Neural Network FEC Decoding for Short-Block Noisy Channels
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Solution Overview
Problem
Current forward error correction (FEC) systems face challenges in reducing bit error rate (BER) at high code rates with short block lengths, particularly in noisy communication channels, due to high time complexity and latency issues in existing decoding algorithms.
Innovation Solution
The implementation of a deep neural network-based system that processes analog channel outputs by calculating absolute and sign representations, multiplying the sign representation with a parity matrix, and inputting the results into a neural network to estimate binary codewords, while optionally employing permutations and reliability operators to enhance decoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional decoding algorithms (ordered statistics decoders, belief propagation decoders) are used, then decoding accuracy is maintained, but time complexity and latency increase
Solution Approach 1:
The patent replaces conventional iterative decoding algorithms (ordered statistics decoders, belief propagation decoders) with a deep neural network-based decoder. The neural network is trained offline to perform maximum a posteriori (MAP) decoding, substituting the online computational mechanics of iterative algorithms with a pre-trained model that provides faster inference with lower latency while maintaining decoding accuracy.
Solution Approach 2:
The patent performs preliminary training of the deep neural network offline using simulated channel data and known codewords. This preliminary action allows the network to learn optimal decoding patterns during training, so that during actual operation, the decoder can quickly process incoming data without requiring iterative computation, thereby reducing latency while maintaining accuracy.
2Productivity
If deep neural network is used for decoding, then latency and complexity are reduced, but training data and computational resources are required
Solution Approach 1:
The patent uses simulated channel data and synthetic codewords for training the neural network, creating artificial training samples that replicate real-world communication scenarios. This copying approach allows comprehensive training without requiring extensive real-world collected data, reducing the burden of data collection while enabling the network to learn effective decoding patterns.
3Productivity
If short block lengths are used, then bandwidth efficiency is improved, but error correction capability is reduced
Solution Approach 1:
The patent changes the operational parameters of the decoding system by using a deep neural network with specific architecture (number of layers, neurons per layer, activation functions) and training configurations. These parameter changes enable the system to achieve effective error correction with short block lengths, as the neural network can learn complex decision boundaries that traditional algorithms cannot exploit with limited data, thereby maintaining reliability while improving bandwidth efficiency.
Data Source
AI summary
A system for reducing analog noise in a noisy channel, comprising: an interface configured to receive analog channel output comprising a stream of noisy binary codewords of a linear code; and a computation component configured to perform the following: for each analog segment of the analog channel output of block length: calculating an absolute value representation and a sign representation of a respective analog segment, calculating a multiplication of a binary representation of the sign representation with a parity matrix of the linear code, inputting the absolute value representation and the outcome of the multiplication into a neural network for acquiring a neural network output, and estimating a binary codeword by component-wise multiplication of the neural network output and the sign representation.