Neural Network Wireless Rate Inference for Environment Control

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Solution Overview

Problem

Current environment control devices face inefficiencies in adapting wireless data transfer rates due to hostile conditions and spectrum competition, with existing methods being either too simple or overly complex to effectively model the interrelated parameters affecting data transfer.

Innovation Solution

A computing device equipped with a neural network inference engine that determines optimal wireless data transfer rates by using a predictive model generated through training, configuring the wireless communication interface to operate at the inferred rate based on parameters such as radio frequency, signal strength, and error rate.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If a simple algorithm is used to control data transfer rate by trying to transfer at the best possible rate and lowering it when unsustainable, then the implementation is straightforward, but the adaptation process is too long and inefficient for hostile conditions and spectrum competition

Engineering Contradiction:

Improveease of implementationVSAvoiddata transfer rate adaptation speed

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent changes the approach from iterative trial-and-error rate adjustment to direct parameter prediction using a neural network. The system predicts optimal data transfer rate parameters based on current channel conditions (signal strength, interference levels, error rates) without requiring repeated attempts at different rates, thus achieving both ease of implementation and fast adaptation.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the mechanical iterative process of testing and lowering data rates with an intelligent prediction system. Instead of physically attempting transfers at different rates and measuring failures, the neural network substitutes this mechanical process by directly computing the optimal rate based on predicted channel conditions, significantly reducing adaptation time.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If a set of rules is defined to select optimal data transfer rate based on current conditions, then the selection process is faster, but the rules are either too simple to properly model interrelated parameters or too complicated to be designed by a human being

Engineering Contradiction:

Improvedata transfer rate adaptation speedVSAvoidcomplexity of rate selection model

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent introduces a neural network as an intermediary between channel condition measurements and data transfer rate selection. This intermediary automatically learns the complex relationships between multiple interrelated parameters (signal strength, interference, error rates, historical performance) and translates them into optimal rate decisions, avoiding both oversimplification and human-designed complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The neural network model is trained offline to automatically capture the complex interactions between channel parameters. During operation, the system self-adjusts the data transfer rate based on real-time conditions without requiring manual rule design or complex real-time computations, making the system both fast and accurately adaptive to changing conditions.

Inventive Principle:

Principle #25Self-service

3Reliability

If the driver lowers the data transfer rate repeatedly until the selected rate can be sustained, then the system ensures reliable transmission, but the process is too long and not efficient for hostile conditions

Engineering Contradiction:

Improvedata transfer reliabilityVSAvoidtime for rate adaptation

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent performs preliminary analysis of channel conditions using the neural network to predict the optimal data transfer rate before actual data transmission begins. By pre-assessing signal strength, interference levels, and error rates through the trained model, the system determines the appropriate rate in advance, ensuring reliable transmission from the start without requiring repeated attempts and failures.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11510097B2Environment control device and method for inferring an optimal wireless data transfer rate using a neural network

Publication Date: 2022.11.22 DISTECH CONTROLS

AI summary

Method and computing device for inferring an optimal wireless data transfer rate using a neural network. The method comprises storing a predictive model generated by a neural network training engine in a memory of a computing device. The method comprises determining, by a processing unit of the computing device, parameters of a data transfer through a wireless communication interface of the computing device. The method comprises executing, by the processing unit, a neural network inference engine using the predictive model for inferring an optimal data transfer rate based on the parameters of the data transfer through the wireless communication interface. The method comprises configuring the wireless communication interface to operate at the optimal data transfer rate. For example, the computing device consists of an environment control device (ECD). The ECD may consist of an environment controller, a sensor, a controlled appliance, and a relay.