Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
Authors: Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Turbo AE approaches state-of-the-art performance under canonical channels; (b) moreover, Turbo AE outperforms the state-of-the-art codes under non-canonical settings in terms of reliability. Turbo AE shows that the development of channel coding design can be automated via deep learning, with near-optimal performance. |
| Researcher Affiliation | Collaboration | Yihan Jiang ECE Department University of Washington Seattle, United States yij021@uw.edu Hyeji Kim Samsung AI Center Cambridge Cambridge, United Kingdom hkim1505@gmail.com Himanshu Asnani School of Technology and Computer Science Tata Institute of Fundamental Research Mumbai, India himanshu.asnani@tifr.res.in Sreeram Kannan ECE Department University of Washington Seattle, United States ksreeram@ee.washington.edu Sewoong Oh Allen School of Computer Science & Engineering University of Washington Seattle, United States sewoong@cs.washington.edu Pramod Viswanath ECE Department University of Illinois at Urbana Champaign Illinois, United States pramodv@illinois.edu |
| Pseudocode | Yes | Algorithm 1 Training Algorithm for Turbo AE |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper describes generating data based on channel models (AWGN, ATN, Markovian-AWGN) and refers to the Vienna 5G simulator [33][34] for benchmarks. It does not provide a specific public dataset with a URL, DOI, or a citation to an existing public data repository. |
| Dataset Splits | No | The paper does not provide specific percentages or sample counts for training, validation, or test dataset splits, as data is generated based on channel models rather than using a fixed dataset with pre-defined splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions 'Vienna 5G simulator [33] [34]' and 'Adam' optimizer but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The hyper-parameters are shown in Table 1. Loss Binary Cross-Entropy (BCE) Encoder 2 layers 1D-CNN, kernel size 5, 100 filters for each fi,θ(.) block Decoder 5 layers 1D-CNN, kernel size 5, 100 filters for each gφi,j(.) block Decoder Iterations 6 Info Feature Size F 5 Batch Size 500 when start, double when saturates for 20 epochs, till reaches 2000 Optimizer Adam with initial learning rate 0.0001 Training Schedule for Each Epoch Train encoder Tenc = 100 times, then train decoder Tdec = 500 times Block Length K 100 Number of Epochs M 800 |