LDPC Decoder Offset Updating for Low-Complexity Min-Sum Decoding
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Solution Overview
Problem
Existing methods for offset optimization in low-density parity-check (LDPC) codes, such as sum-product decoding, face high computational complexity, while min-sum decoding reduces complexity but at the cost of performance, and finding optimal offsets for each case requires exhaustive effort.
Innovation Solution
An apparatus and method using a gradient descent process with backpropagation to determine optimal offset values for LDPC codes, 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
Solution Approach 1:
The patent modifies the min-sum decoding algorithm by introducing an offset parameter that is optimized through gradient descent. This parameter change allows the simplified min-sum approach to achieve performance closer to sum-product decoding while maintaining lower computational complexity. The offset adjustment transforms the fixed min-sum algorithm into an adaptive version that can compensate for the approximation errors.
2Device complexity
If min-sum decoding is used for LDPC codes, then computational complexity is reduced, but decoding performance deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the decoding performance is continuously monitored and used to update the offset parameter through gradient descent. The performance metric (such as bit error rate) feeds back into the optimization process, allowing the system to adaptively adjust the offset to minimize performance loss while maintaining computational efficiency.
Solution Approach 2:
The system performs self-optimization by automatically adjusting its own parameters through gradient descent without external intervention. The offset value is updated based on the observed decoding performance, allowing the min-sum decoder to self-correct its approximation errors and improve its own performance over time.
3Reliability
If exhaustive search is used to find optimal offset for each case, then decoding performance is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary optimization by using gradient descent to find the optimal offset value before actual decoding operations. This pre-computation approach avoids the need for exhaustive search during runtime, as the offset is determined in advance through efficient gradient-based optimization, significantly reducing time consumption while maintaining performance.
Solution Approach 2:
The patent replaces the mechanical exhaustive search process with a more efficient gradient descent optimization method. Instead of systematically trying all possible offset values, the system uses gradient-based mathematical optimization to directly converge to the optimal offset, substituting a computationally intensive mechanical search with a more elegant mathematical approach.
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, and an output configured to provide a decoded codeword, and an offset value updater including an input connected to the output of the decoder, and an output, connected to the second input of the decoder, configured to provide an updated offset value.