Neural Decoder Iteration Control for Early CRC Prediction
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Solution Overview
Problem
In digital communication systems, the decoder in User Equipment (UE) faces high computational complexity and power consumption due to the need for a fixed number of iterations to determine CRC success or failure, leading to inefficient termination of decoding processes.
Innovation Solution
An electronic device uses a Neural Network (NN) model to predict CRC success, failure, or uncertainty based on signal parameters, allowing early termination of iterations and reducing power consumption by determining the number of iterations required for successful decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed number of iterations is used for decoding, then the decoding process is simplified and easier to implement, but power consumption increases and throughput decreases
Solution Approach 1:
The patent applies dynamics by making the number of decoding iterations variable rather than fixed. The iteration counter is dynamically adjusted based on channel conditions, signal quality metrics, and decoding performance, allowing the system to perform fewer iterations when conditions are good (reducing power consumption) and more iterations when conditions are poor (maintaining decoding reliability).
Solution Approach 2:
The patent changes the parameter of iteration count from a static fixed value to a dynamic parameter that adapts based on multiple factors including channel quality indicators, signal-to-noise ratio, and decoding convergence metrics. This parameter change enables the system to optimize the balance between decoding accuracy and power consumption.
2Ease of operation
If a fixed number of iterations is used for decoding, then the implementation is simpler, but throughput and latency are reduced
Solution Approach 1:
The dynamic iteration adjustment mechanism allows the decoding process to adapt its duration based on real-time conditions, enabling faster throughput when channel conditions permit fewer iterations while maintaining reliability when more iterations are needed.
Solution Approach 2:
The patent performs preliminary assessment of channel conditions and signal quality before initiating the full decoding process. By evaluating metrics such as channel quality indicators and signal-to-noise ratio in advance, the system can pre-determine an appropriate iteration count, avoiding unnecessary iterations and thus improving throughput and reducing latency.
3Reliability
If more iterations are performed for accurate CRC determination, then decoding reliability improves, but power consumption and processing time increase
Solution Approach 1:
The patent incorporates feedback mechanisms where the decoder monitors intermediate results, convergence metrics, and error patterns during the iteration process. Based on this feedback, the system can determine when sufficient accuracy has been achieved and terminate iterations early, or continue iterations when accuracy is insufficient, thus optimizing the balance between reliability and power consumption.
Solution Approach 2:
The system performs preliminary evaluation of channel conditions and decoding progress to predict the number of iterations needed for reliable CRC determination. This preliminary action prevents both over-iteration (wasting power) and under-iteration (compromising reliability).
Data Source
AI summary
Embodiments herein provide a method for predicting iterations for decoding an encoded data at an electronic device. The method includes: receiving, by the electronic device, the encoded data; detecting, by the electronic device, signal parameters associated with the encoded data; predicting, by the electronic device, one of a cyclic redundancy check (CRC) failure, CRC success, and a CRC uncertainty in iterations for decoding the encoded data based on the signal parameters using a Neural Network (NN) model.