LDPC Layered Scheduling for Faster 5G Data Decoding
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Solution Overview
Problem
In 5G communication systems, the decoding of low-density parity-check (LDPC) codes is complex and inefficient, leading to suboptimal performance due to noise, fading, and inter-symbol interference, which hampers high-speed digital communication and broadcasting requirements.
Innovation Solution
A method and apparatus for efficiently decoding LDPC codes using layered scheduling, which involves a receiving device that applies suitable decoding scheduling based on the structural or algebraic characteristics of the LDPC code, including identifying a base matrix, lifting size, and determining the order for decoding to improve decoding performance without increasing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional LDPC decoding methods are used, then the decoding process can be implemented, but the decoding complexity is high and convergence is slow
Solution Approach 1:
The parity check matrix is divided into multiple sub-matrices, and the decoding process is segmented into multiple stages. Each stage processes a specific sub-matrix, allowing parallel computation and reducing overall complexity while improving convergence speed through staged processing.
Solution Approach 2:
The decoding method dynamically adjusts the processing order and focus based on the structure of the parity check matrix. By identifying and prioritizing critical sub-matrices that contribute most to error correction, the system dynamically optimizes the decoding path to achieve faster convergence without exhaustive processing of all elements.
2Reliability
If standard decoding scheduling is applied, then the decoding process is straightforward, but error-correcting performance is suboptimal in noisy environments
Solution Approach 1:
Different decoding strategies are applied to different sub-matrices based on their local characteristics. Critical sub-matrices that have greater impact on error correction receive more intensive processing and attention, while less critical sub-matrices use standard processing, optimizing overall reliability without uniform complexity increase.
Solution Approach 2:
The method performs preliminary analysis of the parity check matrix structure to identify critical sub-matrices and potential error patterns before full decoding begins. This preliminary action allows the system to prepare optimized processing paths and focus computational resources on the most impactful areas, improving error correction efficiency.
Data Source
AI summary
The disclosure relates to a communication technique for converging a 5G communication system for supporting a higher data transfer rate beyond a 4G system with an IoT technology, and a system therefor. The disclosure may be applied to intelligent services (for example, smart home, smart buildings, smart cities, smart cars or connected cars, health care, digital educations, retail business, security and safety-related services, etc.) based on a 5G communication technology and an IoT-related technology. The disclosure provides an apparatus and a method for efficiently decoding a low-density parity-check (LDPC) code in a communication or broadcasting system. Further, the disclosure provides an LDPC decoding device and method for improving decoding performance without increasing the decoding complexity by applying suitable decoding scheduling according to the structural or algebraic characteristics of the LDPC code in a process of decoding the LDPC code using layered scheduling or a scheme similar thereto. Further, a method of a low density parity check (LDPC) decoding performed by a receiving device in a wireless communication system is provided, the method comprising: receiving, from a transmitting device, a signal corresponding to input bits; performing demodulation based on the signal to determine values corresponding to the input bits; identifying a number of the input bits based on the signal; identifying a base matrix and a lifting size based on the number of the input bits; identifying a parity check matrix based on the base matrix; identifying an index corresponding to the values; determining a number of layers based on the index and the lifting size; determining an order for LDPC decoding based on the number of layers and a predetermined sequence; and performing LDPC decoding to determine the input bits based on the values, the parity check matrix and the order.