An improved analysis of least squares superposition codes with Bernoulli dictionary
DOI10.1007/s42081-019-00057-9zbMath1455.94089arXiv1801.02930OpenAlexW2973363442MaRDI QIDQ2303504
Yoshinari Takeishi, Jun'ichi Takeuchi
Publication date: 4 March 2020
Published in: Japanese Journal of Statistics and Data Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1801.02930
Gaussian channelEuler-Maclaurin formulachannel coding theoremexponential error boundssparse superposition codes
Measures of information, entropy (94A17) Channel models (including quantum) in information and communication theory (94A40) Coding theorems (Shannon theory) (94A24) Source coding (94A29)
Related Items (1)
Cites Work
- A Mathematical Theory of Communication
- Least Squares Superposition Codes With Bernoulli Dictionary are Still Reliable at Rates up to Capacity
- Polynomial Codes Over Certain Finite Fields
- Finite Sample Analysis of Approximate Message Passing Algorithms
- Channel Polarization: A Method for Constructing Capacity-Achieving Codes for Symmetric Binary-Input Memoryless Channels
- Sparse Regression Codes
- Least Squares Superposition Codes of Moderate Dictionary Size Are Reliable at Rates up to Capacity
- Capacity-Achieving Sparse Superposition Codes via Approximate Message Passing Decoding
- Threshold Saturation via Spatial Coupling: Why Convolutional LDPC Ensembles Perform So Well over the BEC
- Fast Sparse Superposition Codes Have Near Exponential Error Probability for <formula formulatype="inline"><tex Notation="TeX">$R<{\cal C}$</tex></formula>
- Approximate Message-Passing Decoder and Capacity Achieving Sparse Superposition Codes
- Elements of Information Theory
This page was built for publication: An improved analysis of least squares superposition codes with Bernoulli dictionary
