Fast Channel Simulation via Error-Correcting Codes
Authors: Sharang Sriramu, Rochelle Barsz, Elizabeth Polito, Aaron Wagner
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3.4 Experimental Results We run Polar Sim on the reverse (PY |X) version of three channels: (1) the BSC with a uniform input (2) the binary erasure channel, X = Z Y , where Y is uniform over { 1, 1} and Z is Bernoulli(ϵ), and (3) the binary Gaussian channel X = Y + Z, where Y is again uniform over { 1, 1} and Z is N(0, σ2). Note that the reverse of the BSC with a uniform input is the BSC itself. Fig. 3 and Fig. 4 show the rate performance of these simulations. |
| Researcher Affiliation | Academia | Sharang M. Sriramu School of ECE Cornell University Ithaca, NY 14853 EMAIL Rochelle Barsz School of ECE Cornell University Ithaca, NY 14853 EMAIL Elizabeth Polito School of ECE Cornell University Ithaca, NY 14853 EMAIL Aaron B. Wagner School of ECE Cornell University Ithaca, NY 14853 EMAIL |
| Pseudocode | Yes | Algorithms 1 and 2 describe the complete scheme. |
| Open Source Code | Yes | We include code as part of our supplementary material with instructions on how to run it. |
| Open Datasets | No | The paper uses synthetically generated data (e.g., 'BSC with a uniform input', 'binary erasure channel', 'binary Gaussian channel') rather than pre-existing public datasets, so no external access information is provided or needed. |
| Dataset Splits | No | The paper does not specify training, validation, or test splits, as it primarily uses simulated data based on channel characteristics rather than splitting a fixed dataset. |
| Hardware Specification | No | It is noted in the paper that all experiments were run on a consumer-grade CPU. |
| Software Dependencies | No | The paper mentions using 'Dr. Henry Pfister s polar code implementation' but does not provide specific version numbers for general software dependencies (e.g., Python, PyTorch, etc.) required for replication. |
| Experiment Setup | Yes | We run Polar Sim on the reverse (PY |X) version of three channels: (1) the BSC with a uniform input (2) the binary erasure channel, X = Z Y , where Y is uniform over { 1, 1} and Z is Bernoulli(ϵ), and (3) the binary Gaussian channel X = Y + Z, where Y is again uniform over { 1, 1} and Z is N(0, σ2). ... We implement Algorithm 3 from Flamich [2024]. The proposal distribution P is chosen to be i.i.d. Bernoulli(1/2) with n = 8. Given the input Xn = xn, the target distribution Q(yn) is chosen to be Qn i=1 BSCp(yi|xi), where p ranges over (0, 1/2). ... One of the virtues of the Polar Sim is that there are no hyperparameters to choose. |