RF Spatial Layout Reconstruction With Transformer-Based Models

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Solution Overview

Problem

Accurately estimating the layout of a spatial area based on radio frequency measurements is challenging due to noise and variance caused by factors such as radio frequency interference and physical obstructions, making it difficult to reconstruct the layout using signal measurements alone.

Innovation Solution

A machine learning model is trained to predict the layout of a spatial area using a set of multidimensional samples obtained while traversing the area, incorporating channel state information, timing data, and motion information, and employs transformer encoder and multilayer perceptron models to generate bounding boxes representing discrete portions of the area.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If radio frequency measurements are used to estimate spatial layout, then location estimation and beamforming can be improved, but measurement precision deteriorates due to noise and variance from interference and obstructions

Engineering Contradiction:

Improvelayout estimation accuracyVSAvoidradio frequency interference and obstructions

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

A machine learning model serves as an intermediary between noisy radio frequency measurements and accurate layout estimation. The model processes channel state information, timing data, and motion information to reconstruct spatial layout, filtering out the effects of radio frequency interference and obstructions that would otherwise degrade measurement precision.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces direct physical measurement methods with a computational approach using machine learning. Instead of relying on clean physical measurements that are corrupted by radio frequency interference, the system uses ML algorithms to infer layout from noisy signal data, effectively substituting the measurement process with an intelligent reconstruction process.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If machine learning model is trained to predict layout from noisy measurements, then layout estimation accuracy improves, but device complexity increases

Engineering Contradiction:

Improvelayout prediction accuracyVSAvoidmachine learning model complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The machine learning model processes information in segmented components: channel state information, timing data, and motion information are handled as separate input streams. The model architecture is divided into functional segments (encoder, processor, decoder) that progressively transform raw measurements into layout predictions, making the complex system more manageable and trainable.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by collecting and organizing multiple types of data (channel state information, timing data, motion information) before feeding them into the machine learning model. This pre-processing and structuring of data reduces the effective complexity of the model by providing it with already-organized input features.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If multiple types of data are combined for layout prediction, then prediction accuracy improves, but difficulty of detecting and measuring increases

Engineering Contradiction:

Improvespatial layout prediction accuracyVSAvoiddata collection and processing complexity

Core Design Contradiction:

Measurement precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The machine learning model is designed with multi-functionality to handle diverse data types (channel state information, timing data, motion information) and transform them into unified layout predictions. This universal approach consolidates multiple measurement difficulties into a single processing framework, reducing overall system complexity despite the variety of inputs.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250327891A1Spatial area layout reconstruction based on radio frequency measurements

Publication Date: 2025.10.23 QUALCOMM INC

AI summary

Certain aspects of the present disclosure provide techniques and apparatus for training and using machine learning models to predict a layout of a spatial area based on an input data set of samples from the spatial environment. An example method generally includes receiving an input data set including a plurality of samples from a spatial area. Each sample of the plurality of samples generally includes at least channel state information data. A machine learning model is trained to predict a layout of the spatial area based on the input data set. The predicted layout of the spatial area generally includes a plurality of bounding boxes defining different regions of the spatial area.