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Capacity-Achieving Codes for Noisy Channels with Bounded Graphical Complexity and
 

Summary: 1
Capacity-Achieving Codes for Noisy Channels
with Bounded Graphical Complexity and
Maximum Likelihood Decoding
Chun-Hao Hsu and Achilleas Anastasopoulos
Electrical Engineering and Computer Science Department
University of Michigan
Ann Arbor, MI, 48109-2122
email: {chhsu, anastas}@umich.edu
Submitted: February 2006
Abstract
In this paper, capacity-achieving codes for memoryless binary-input output-symmetric (MBIOS)
channels under maximum-likelihood (ML) decoding with bounded graphical complexity are investigated.
The graphical complexity of a code is defined as the number of edges in the graphical representation
of the code per information bit and is proportional to the decoding complexity per information bit per
iteration under iterative decoding.
Irregular repeat-accumulate (IRA) codes are studied first. By deriving their asymptotic average
weight distribution (AAWD) it is shown that simple nonsystematic IRA ensembles outperform systematic
IRA and regular low-density parity-check (LDPC) ensembles with the same graphical complexity, and
are only 0.124 dB away from the Shannon limit for the binary-input additive white Gaussian noise

  

Source: Anastasopoulos, Achilleas - Department of Electrical Engineering and Computer Science, University of Michigan

 

Collections: Engineering; Computer Technologies and Information Sciences