9 citations · 9 across the 4 of their papers we have counts for
6 papers
SynDe: Syndrome-guided Decoding of Raw Nanopore Reads
Anisha Banerjee, Roman Sokolovskii, Thomas Heinis +3
Nanopore sequencing technology remains highly error-prone, making efficient error correction essential in DNA-based data storage. Prior work addressed high error rates using convol…
Lower Bounds for the Algorithmic Complexity of Learned Indexes
Luis Alberto Croquevielle, Roman Sokolovskii, Thomas Heinis
Learned index structures aim to accelerate queries by training machine learning models to approximate the rank function associated with a database attribute. While effective in pra…
Finite-Length Scaling of SC-LDPC Codes With a Limited Number of Decoding Iterations
Roman Sokolovskii, Alexandre Graell i Amat, Fredrik Brännström
We propose four finite-length scaling laws to predict the frame error rate (FER) performance of spatially-coupled low-density parity-check codes under full belief propagation (BP)…
On Doped SC-LDPC Codes for Streaming
Roman Sokolovskii, Alexandre Graell i Amat, Fredrik Brännström
In streaming applications, doping improves the performance of spatially-coupled low-density parity-check (SC-LDPC) codes by creating reduced-degree check nodes in the coupled chain…
Finite-Length Scaling of Spatially Coupled LDPC Codes Under Window Decoding Over the BEC
Roman Sokolovskii, Alexandre Graell i Amat, Fredrik Brännström
We analyze the finite-length performance of spatially coupled low-density parity-check (SC-LDPC) codes under window decoding over the binary erasure channel. In particular, we prop…
A Refined Scaling Law for Spatially Coupled LDPC Codes Over the Binary Erasure Channel
Roman Sokolovskii, Fredrik Brännström, Alexandre Graell i Amat
We propose a refined scaling law to predict the finite-length performance in the waterfall region of spatially coupled low-density parity-check codes over the binary erasure channe…