Convolutional Encoding with Sparse Generator Polynomials for Short Packets
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Solution Overview
Problem
Current channel encoding methods for short packets in wireless communication networks are complex, and there is an urgent need to reduce the complexity of convolutional encoding and decoding to meet the demands of ultra-low latency and ultra-high reliability in applications such as intelligent transportation and industrial control.
Innovation Solution
A method and apparatus that utilize T component code generator polynomials for convolutional encoding and decoding, reducing complexity by minimizing the number of non-zero coefficients and XOR gates in the encoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional channel encoding methods are used for short packets, then encoding and decoding can be performed, but the complexity of convolutional encoding and decoding is high
Solution Approach 1:
The patent divides the channel encoding process into multiple parallel component encoders (first component encoder, second component encoder, etc.), each handling a portion of the encoding task. This segmentation allows the system to process data through multiple simplified paths rather than a single complex encoding path, reducing the computational complexity of each individual encoder while maintaining overall encoding effectiveness.
Solution Approach 2:
The patent changes the parameters of the component code generator polynomials by carefully selecting polynomials with specific properties (small number of non-zero coefficients, appropriate constraint lengths). This parameter optimization reduces the number of XOR gates required in each component encoder, directly lowering the encoding complexity while preserving the error correction capability needed for reliable transmission.
2Device complexity
If the number of non-zero coefficients in component code generator polynomials is reduced, then the complexity of convolutional encoding is reduced, but the robustness of data communication must be maintained
Solution Approach 1:
The patent optimizes the parameters of component code generator polynomials by selecting specific polynomials that have a small number of non-zero coefficients. This parameter selection reduces the number of XOR operations required during encoding, simplifying the hardware implementation. Simultaneously, the patent carefully chooses constraint lengths and polynomial combinations that maintain adequate error correction capability, ensuring robustness is preserved despite the simplified structure.
Solution Approach 2:
The patent combines multiple component codes with different generator polynomials and constraint lengths to form a composite encoding scheme. This composite approach allows the system to leverage the strengths of different polynomial configurations, achieving both low complexity (through sparse polynomials) and high robustness (through diverse code combinations that provide strong error correction).
Data Source
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AI summary
Provided in embodiments of the present application are an encoding/decoding method, a communication apparatus, and a storage medium, which relate to the technical field of communications and are used for reducing the complexity of convolutional encoding. The encoding method specifically comprises: firstly acquiring an information bit sequence; then generating a polynomial according to T component codes to perform convolutional encoding on the information bit sequence to obtain an encoded bit sequence, T being an integer greater than 1; and sending all or some of the bits in the encoded bit sequence.