Self-Learning Network Management Using Semantic Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Next generation networks face challenges such as high data traffic, latency, cost, security, energy efficiency, frequency band limitations, dynamic network topologies, scalability, and integration of advanced technologies like AI and edge computing, which current methods struggle to address effectively.
Innovation Solution
A self-learning management system utilizing probes, AI, and machine learning techniques to collect, convert, and analyze network data, incorporating human feedback, and generating synthetic data to optimize network performance and adapt to dynamic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional network probes are used for monitoring and management, then network performance can be tracked, but the system cannot effectively handle high data traffic volumes and generates massive amounts of data requiring manual analysis
Solution Approach 1:
The system employs self-learning AI models that automatically analyze network probe data without requiring manual intervention. The foundation models continuously learn from incoming data streams, automatically identifying patterns, anomalies, and optimization opportunities, thereby eliminating the need for manual data analysis while handling high traffic volumes
Solution Approach 2:
The patent transforms raw network data into semantic vectors through embedding models, changing the parameter representation from traditional network metrics to high-dimensional semantic spaces. This transformation enables more efficient processing and analysis of massive data volumes by AI models, improving monitoring effectiveness while reducing processing complexity
2Measurement precision
If advanced probing systems are deployed for high resolution network traffic processing, then monitoring accuracy improves, but deployment and maintenance costs increase significantly
Solution Approach 1:
The patent implements a universal foundation model that can perform multiple network analysis functions simultaneously - traffic classification, anomaly detection, performance optimization, and security monitoring. This multi-functional approach eliminates the need for deploying multiple specialized probing systems, achieving high measurement precision while reducing overall deployment and maintenance costs
Solution Approach 2:
The system uses synthetic data generation through generative adversarial networks (GANs) to create realistic network traffic samples. These synthetic copies enable training and testing of advanced analysis models without requiring extensive real-world data collection infrastructure, reducing deployment costs while maintaining high analysis accuracy
3Productivity
If manual analysis and follow-up actions are performed on network data, then data can be processed, but latency increases and real-time processing capability is reduced
Solution Approach 1:
The system implements closed-loop feedback mechanisms where AI models continuously monitor network performance and automatically generate and execute optimization actions. This automated feedback loop eliminates manual analysis delays, enabling real-time processing and immediate response to network conditions, thereby reducing latency while maintaining high productivity
Solution Approach 2:
The foundation models are pre-trained on extensive network data to learn patterns and relationships before deployment. This preliminary training enables the models to immediately analyze and respond to new network data without requiring manual setup or analysis protocols, achieving both high throughput and low latency from the start
4Reliability
If network probes generate and store massive volumes of data at high resolution, then monitoring capability improves, but infrastructure costs and energy consumption increase
Solution Approach 1:
The system extracts only the most relevant features and semantic information from massive network data streams using embedding models and AI analysis. Instead of storing and processing all raw data, the system extracts essential patterns and anomalies, maintaining monitoring reliability while dramatically reducing data storage requirements and associated energy consumption
Solution Approach 2:
The patent transforms raw network traffic data into compact semantic vectors that capture essential network states and behaviors. This parameter transformation reduces the dimensionality and storage requirements of network data while preserving the information needed for reliable monitoring and analysis, thereby reducing energy consumption without sacrificing reliability
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a system including: one or more probes in a communication network; a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: collecting data from the one or more probes and application program interfaces of network elements in the communication network; converting the data into semantic vectors using an embedding model; storing the semantic vectors in a vector database; using foundation models to generate outputs based on the semantic vectors; generating synthetic data using a generative adversarial network, wherein the synthetic data is used test the foundation models; using federated reinforcement to incorporate human feedback into the semantic vectors; and managing the communication network based on the outputs. Other embodiments are disclosed.