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.
Optimizing Puncturing Patterns of 5G NR LDPC Codes for Few-Iteration Decoding
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abstract
Rate-matching of low-density parity-check (LDPC) codes enables a single code description to support a wide range of code lengths and rates. In 5G NR, rate matching is accomplished by extending (lifting) a base code to a desired target length and by puncturing (not transmitting) certain code bits. LDPC codes and rate matching are typically designed for the asymptotic performance limit with an ideal decoder. Practical LDPC decoders, however, carry out tens or fewer message-passing decoding iterations to achieve the target throughput and latency of modern wireless systems. We show that one can optimize LDPC code puncturing patterns for such few-iteration-constrained decoders using a method we call swapping of punctured and transmitted blocks (SPAT). Our simulation results show that SPAT yields from 0.20 dB up to 0.55 dB improved signal-to-noise ratio performance compared to the standard 5G NR LDPC code puncturing pattern for a wide range of code lengths and rates.
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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.