DeepAI AI Chat
Log In Sign Up

Design of Bilayer and Multi-layer LDPC Ensembles from Individual Degree Distributions

by   Eshed Ram, et al.

A new approach for designing bilayer and multi-layer LDPC codes is proposed and studied in the asymptotic regime. The ensembles are defined through individual uni-variate degree distributions, one for each layer. We present a construction that: 1) enables low-complexity decoding for high-SNR channel instances, 2) provably approaches capacity for low-SNR instances, 3) scales linearly (in terms of design complexity) in the number of layers. For the setup where decoding the second layer is significantly more costly than the first layer, we propose an optimal-cost decoding schedule and study the trade-off between code rate and decoding cost.


page 1

page 2

page 3

page 4


CRC-Aided List Decoding of Convolutional Codes in the Short Blocklength Regime

This paper identifies convolutional codes (CCs) used in conjunction with...

Reconciliation of Weakly Correlated Information Sources Utilizing Generalized EXIT Chart

The current bottleneck of continuous-variable quantum key distribution (...

Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding

Recently deep neural networks have been successfully applied in channel ...

Joint Design of Convolutional Code and CRC under Serial List Viterbi Decoding

This paper studies the joint design of optimal convolutional codes (CCs)...

Low-Complexity Concatenated LDPC-Staircase Codes

A low-complexity soft-decision concatenated FEC scheme, consisting of an...

Information Density in Multi-Layer Resistive Memories

Resistive memories store information in a crossbar arrangement of two-te...

A multi-layer network based on Sparse Ternary Codes for universal vector compression

We present the multi-layer extension of the Sparse Ternary Codes (STC) f...