Context-Based Decoder Feedback for Error Correction Efficiency
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Solution Overview
Problem
Existing decoding technologies struggle to accurately decode data due to interference and errors caused by unreliable communication channels or storage devices, leading to increased errors and reduced efficiency.
Innovation Solution
Utilizing context information from higher layers, such as application layers, to provide decoding feedback to channel decoders, which includes correcting errors and updating decoding probabilities based on context, such as word frequency, voice characteristics, and object recognition, to improve decoding accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional decoding schemes are used without context information, then the decoding process is simpler and faster, but decoding accuracy decreases due to errors from interference and multipath fading
Solution Approach 1:
The patent implements feedback by using context information from higher layers (application layer) to provide decoding feedback to the channel decoder. This feedback loop allows the decoder to correct errors by comparing decoded output against expected context, thereby improving decoding accuracy while managing complexity through targeted correction rather than complete re-decoding
Solution Approach 2:
The patent applies preliminary action by preparing context information in advance from higher layers before the actual decoding process. This pre-prepared context (such as expected data patterns, application-specific constraints) is then used to guide the decoding process, allowing the decoder to make more accurate decisions without significantly increasing real-time computational complexity
2Measurement precision
If context-based feedback is used to correct decoding errors, then decoding accuracy increases, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by using context information selectively rather than processing all possible corrections. The decoder uses context feedback to target specific error corrections where they are most needed, rather than performing exhaustive error checking on all decoded data, thus reducing processing time while maintaining improved accuracy
Solution Approach 2:
By preparing context information in advance from higher layers, the system reduces real-time processing requirements. The context is pre-processed and made available for quick reference during decoding, allowing error correction to proceed faster than if context were generated on-the-fly during the decoding process
3Reliability
If multiple decoding possibilities are considered with context feedback, then error detection capability improves, but device complexity increases
Solution Approach 1:
The feedback mechanism allows the decoder to evaluate multiple decoding possibilities by comparing each against context information from higher layers. This feedback-driven evaluation process improves error detection capability while managing complexity by using context as a filtering criterion to eliminate unlikely candidates early in the evaluation process
Data Source
AI summary
Methods, systems, and machine-readable mediums utilizing context information to create decoding feedback information to improve decoder accuracy and/or performance. In some examples, the context information is from layers of a network stack above the layers in which the decoders are present. The context information may be or be based upon information about previously received and decoded data and/or information about the sender to provide decoding feedback information to the decoder that is used either to correct a previous decoding error or to inform the decoder on which of a plurality of decoding choices is more likely to be correct. This may increase decoding performance by decreasing errors and in some examples, reducing the complexity of choices by eliminating certain decoding possibilities and thus increasing decoder efficiency.