KPIs that related to a network anomaly using a ML model. Further, the method also includes determining a correlation of the target KPI with the plurality of KPIs for the network anomaly using the ML model. Further, the method also includes determining the matrix indicating a relation of the target KPI with the plurality of KPIs. Furthermore, the method includes optimizing a resource of the network device by embedding the matrix in the network device.
