Wireless AI Data Collection With Event-Triggered Reporting
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies in data collection for artificial intelligence (AI) applications, particularly in minimizing overhead and latency, and lack optimized mechanisms for triggering events based on data novelty, network configuration changes, and model performance metrics.
Innovation Solution
Implementations provide enhanced data collection processes in wireless communications systems, utilizing AI models to trigger events based on data sample novelty, network configuration changes, and model performance metrics, enabling efficient data collection and reporting through UEs and network equipment (NEs), with support for multiple logging configurations and improved signaling accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data collection methods are used in wireless communication systems, then data can be collected for AI applications, but signaling overhead and latency increase
Solution Approach 1:
The patent extracts and separates trigger event conditions from general data collection processes. By identifying specific triggering conditions (data novelty thresholds, configuration changes, model performance metrics), the system only initiates data collection when necessary, rather than continuously collecting data. This extraction of trigger conditions reduces unnecessary signaling overhead while maintaining adequate data collection for AI applications.
Solution Approach 2:
The patent changes the parameter of data collection from continuous to event-triggered based on multiple conditions including data novelty measures, configuration parameter changes, and model performance metrics. This parameter change allows the system to adapt data collection frequency dynamically, reducing signaling overhead when conditions are stable while ensuring data collection when changes occur that affect AI model performance.
2Quantity of substance
If continuous data collection is performed for AI applications, then sufficient training data is available, but latency increases
Solution Approach 1:
The patent implements preliminary action by pre-defining trigger events and conditions before data collection begins. The system pre-establishes thresholds for data novelty, configuration changes, and model performance metrics that will trigger data collection. This preliminary configuration allows the system to respond immediately when conditions are met, reducing latency compared to continuous monitoring without predefined triggers.
Solution Approach 2:
The patent implements periodic action through event-triggered data collection based on specific conditions occurring at irregular intervals. Rather than continuous collection, the system periodically checks for trigger events (data novelty thresholds, configuration changes, model performance degradation) and collects data only when these events occur. This periodic, event-driven approach reduces overall latency while ensuring data collection when it is most needed for AI model updates.
3Measurement precision
If data collection is triggered by multiple conditions (novelty, configuration changes, performance metrics), then data collection accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the trigger event monitoring into distinct, independent conditions: data novelty measures, configuration parameter changes, and model performance metrics. Each condition is monitored and evaluated separately with its own thresholds and evaluation criteria. This segmentation allows the system to maintain high measurement precision through multiple independent checks while managing complexity by treating each condition as a separate, manageable module rather than a monolithic complex system.
Data Source
AI summary
Various aspects of the present disclosure relate to data collection for artificial intelligence in wireless communications. An apparatus, such as a UE, receives control information comprising a first field indicating a trigger event for a data collection process, where the trigger event is based at least in part on one or more of a data sample novelty measure, a change in a configuration parameter of a node, a change in a condition of a node, a model performance metric, or model monitoring output. The apparatus triggers, based at least in part on an occurrence of the trigger event, a data collection process to generate a reporting set from a set of measured samples, and processes the reporting set based at least in part on the control information including to one or more of report the reporting set to a different node or log the reporting set to generate a logged reporting set.