Deep Polar Codes
- URL: http://arxiv.org/abs/2308.03004v1
- Date: Sun, 6 Aug 2023 03:29:18 GMT
- Title: Deep Polar Codes
- Authors: Geon Choi and Namyoon Lee
- Abstract summary: We introduce a novel class of pre-transformed polar codes, termed as deep polar codes.
We first present a deep polar encoder that harnesses a series of multi-layered polar transformations with varying sizes.
Our encoding method offers flexibility in rate-profiling, embracing a wide range of code rates and blocklengths.
- Score: 19.265010348250897
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this paper, we introduce a novel class of pre-transformed polar codes,
termed as deep polar codes. We first present a deep polar encoder that
harnesses a series of multi-layered polar transformations with varying sizes.
Our approach to encoding enables a low-complexity implementation while
significantly enhancing the weight distribution of the code. Moreover, our
encoding method offers flexibility in rate-profiling, embracing a wide range of
code rates and blocklengths. Next, we put forth a low-complexity decoding
algorithm called successive cancellation list with backpropagation parity
checks (SCL-BPC). This decoding algorithm leverages the parity check equations
in the reverse process of the multi-layered pre-transformed encoding for SCL
decoding. Additionally, we present a low-latency decoding algorithm that
employs parallel-SCL decoding by treating partially pre-transformed bit
patterns as additional frozen bits. Through simulations, we demonstrate that
deep polar codes outperform existing pre-transformed polar codes in terms of
block error rates across various code rates under short block lengths, while
maintaining low encoding and decoding complexity. Furthermore, we show that
concatenating deep polar codes with cyclic-redundancy-check codes can achieve
the meta-converse bound of the finite block length capacity within 0.4 dB in
some instances.
Related papers
- Breadth-first graph traversal union-find decoder [0.0]
We develop variants of the union-find decoder that simplify its implementation and provide potential decoding speed advantages.
We show how these methods can be adapted to decode non-topological quantum low-density-parity-check codes.
arXiv Detail & Related papers (2024-07-22T18:54:45Z) - Collective Bit Flipping-Based Decoding of Quantum LDPC Codes [0.6554326244334866]
We improve both the error correction performance and decoding latency of variable degree-3 (dv-3) QLDPC codes under iterative decoding.
Our decoding scheme is based on applying a modified version of bit flipping (BF) decoding, namely two-bit bit flipping (TBF) decoding.
arXiv Detail & Related papers (2024-06-24T18:51:48Z) - Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decoding [62.25533750469467]
Low-Density Parity-Check (LDPC) codes possess several advantages over other families of codes.
The proposed approach is shown to outperform the decoding performance of existing popular codes by orders of magnitude.
arXiv Detail & Related papers (2024-06-09T12:08:56Z) - Learning Linear Block Error Correction Codes [62.25533750469467]
We propose for the first time a unified encoder-decoder training of binary linear block codes.
We also propose a novel Transformer model in which the self-attention masking is performed in a differentiable fashion for the efficient backpropagation of the code gradient.
arXiv Detail & Related papers (2024-05-07T06:47:12Z) - Bit-flipping Decoder Failure Rate Estimation for (v,w)-regular Codes [84.0257274213152]
We propose a new technique to provide accurate estimates of the DFR of a two-iterations (parallel) bit flipping decoder.
We validate our results, providing comparisons of the modeled and simulated weight of the syndrome, incorrectly-guessed error bit distribution at the end of the first iteration, and two-itcrypteration Decoding Failure Rates (DFR)
arXiv Detail & Related papers (2024-01-30T11:40:24Z) - Improved Logical Error Rate via List Decoding of Quantum Polar Codes [8.122270502556372]
We show that the successive cancellation list decoder (SCL) is an efficient decoder for classical polar codes with low decoding error.
We apply SCL decoding to a novel version of quantum polar codes based on the polarization weight method.
Both SCL-E and SCL-C maintain the complexity O(LN logN) of SCL for code size N and list size L.
arXiv Detail & Related papers (2023-04-10T17:56:10Z) - Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes [59.55193427277134]
Reed-Muller (RM) codes achieve the capacity of general binary-input memoryless symmetric channels.
RM codes only admit limited sets of rates.
Efficient decoders are available for RM codes at finite lengths.
arXiv Detail & Related papers (2023-01-16T04:11:14Z) - Neural Belief Propagation Decoding of Quantum LDPC Codes Using
Overcomplete Check Matrices [60.02503434201552]
We propose to decode QLDPC codes based on a check matrix with redundant rows, generated from linear combinations of the rows in the original check matrix.
This approach yields a significant improvement in decoding performance with the additional advantage of very low decoding latency.
arXiv Detail & Related papers (2022-12-20T13:41:27Z) - Scalable Polar Code Construction for Successive Cancellation List
Decoding: A Graph Neural Network-Based Approach [11.146177972345138]
This paper first maps a polar code to a heterogeneous graph called the polar-code-construction message-passing graph.
Next, a graph-neural-network-based iterative message-passing algorithm is proposed which aims to find a PCCMP graph that corresponds to the polar code.
Numerical experiments show that IMP-based polar-code constructions outperform classical constructions under CA-SCL decoding.
arXiv Detail & Related papers (2022-07-03T19:27:43Z) - Construction of Polar Codes with Reinforcement Learning [13.977646909897796]
This paper formulates the polar-code construction problem for the successive-cancellation list (SCL) decoder as a maze-traversing game.
The proposed method provides a novel technique for polar-code construction that no longer depends on sorting and selecting bit-channels by reliability.
arXiv Detail & Related papers (2020-09-19T17:59:02Z) - Pruning Neural Belief Propagation Decoders [77.237958592189]
We introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning.
We achieve performance within 0.27 dB and 1.5 dB of the ML performance while reducing the complexity of the decoder.
arXiv Detail & Related papers (2020-01-21T12:05:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.