Location-Based Error Correction Code Selection for Low-Delay Reliability
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Solution Overview
Problem
Existing communication systems apply a one-size-fits-all approach to error correction, leading to performance degradation and unnecessary overhead due to dynamic changes in user circumstances such as location, network load, and environmental factors.
Innovation Solution
A system that dynamically selects error correction codes and transcoding based on user-specific factors like location, device type, network conditions, and environmental data using machine learning models to optimize communication quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a higher percentage of error correction is applied to improve system reliability, then data reliability is improved, but delay and overhead increase due to redundant bits
Solution Approach 1:
The patent implements dynamic error correction code selection that adapts to changing network conditions, user location, and environmental factors. The system transitions from static one-size-fits-all error correction to dynamic adaptation, selecting appropriate error correction levels based on real-time conditions, thereby optimizing the balance between reliability and delay.
Solution Approach 2:
The patent applies different error correction strategies to different users and network conditions rather than uniformly to all traffic. By analyzing user-specific factors including location, device type, and environmental data, the system tailors error correction application locally to where it is most needed, avoiding unnecessary overhead in good conditions while providing enhanced protection where required.
2Reliability
If error correction is applied uniformly to all users, then system reliability is maintained, but overhead and delay increase unnecessarily for users in good conditions
Solution Approach 1:
The patent changes the parameters of error correction application based on multiple factors including user location, device type, network conditions, and environmental data. By dynamically adjusting error correction parameters rather than maintaining fixed uniform application, the system reduces overhead and energy consumption while preserving reliability through context-aware adaptation.
Solution Approach 2:
The system applies error correction locally and selectively based on user-specific conditions rather than uniformly across all communications. This targeted approach ensures reliability is maintained where needed while minimizing unnecessary overhead and energy consumption in conditions where error correction is less critical.
3Device complexity
If the same link level optimization is applied regardless of network load, then implementation simplicity is maintained, but performance degradation occurs under varying network conditions
Solution Approach 1:
The patent implements dynamic link level optimization that adapts to varying network load and conditions. The system monitors factors such as network congestion, user location, and environmental data to dynamically adjust optimization parameters, transitioning from static uniform optimization to dynamic condition-based optimization, thereby improving communication efficiency without excessive complexity.
Data Source
AI summary
A computer-implemented method includes receiving a plurality of data associated with a device, wherein the plurality of data includes a location data associated with the device; applying the plurality of data as an input to a trained Machine Learning (ML) model to determine an error correcting code to be used for the device based on an output of the selected ML model; and sending the error correcting code to the device.