Neural Concatenated Channel Coding for Noisy Nonlinear Links
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Solution Overview
Problem
Existing channel codes struggle with adapting to changes in channel models, leading to deteriorated reliability and high complexity in error correction, especially for noisy and nonlinear communication channels.
Innovation Solution
A method and system that combines a Reed-Solomon code with a neural network encoder-decoder pair to create a concatenated channel code, where the neural network is trained to provide robust error correction by optimizing the encoding and decoding processes, reducing complexity and improving error rate performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If practical channel codes are designed for noisy channels, then reliability is improved, but device complexity increases due to careful adaptation of encoder-decoder pairs for different parameters
Solution Approach 1:
The patent applies parameter changes by training the neural network decoder with varying channel parameters (noise levels, fading coefficients) during the training phase. This allows the decoder to adapt to different channel conditions without requiring complex manual reconfiguration, thereby maintaining reliability while reducing adaptation complexity.
Solution Approach 2:
The patent replaces traditional mechanical adaptation mechanisms (manual encoder-decoder pair configuration) with a neural network-based system that automatically adapts through training. The neural network learns optimal decoding strategies for various channel conditions during training, eliminating the need for complex real-time adaptation mechanisms.
2Adaptability or versatility
If code design is automated using deep neural networks, then adaptability to channel model changes is improved, but device complexity increases due to neural network implementation
Solution Approach 1:
The patent segments the error correction system into two parts: a traditional outer code (e.g., Reed-Solomon) that provides structured error correction, and a neural network inner code that handles channel-specific distortions. This segmentation allows the neural network to focus only on adapting to channel variations while the outer code maintains overall system reliability, reducing the adaptation burden on the neural network.
Solution Approach 2:
The patent creates a universal neural network decoder that can handle multiple channel models (AWGN, fading, nonlinear channels) through a single trained model. The neural network is trained on diverse channel conditions during the training phase, enabling it to generalize to unseen channel variations without requiring separate decoders for each channel type.
3Device complexity
If classic channel codes are used, then device complexity is reduced, but reliability deteriorates when channel models change
Solution Approach 1:
The patent introduces dynamics by combining a static outer code with a dynamic neural network decoder. The outer code provides a fixed, low-complexity foundation, while the neural network decoder dynamically adapts its behavior based on the actual channel conditions observed during training. This dynamic component allows the system to maintain high reliability across varying channel models without significantly increasing overall complexity.
Data Source
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AI summary
A method for data transfer via a communication channel (11) is provided, comprising: determining, in a first data processing unit (10), a codeword from a message using a channel code and sending the codeword via the communication channel (11), wherein the channel code comprises an outer code concatenated with an inner code; the outer code is one of a Reed-Solomon code, a folded Reed-Solomon code, a twisted Reed-Solomon code, and a generalized Reed-Solomon code; the inner code is a neural network code comprising a neural encoder-decoder pair, which includes an encoding neural network (21) and a decoding neural network (23); a nonlinear channel and/or a noisy channel (22) is arranged between the encoding neural network (21) and the decoding neural network (23); and the neural encoder-decoder pair has been adapted such that the decoding neural network (23) provides an estimated outer codeword symbol for an input outer codeword symbol by training the neural encoder-decoder pair using a training data set which comprises a plurality of outer codewords determined from a plurality of input messages by an outer code encoder (31), wherein one or more outer codeword symbols of the plurality of outer codewords are used as an input to the encoding neural network (21). Moreover, a further method and systems for data transfer via a communication channel (11) are provided.