Polar Autoencoder Coding With Nonlinear NN Outer Encoders
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Solution Overview
Problem
Existing deep learning-based channel coding schemes for polar codes fail to adequately capture nonlinearities in channel environments, leading to suboptimal performance in complex decoding strategies like successive cancellation list decoding.
Innovation Solution
A generalized concatenated polar autoencoder (AE) that integrates non-linear neural network (NN) components for encoding and decoding, utilizing non-linear NN outer encoders and decoders with specialized loss functions to optimize performance across various information/frozen set patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If linear or rigid deep learning structures are used for polar decoding, then the implementation is simpler, but the performance in capturing nonlinearities of channel environments deteriorates
Solution Approach 1:
The patent applies dynamics by transforming the static, rigid neural network structure into a dynamic, adaptive one. The neural network is trained to learn and adapt to the nonlinear characteristics of specific channel environments, allowing the decoder to dynamically adjust its decoding behavior based on the learned channel properties rather than using a fixed linear structure.
Solution Approach 2:
The patent changes the parameters of the neural network (weights, biases, architecture configurations) through training on channel-specific data. This allows the network to capture nonlinear channel characteristics by optimizing its internal parameters to match the statistical properties and nonlinearities of the target channel environment.
2Device complexity
If traditional polar decoding methods are used, then the computational complexity is lower, but the performance under complex decoding strategies like successive cancellation list decoding deteriorates
Solution Approach 1:
The patent substitutes the traditional mechanical/polynomial-based polar decoding operations with a neural network-based system. Instead of using fixed algebraic operations and lookup tables, the system employs a trained neural network that learns the decoding transformations, enabling more flexible and accurate handling of complex decoding strategies like SCL decoding.
Solution Approach 2:
The neural network decoder is designed to be self-adaptive, automatically learning the optimal decoding strategy for the given channel conditions without requiring manual tuning or complex pre-computation of decoding tables. The network serves itself by learning from training data the appropriate decoding transformations for various channel scenarios.
3Extent of automation
If deep learning-based channel autoencoders are used, then the automation of code design is achieved, but the generalization of concatenated coding concepts and integration of non-linear learnable components deteriorates
Solution Approach 1:
The patent segments the polar coding system into distinct neural network components: an outer encoder neural network, a polarization kernel neural network, and an outer decoder neural network. Each component is independently trained and optimized, allowing the system to generalize concatenated coding concepts by treating each segment as a learnable module rather than a fixed operation.
Solution Approach 2:
The patent creates a composite deep learning architecture by combining multiple neural network components (outer encoder, polarization kernel, outer decoder) into a unified polar autoencoder system. This composite structure integrates non-linear learnable components at multiple stages of the encoding-decoding process, enabling the system to capture complex channel characteristics that single-component models cannot handle.
Data Source
AI summary
Methods and devices are provided in which a processor of an electronic device encodes segments of a binary message word into real-valued outer codewords using corresponding non-linear neural network (NN) outer encoding processes. The processor combines the real-valued outer codewords using a real-field polarization operation to generate a codeword for the binary message word.