Cellular Network Data Collection via Unified Data Bus
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Solution Overview
Problem
Telecommunications companies face challenges in cost-effectively expanding their mobile network bandwidth and improving user experience while efficiently monitoring and managing radio access networks (RANs), which require extensive observability and specialized hardware/software.
Innovation Solution
Implementing a system that uses containerized applications, such as kubernetes clusters, to configure and manage 5G cellular networks, allowing for centralized data collection and observability across all domains through a data bus like Kafka, enabling efficient data transmission and storage, and providing a self-healing network architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized hardware and software are used for RAN monitoring and data collection, then measurement precision and reliability are improved, but device complexity and cost increase
Solution Approach 1:
The patent applies universality by implementing a standardized data collection framework that can monitor multiple RAN components (gNodeBs, eNodeBs, radio units) through a common architecture. The framework uses universal data formats and protocols that work across different hardware platforms, eliminating the need for specialized monitoring hardware for each component while maintaining comprehensive observability.
Solution Approach 2:
The patent introduces an intermediary data collection system that acts as a mediator between RAN components and monitoring systems. This intermediary layer standardizes data collection through a unified framework that translates various RAN data formats into a common representation, reducing the complexity of direct monitoring hardware while preserving measurement precision through standardized data protocols.
2Reliability
If multiple data transport streams are implemented for redundancy, then reliability is improved, but data volume and storage requirements increase
Solution Approach 1:
The patent merges multiple data transport streams into a unified data collection framework that consolidates redundant data transmissions. Instead of maintaining separate parallel streams for each RAN component, the system combines data from multiple sources into a single standardized data flow, reducing overall data volume while preserving reliability through the framework's error handling and validation mechanisms.
Solution Approach 2:
The patent implements selective data retention where redundant data transmissions are discarded after validation, and only essential data is stored. The system recovers critical information through error correction protocols while discarding duplicate data streams, reducing storage requirements while maintaining reliability through selective data recovery mechanisms.
3Ease of operation
If centralized data collection is implemented, then ease of operation and data management are improved, but data transmission overhead and network bandwidth increase
Solution Approach 1:
The patent segments data collection into localized processing at RAN components before centralized aggregation. Each component performs local data validation and filtering, segmenting the data stream to reduce unnecessary transmissions to the central collection point. This segmentation reduces network bandwidth consumption while maintaining ease of operation through distributed data management capabilities.
Solution Approach 2:
The patent implements partial data collection where only essential data is transmitted to the central system, rather than all raw data. The system performs selective data aggregation at intermediate nodes, transmitting only processed and validated data to the central collection point, reducing bandwidth overhead while maintaining effective data management through partial action at strategic locations.
Data Source
AI summary
A cellular network system for collecting data regarding operations in the cellular network includes a series of clusters comprising a first cluster. The first cluster includes a distributed unit (DU); a first data transport stream transmitting a first set of data from the DU; and a second data transport stream transmitting a second set of data from the DU. The first set of data includes the same data as the second set of data.