Wireless Device Rate Adaptation via Interference Prediction

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Solution Overview

Problem

Conventional techniques for managing wireless device activity in wireless environments are limited in addressing interference between devices, leading to reduced data throughput and increased power consumption due to collisions and inefficient bandwidth usage.

Innovation Solution

Implementing dynamic data transmission adaptation operations that use machine learning models to predict interference patterns, allowing for adaptive configuration of wireless communication channels without reducing data rates, by identifying and compensating for interference between access points.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional techniques are used to manage wireless device activity, then device simplicity is maintained, but data throughput is reduced and power consumption increases due to interference and collisions

Engineering Contradiction:

Improvedata throughputVSAvoidtransmission adaptation complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by predicting interference patterns before they occur and proactively adjusting transmission parameters in advance. The machine learning model analyzes historical data and predicts future interference events, allowing the system to preemptively modify transmission schedules and parameters to avoid collisions, thereby maintaining high data throughput without requiring complex real-time reactions during interference events.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring wireless channel conditions, interference patterns, and transmission outcomes. This feedback is fed into the machine learning model to refine predictions and optimize transmission adaptation decisions. The feedback loop enables the system to learn from past performance and improve its transmission strategy over time, resolving the contradiction between maintaining simplicity and achieving high productivity through adaptive control.

Inventive Principle:

Principle #23Feedback

2Productivity

If dynamic transmission adaptation is implemented to compensate for interference, then data throughput is enhanced, but power consumption increases due to continuous monitoring and processing

Engineering Contradiction:

Improvedata throughputVSAvoidpower consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system reduces power consumption by performing interference prediction and transmission scheduling in advance during low-activity periods, rather than continuously monitoring and reacting in real-time. The machine learning model processes historical interference data during off-peak moments to build prediction models, allowing the system to operate in a more energy-efficient manner while still achieving high data throughput during active transmission periods through preemptive adaptation decisions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system employs periodic action by conducting interference pattern analysis and model training at scheduled intervals rather than continuously. The machine learning model is updated periodically with new interference data, and transmission adaptation is applied in periodic cycles. This approach maintains effective interference compensation while significantly reducing the continuous processing load and associated power consumption compared to real-time adaptive systems.

Inventive Principle:

Principle #19Periodic action

3Measurement precision

If machine learning models are used to predict interference patterns, then interference compensation accuracy is improved, but device complexity and processing requirements increase

Engineering Contradiction:

Improveinterference prediction accuracyVSAvoidprocessing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system manages processing complexity by dynamically adjusting the level of interference analysis and model complexity based on current channel conditions and device state. When interference is detected or predicted, the system activates more sophisticated machine learning-based prediction and adaptation mechanisms. When the channel is stable and interference is minimal, the system uses simpler, lower-complexity transmission strategies. This parameter-based activation approach maintains high prediction accuracy when needed while minimizing processing complexity during normal operation.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20240276532A1Systems, methods, and devices for rate adaptation for data throughput enhancement in wireless devices

Publication Date: 2024.08.15 INFINEON TECHNOLOGIES AMERICAS CORP

AI summary

Systems, methods, and devices enhance data throughput in wireless devices. Methods may include determining, using a processing device comprising processing elements, a plurality of wireless parameters representing wireless data features on a wireless communications channel, and determining, using the processing device, a plurality of interference parameters based, at least in part, on the plurality of wireless parameters, the plurality of interference parameters identifying interference events on the wireless communications channel. The methods may also include generating one or more data transmission pattern modifications based, at least in part, on the plurality of interference parameters.