Publication

Hardware Architecture for List Successive Cancellation Decoding of Polar Codes

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming
2014
Journal Articles

Abstract

This brief presents a hardware architecture and algorithmic improvements for list successive cancellation (SC) decoding of polar codes. More specifically, we show how to completely avoid copying of the likelihoods, which is algorithmically the most cumbersome part of list SC decoding. The hardware architecture was synthesized for a blocklength of N = 1024 bits and list sizes L = 2, 4 using a UMC 90 nm VLSI technology. The resulting decoder can achieve a coded throughput of 181 Mb/s at a frequency of 459 MHz.

Official source

https://infoscience.epfl.ch/entities/publication/e9c88984-7a13-4768-840e-3126f151fdcd

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Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming
2014
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/e9c88984-7a13-4768-840e-3126f151fdcd

About this result

Related concepts (11)

Decoding methods

In coding theory, decoding is the process of translating received messages into codewords of a given code. There have been many common methods of mapping messages to codewords. These are often used to recover messages sent over a noisy channel, such as a binary symmetric channel. is considered a binary code with the length ; shall be elements of ; and is the distance between those elements. One may be given the message , then ideal observer decoding generates the codeword .

Viterbi decoder

A Viterbi decoder uses the Viterbi algorithm for decoding a bitstream that has been encoded using a convolutional code or trellis code. There are other algorithms for decoding a convolutionally encoded stream (for example, the Fano algorithm). The Viterbi algorithm is the most resource-consuming, but it does the maximum likelihood decoding. It is most often used for decoding convolutional codes with constraint lengths k≤3, but values up to k=15 are used in practice. Viterbi decoding was developed by Andrew J.

Low-density parity-check code

In information theory, a low-density parity-check (LDPC) code is a linear error correcting code, a method of transmitting a message over a noisy transmission channel. An LDPC code is constructed using a sparse Tanner graph (subclass of the bipartite graph). LDPC codes are , which means that practical constructions exist that allow the noise threshold to be set very close to the theoretical maximum (the Shannon limit) for a symmetric memoryless channel.

Related publications (31)

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