Neural Network Wireless Signal Quality Prediction

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current algorithms for measuring and reporting 5G radio link quality from user equipment (UE) to a base station suffer from high latency, as they require UE to measure wireless signal properties over time and then send reports to the base station, potentially delaying mitigation of poor signal quality.

Innovation Solution

The use of a neural network to predict wireless signal measurements based on reference signals, allowing for the generation of measurement reports without the need for prolonged measurement periods, thereby reducing latency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional measurement algorithms are used to measure wireless signal properties over time, then measurement precision is improved, but latency increases

Engineering Contradiction:

Improvesignal quality measurement accuracyVSAvoidmeasurement reporting latency

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The neural network is trained in advance on historical measurement data to learn the relationship between reference signals and measurement report quantities. During operation, the trained model immediately predicts measurement reports without requiring prolonged measurement periods, thus resolving the latency issue while maintaining accuracy through the pre-learned patterns

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional time-consuming signal measurement and processing algorithms with a neural network-based prediction system. The neural network processes reference signals to directly generate measurement report quantities, substituting the mechanical measurement process with an intelligent prediction mechanism that achieves both speed and accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If measurement reports are generated based on prolonged measurement periods, then reliability of signal quality assessment is improved, but productivity of network response is reduced

Engineering Contradiction:

Improvesignal quality assessment reliabilityVSAvoidnetwork response speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The neural network performs preliminary learning during the training phase using extensive measurement data, capturing reliable patterns of signal behavior. During actual operation, this pre-learned knowledge enables immediate prediction of measurement reports with high reliability, eliminating the need for prolonged measurement periods while maintaining assessment accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The neural network creates a predictive model that copies and generalizes the relationships between reference signals and measurement outcomes learned from historical data. This copied knowledge allows the system to generate reliable measurement reports instantly without repeating the full measurement process, thus improving network response productivity

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250119223A1Neural networks to predict qualities of wireless signals

Publication Date: 2025.04.10 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to use one or more neural networks to cause a prediction of a quality of one or more wireless signals to be transmitted, wherein the prediction is based, at least in part, on one or more reference signals. In at least one embodiment, a measurement report is to be generated by one or more neural networks.