Location-Based Error Correction Code Selection for Low-Delay Reliability
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Solution Overview
Problem
Existing communication systems employ a one-size-fits-all approach to error correction, which can lead to performance degradation due to unnecessary overhead and delay, as they fail to dynamically adapt error correction codes based on user-specific factors such as location and network conditions.
Innovation Solution
The use of Artificial Intelligence (AI)/Machine Learning (ML) inferences to dynamically select an appropriate error correcting code and/or percentage of error correction for each user based on real-time factors like location, device type, network load, and environmental conditions.
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 system dynamically adjusts the error correction percentage based on real-time network conditions and user location. The correction level is not fixed but adapts continuously, increasing when conditions are poor and decreasing when conditions are good, thus resolving the contradiction between reliability and delay
Solution Approach 2:
The system changes the error correction parameter (percentage of redundant bits) based on varying operating conditions. By monitoring network quality, location, and device characteristics, the system optimizes the correction parameter to achieve appropriate reliability without excessive overhead
2Reliability
If a higher percentage of error correction is applied to improve system reliability, then data reliability is improved, but overhead increases due to redundant bits
Solution Approach 1:
The error correction level is made dynamic rather than static. The system continuously monitors network conditions and adjusts the correction percentage accordingly, applying high correction only when necessary and reducing it when conditions are favorable, thus minimizing overhead while maintaining reliability
Solution Approach 2:
The system optimizes the error correction parameter by changing it based on actual operating conditions. This parameter adaptation allows the system to achieve the minimum necessary reliability without introducing excessive redundant bits that would increase overhead
3Device complexity
If the same link level optimization is applied regardless of network load and congestion, then system simplicity is maintained, but communication quality degrades under varying conditions
Solution Approach 1:
The link level optimization parameters are made dynamic and adaptive to network conditions. The system automatically adjusts modulation schemes, coding rates, and power levels based on real-time measurements of network load and congestion, thereby maintaining high communication quality without requiring complex manual configuration
Solution Approach 2:
The system performs self-optimization by automatically adjusting link level parameters based on monitored network conditions. This self-service capability allows the system to adapt to varying load and congestion without external intervention, maintaining communication quality while avoiding the complexity of manual optimization
4Device complexity
If a one-size-fits-all error correction approach is used, then device complexity is reduced, but user experience degrades due to unnecessary overhead and delay
Solution Approach 1:
The system applies different error correction strategies tailored to each user's local conditions. By considering individual factors such as user location, device type, and specific network conditions, the system optimizes error correction for each user independently, thereby improving user experience without significantly increasing overall system complexity
Solution Approach 2:
The error correction parameters are customized for each user based on their specific circumstances. The system adjusts correction levels, codes, and rates individually for different users and devices, providing optimized performance for each user while managing complexity through automated parameter selection
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.