AI Control for Disaggregated Wireless Network Data
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Solution Overview
Problem
Network operators face challenges in utilizing disaggregated device, network, and core data from different solution providers, as conventional systems lack the capability to monetize this data effectively, and data collection is often external to the RAN and core networks.
Innovation Solution
Implementing data-driven artificial intelligence (AI) mechanisms within the communication network to process and analyze disaggregated data from multiple sources, enabling dynamic network decisions and command generation for network components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems are used to collect and manage network data, then data collection capabilities are external to the RAN and core networks, but network operators cannot effectively utilize or monetize disaggregated data from different solution providers
Solution Approach 1:
The patent embeds AI data collection and processing capabilities directly within the RAN and core network infrastructure, nesting intelligence inside the existing network architecture rather than maintaining external systems. This allows the network to autonomously collect, process, and monetize disaggregated data from multiple solution providers while managing complexity through integrated design.
Solution Approach 2:
The patent creates a universal AI platform within the network that can handle disaggregated data from multiple different solution providers and vendors through a single integrated system. This multi-functional approach enables the network to process diverse data types (device, network, core data) uniformly, making the system adaptable to various data sources without requiring separate specialized systems for each provider.
2Adaptability or versatility
If data collection capabilities are placed external to the RAN and core networks, then carriers can purchase AI systems, but data collection from multiple sources becomes difficult or impossible
Solution Approach 1:
The patent enables the network infrastructure to autonomously collect data from multiple sources through embedded AI capabilities that automatically interface with different solution providers. The system self-configures to collect disaggregated data from device, network, and core sources without requiring manual external coordination, making multi-source data collection operationally simple while highly adaptable.
Solution Approach 2:
The patent introduces an AI-mediated layer within the network architecture that serves as an intermediary between multiple solution providers and the network operator. This intermediary AI platform standardizes data collection from diverse sources, translating various provider formats into a unified structure that simplifies operation while maintaining compatibility with multiple vendors.
3Loss of information
If disaggregated data from different solution providers is collected, then network operators have access to comprehensive data, but conventional systems lack the capability to make sense of this data
Solution Approach 1:
The patent replaces conventional manual or rule-based data processing systems with AI-based automated processing embedded within the network. This substitution enables the system to automatically interpret and derive insights from disaggregated data from multiple solution providers, transforming raw data into actionable intelligence without requiring external analytical systems or manual processing.
Solution Approach 2:
The patent implements preliminary AI processing and data interpretation capabilities directly at the point of data collection within the network infrastructure. By performing data analysis and sense-making operations as data is initially collected from disaggregated sources, the system prepares information for monetization and operational use before it needs to be accessed, reducing subsequent processing requirements and enabling faster decision-making.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, retrieving, from an artificial intelligence (AI) repository, historic data associated with components of a disaggregated wireless communication network, where the components are associated with a plurality of vendors and communicate using a plurality of formats, and wherein the historic data is stored in a unified format; and training, by a processing system including a processor, an AI process comprising a machine learning (ML) model using the historic data. The ML model is trained to control network operations of a first set of the components of the disaggregated wireless communication network, and the AI process receives operational data of the first set of the components of the disaggregated wireless communication network and generates, based on the operational data, commands that control the network operations of the first set of the components of the disaggregated wireless communication network. Other embodiments are disclosed.