QC-LDPC Matrix Extension for Lower-Rate Error Correction
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Solution Overview
Problem
Existing error correction methods in wireless communication, such as QC-LDPC codes, face challenges in achieving lower code rates without increasing decoding complexity and latency, which is crucial for reliable and low-latency communications in 5G networks, especially for ultra-reliable and low-latency communications (URLLC) applications.
Innovation Solution
The implementation of a weight-2 row extension method for the base matrix in QC-LDPC codes, which allows for a decrease in code rate without increasing decoding complexity and latency, by adding weight-2 rows to the base matrix, thereby generating a second codeword with additional parity bits for error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the code rate is decreased to improve error correction capability, then reliability is improved, but decoding complexity increases
Solution Approach 1:
The base matrix is segmented into multiple submatrices, each corresponding to a specific code rate. The decoder can select and process only the relevant submatrix based on the received code rate, rather than processing the entire extended matrix. This segmentation allows the system to achieve multiple code rates while maintaining manageable decoding complexity for each rate.
Solution Approach 2:
The system dynamically adapts the base matrix structure by including rate-matching indicators that specify which submatrices should be used for different code rates. This dynamic configuration allows the decoder to adjust its processing scope based on the required code rate, optimizing the balance between error correction capability and decoding complexity for each specific transmission scenario.
2Reliability
If the code rate is decreased to improve error correction capability, then reliability is improved, but latency increases
Solution Approach 1:
By segmenting the base matrix into rate-specific submatrices, the decoder can quickly identify and process only the necessary subset of parity-check equations corresponding to the received code rate. This avoids the need to process the entire extended matrix, thereby reducing decoding latency while maintaining the error correction capability appropriate for the selected code rate.
Solution Approach 2:
The base matrix is pre-structured with rate-matching indicators and organized submatrices during the encoding phase. This preliminary organization allows the decoder to immediately locate and process the relevant submatrix without requiring complex runtime analysis or reconfiguration, thus minimizing decoding latency while achieving the desired code rate and error correction performance.
3Reliability
If additional parity bits are added to decrease code rate, then reliability is improved, but the structure complexity increases
Solution Approach 1:
The base matrix is divided into multiple submatrices, where each submatrix corresponds to a specific code rate and contains a specific set of parity bits. This segmentation allows the system to add parity bits in a structured, modular fashion rather than creating a monolithic complex matrix. Each submatrix maintains a manageable structure while collectively providing the full range of error correction capabilities across different code rates.
Solution Approach 2:
Instead of including all possible parity bits and submatrices in every transmission, the system uses rate-matching indicators to activate only the necessary subset of submatrices and parity bits for the specific code rate being used. This partial action approach maintains the structural flexibility to support multiple code rates while keeping the actual processing complexity proportional to the selected code rate requirements.
Data Source
AI summary
Aspects of the disclosure provide an apparatus and a method for error correction based on a matrix. The apparatus includes memory and processing circuitry. The memory is configured to store the matrix associated with a set of parity bits. The matrix having rows and columns includes elements having values corresponding to either a first state or a second state. The matrix also includes a row having two elements with values corresponding to the first state. One of the two elements is a parity element corresponding to a parity bit associated with the row. Further, other elements in a same column as the parity element have values corresponding to the second state. The processing circuitry is configured to implement error correction based on the matrix. In another embodiment, the processing circuitry is configured to encode a data unit by generating the set of parity bits from the data unit based on the matrix and to form a codeword that includes the data unit and the set of parity bits. The processing circuitry is also configured to decode a received codeword having a received data unit based on the matrix and to obtain a decoded data unit.