Spatial Distribution Requirements for ML Positioning Training Data
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Solution Overview
Problem
Current AI/ML models for positioning tasks in telecommunications face challenges in achieving target positioning accuracy due to inadequate spatial distribution of training data, particularly in environments with varying clutter densities and synchronization errors, leading to poor generalization and increased computational complexity.
Innovation Solution
A method is introduced where a first device determines the requirement for spatial distribution of training samples for an ML model, transmitting this requirement to a second device, which then constructs a training dataset with samples that meet these requirements, ensuring sufficient spatial diversity and optimizing the model's performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training data is collected without specific spatial distribution requirements, then data collection is simpler and faster, but the ML model fails to achieve target positioning accuracy
Solution Approach 1:
The patent applies preliminary action by determining spatial distribution requirements for training samples before actual data collection begins. The first device establishes criteria for spatial diversity, minimum distance thresholds, and distribution patterns in advance, guiding the second device to collect data that automatically satisfies these pre-defined spatial requirements, thereby ensuring positioning accuracy without ad-hoc adjustments during collection
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting spatial distribution parameters such as minimum distance between samples, spatial density thresholds, and distribution patterns based on environmental conditions. The system modifies these parameters according to clutter density and synchronization errors in different scenarios, allowing the training data to adapt to varying environmental conditions while maintaining positioning accuracy
2Adaptability or versatility
If training samples have insufficient spatial diversity, then data collection is easier, but the ML model shows poor generalization performance
Solution Approach 1:
The patent applies local quality by ensuring that training samples have different spatial distribution characteristics suited to specific local environments. The system identifies different spatial patterns required for different deployment scenarios (e.g., urban canyons, open areas, indoor environments) and collects data with locally optimized spatial diversity, allowing the model to generalize better across varied environments rather than using a one-size-fits-all approach
Solution Approach 2:
The patent introduces another dimension by adding spatial distribution dimensions to the training data collection process. Beyond collecting standard positioning samples, the system explicitly controls and verifies spatial coordinates, distance relationships, and geometric distributions of training samples, transforming the data collection from a simple sampling process into a multi-dimensional spatial optimization process that enhances generalization
3Measurement precision
If spatial distribution requirements are enforced during training, then positioning accuracy improves, but computational complexity increases
Solution Approach 1:
The patent reduces computational complexity through preliminary action by pre-calculating and storing spatial distribution characteristics of training samples during the data collection phase. Spatial metrics such as distance matrices, distribution histograms, and diversity scores are computed in advance and stored with the training data, so that during model training, the system can directly utilize these pre-computed features rather than recalculating spatial relationships from raw coordinates, significantly reducing training computational burden
Data Source
AI summary
A method comprising: determining at a first device a requirement for a spatial distribution associated with training samples for a machine learning model, wherein the machine learning model is used in solving a positioning task; performing at least one of: transmitting, to the second device, information indicating the requirement for training the machine learning model, or training the machine learning model by using a training dataset comprising training samples satisfying the requirement.