SINR Estimation Using Regression and Machine Learning
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Solution Overview
Problem
Current systems for estimating Signal to Interference and Noise Ratio (SINR) in wireless network environments lack high geographical resolution, leading to variations in SINR across small areas due to environmental conditions such as tall buildings and fluctuations in connected devices.
Innovation Solution
A method and system that collect wireless connection datasets with measured environment parameters and SINR, generate a regression model to estimate SINR based on these parameters, and train an SINR optimization machine learning algorithm to refine the estimates, accounting for edge cases using simulated wireless network environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current systems estimate SINR using traditional methods, then the estimation process is simple, but the geographical resolution is low and accuracy is insufficient
Solution Approach 1:
The patent segments the SINR estimation problem into multiple components: collecting wireless connection datasets with environment parameters, generating a regression model for initial estimation, and training a machine learning algorithm for optimization. This segmentation allows each component to focus on specific aspects, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent performs preliminary actions by collecting and storing wireless connection datasets with measured environment parameters before generating the regression model. This preliminary data collection and preprocessing enables the model to be trained on real-world conditions, improving estimation accuracy without adding complexity during the actual estimation process.
2Measurement precision
If high geographical resolution SINR estimation is implemented, then accuracy improves, but data collection and processing complexity increases
Solution Approach 1:
The patent extracts relevant features from the collected wireless connection datasets, such as environment parameters (obstacle density, building height, connected devices). By extracting and focusing on these key parameters rather than processing all raw data, the system achieves high geographical resolution estimation while managing data volume through feature selection.
Solution Approach 2:
The patent transforms the estimation approach by changing from direct physical measurement to parameter-based prediction. The regression model and machine learning algorithm process environment parameters (CQI, weather conditions, environment complexity) to estimate SINR, converting physical measurement problems into computational parameter relationships that can be processed more efficiently.
3Measurement precision
If environment parameters are collected from multiple sources, then estimation accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent merges multiple data sources and processing steps into a unified machine learning framework. The regression model and machine learning algorithm work together to process environment parameters from various sources (wireless connection datasets, obstacle information, weather conditions) and produce optimized SINR estimates, combining complexity management through integration rather than separate processing stages.
Data Source
AI summary
A method for estimating signal to interference and noise ratio (SINR) in a wireless network environment is provided. The method may include collecting a plurality of wireless connection datasets about the wireless network environment. Each of the plurality of wireless connection datasets includes at least one measured environment parameter and a measured SINR. The method further may include generating a regression model based at least in part on the plurality of wireless connection datasets. The regression model is configured to determine an estimated SINR based on the at least one measured environment parameter. The method further may include training an SINR optimization machine learning algorithm to determine an optimized estimated SINR based at least in part on the estimated SINR.