Cyclically Equivariant Neural Decoders for Cyclic Codes
Authors: Xiangyu Chen, Min Ye
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive simulations with BCH codes and punctured Reed-Muller (RM) codes show that our new decoder consistently outperforms previous neural decoders when decoding cyclic codes. |
| Researcher Affiliation | Academia | 1Data Science and Information Technology Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Shenzhen, China. |
| Pseudocode | No | The paper describes the decoding algorithms and procedures using mathematical equations and step-by-step descriptions in paragraph text (e.g., in Section 4, 'Step 1: Prepend a dummy symbol L0 = 0 to the LLR vector.'), but it does not contain structured pseudocode or an explicitly labeled algorithm block. |
| Open Source Code | Yes | Code available at github.com/cyclicallyneuraldecoder |
| Open Datasets | No | No concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset used for training was provided. The paper discusses using 'BCH codes and punctured RM codes' and mentions training using 'the all-zero codeword', implying data is generated based on these code structures rather than loaded from a named external dataset. |
| Dataset Splits | No | The paper mentions 'For all codes, we train with a batch size of 160 samples...' and 'We use 105 samples for testing.' but does not explicitly provide details about a validation dataset split or its size. |
| Hardware Specification | No | No specific hardware details such as CPU/GPU models, processor types, or memory amounts used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as 'Python 3.8' or 'PyTorch 1.9'. |
| Experiment Setup | Yes | In our simulations, the number of BP iterations is 5 for all the methods. For all codes, we train with a batch size of 160 samples, among which we produce 20 samples from each of the following 8 SNR values: 1d B, 2d B, . . . , 8d B. |