Polar Encoding with Polarization Weights for Low-Complexity Bit Selection
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Solution Overview
Problem
Existing polar code encoding methods face high calculation and storage complexity due to reliance on channel parameters and code rates for determining information bit positions, leading to inefficient reliability estimation and excessive storage overheads.
Innovation Solution
The proposed method calculates polarization weights of polarized channels to determine information bit positions independently of channel parameters and code rates, reducing complexity through online or offline storage methods, and adjusts parameters φ and α for optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DE method or GA method is used to estimate polarized channel reliability, then measurement precision is improved, but device complexity increases due to high calculation complexity
Solution Approach 1:
The patent extracts the essential reliability estimation function from complex DE/GA methods by using a simplified formula that depends only on code length and code rate. This extraction maintains adequate measurement precision while eliminating the high calculation complexity of the original methods, making the solution suitable for online calculation.
Solution Approach 2:
Instead of using complex iterative methods (DE/GA) to estimate reliability, the patent inverts the approach by using a direct analytical formula that computes reliability metrics through simple mathematical operations. This inversion transforms an computationally intensive problem into an efficient closed-form solution.
2Productivity
If offline storage method is used to store information bit position sequences, then productivity is improved, but loss of information increases due to excessive storage overheads
Solution Approach 1:
The patent changes the parameters used for storing information bit positions from comprehensive channel reliability data (requiring large storage) to simplified indices based on code length and code rate. This parameter transformation dramatically reduces storage overhead while maintaining the ability to quickly retrieve and apply the correct bit positions through online calculation.
Data Source
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AI summary
This application provides an encoding method and device, and an apparatus. The method includes: determining N to-be-encoded bits, where the N to-be-encoded bits include information bits and frozen bits; obtaining a first polarization weight vector including polarization weights of N polarized channels, where the N to-be-encoded bits are corresponding to the N polarized channels; determining positions of the information bits based on the first polarization weight vector; and performing polar encoding on the N to-be-encoded bits to obtain polar-encoded bits.