LDPC Decoder Offset Updating for Low-Complexity Performance Tuning
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Solution Overview
Problem
The existing methods for offset optimization in low-density parity-check (LDPC) codes, such as sum-product decoding and minimum-sum decoding, face challenges in balancing computational complexity and performance, with offset min-sum decoding requiring exhaustive efforts to find optimal offsets for varying code parameters and channel conditions.
Innovation Solution
An apparatus and method that utilize a gradient descent process with backpropagation to determine optimal offset values, enabling low complexity gradient calculations and adapting to different transmission environments through online learning and expanding the search space for offset values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sum-product decoding is used for LDPC codes, then decoding performance is improved, but computational complexity increases significantly
Solution Approach 1:
The patent modifies the min-sum decoding algorithm by introducing an offset parameter that is added to the minimum value during the check node update process. This parameter adjustment allows the simplified min-sum algorithm to achieve performance closer to the optimal sum-product decoding while maintaining lower computational complexity. The offset compensates for the approximation error introduced by the min-sum simplification.
2Reliability
If offset min-sum decoding is used to approach sum-product performance, then decoding performance is improved, but finding optimal offset values requires exhaustive effort
Solution Approach 1:
The patent pre-calculates and stores optimal offset values in a lookup table before actual decoding operations. These offset values are determined offline for various channel conditions and code parameters, allowing the decoder to quickly retrieve the appropriate offset without performing exhaustive search during real-time operation. This preliminary preparation eliminates the time-consuming optimization process from the critical decoding path.
Solution Approach 2:
The patent implements a feedback mechanism where the decoder monitors decoding performance and adjusts the offset value dynamically based on observed errors and channel conditions. This adaptive feedback allows the system to automatically converge to optimal offset values during operation, eliminating the need for exhaustive manual optimization while maintaining high decoding performance.
3Device complexity
If fixed offset values are used in min-sum decoding, then device complexity is reduced, but adaptability to different transmission environments deteriorates
Solution Approach 1:
The patent transitions from fixed offset values to dynamic offset values that adapt to changing channel conditions. The offset is selected from a pre-computed table based on detected channel parameters such as signal-to-noise ratio or error patterns, allowing the decoder to optimize its performance for current transmission conditions while maintaining the computational simplicity of min-sum decoding. This dynamic selection provides adaptability without significantly increasing complexity.
Data Source
AI summary
An apparatus and method are provided. The apparatus includes a decoder, including a first input configured to receive transport blocks, a second input, a third input, a fourth input, and an output configured to provide a decoded codeword, and an offset value updater, including an input connected to the output of the decoder, a first output connected to the third input of the decoder configured to provide an updated offset value, and a second output connected to the fourth input of the decoder configured to provide an index for a next codeword to be decoded.