A linear-attention transformer decoder achieves bit error rate comparable to a standard transformer and better than one-iteration belief propagation on 5G NR LDPC codes, with O(n) instead of O(n^2) complexity.
Learning to Decode Linear Codes Using Deep Learning
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abstract
A novel deep learning method for improving the belief propagation algorithm is proposed. The method generalizes the standard belief propagation algorithm by assigning weights to the edges of the Tanner graph. These edges are then trained using deep learning techniques. A well-known property of the belief propagation algorithm is the independence of the performance on the transmitted codeword. A crucial property of our new method is that our decoder preserved this property. Furthermore, this property allows us to learn only a single codeword instead of exponential number of code-words. Improvements over the belief propagation algorithm are demonstrated for various high density parity check codes.
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5G LDPC Linear Transformer for Channel Decoding
A linear-attention transformer decoder achieves bit error rate comparable to a standard transformer and better than one-iteration belief propagation on 5G NR LDPC codes, with O(n) instead of O(n^2) complexity.