5G Network Status Analysis via ML Summaries
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Solution Overview
Problem
The rapid scalability and dynamic nature of 5G wireless networks pose challenges in maintaining accurate and up-to-date summaries of network status, leading to difficulties in monitoring performance, connectivity, and identifying issues in a timely manner.
Innovation Solution
A data management system that utilizes machine learning models to automatically collect, process, and store status data from various network components, enabling real-time monitoring and generating summaries in response to user or automated queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtualized cloud-based architecture is used for 5G networks, then scalability and deployment flexibility are improved, but network status monitoring complexity increases
Solution Approach 1:
The patent introduces a network status summary system as an intermediary layer between the complex virtualized network components and the monitoring interface. This system automatically generates simplified status summaries from detailed telemetry data, acting as a mediator that translates complex virtualized network states into manageable information formats for users and automated systems.
Solution Approach 2:
The patent replaces manual monitoring approaches with automated machine learning-based systems. Instead of requiring human operators to manually check network status across multiple virtualized components, the system uses ML models to automatically analyze telemetry data, generate status summaries, and detect anomalies, substituting mechanical manual processes with automated intelligent systems.
2Reliability
If real-time status monitoring is implemented, then network reliability is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts and filters only the critical information needed for network status monitoring from the vast amount of telemetry data generated by virtualized components. By identifying and extracting only essential status parameters rather than processing all raw data, the system maintains real-time monitoring capability while significantly reducing data processing requirements.
Solution Approach 2:
The patent performs preliminary data processing and aggregation at the source components (gNBs, DUs, CUs) before data reaches the central monitoring system. Status information is pre-processed and summarized at edge locations, reducing the volume of data that needs to be transmitted and processed centrally while maintaining real-time awareness of network status.
3Measurement precision
If detailed status tracking is maintained, then measurement precision is improved, but system response time increases
Solution Approach 1:
The patent pre-calculates and stores status summaries in advance, maintaining up-to-date aggregated information about network components. When queries are received, the system can quickly retrieve pre-computed summaries rather than performing complex real-time analysis, significantly reducing query response time while maintaining measurement precision through continuous background updates.
Solution Approach 2:
The patent implements a dynamic status summary system that automatically updates summaries in response to network events and component state changes. The system adjusts the level of detail and update frequency based on current network conditions, providing high precision when needed while optimizing response times during normal operation through intelligent data management.
Data Source
AI summary
Systems, devices, and automated processes are described to provide collection of wireless network status data, such as a 5G or other mobile network, and to automatically respond to queries regarding the status of the network. Systems and automated processes may obtain status data from a plurality of network components, for example a radio unit (RU), a distributed unit (DU), and a centralized unit (CU) associated with a cell site, ingest the obtained status data, including processing the status data with a machine learning model system, store the ingested status data in a data store, receive a user query related to the network, ingest the received user query using the MLM system, retrieve a result corresponding to the ingested query from the data store using the MLM system, generate, using the MLM system, a summary of the retrieved status data, and present the generated summary via a user interface.