We study the limits of performance of Gallager codes (low-density parity-check codes) over binary linear intersymbol interference (ISI) channels with additive Gaussian noise. Using the graph representations of the channel, the code and the sum-product message-passing detector/decoder, we prove two error concentration theorems. Our proofs expand on previous work by handling complications introduced by the channel memory. We circumvent these problems by considering not just linear Gallager codes but also their cosets and by distinguishing between dierent types of message
ow neighborhoods depending on the actual transmitted symbols. We compute the noise tolerance threshold using a suitably developed density evolution algorithm and verify by simulation that the thresholds represent accurate predictions of the performance of the sum-product algorithm for nite (but large) block lengths. We also demonstrate that for high rates the thresholds are very close to the theoretical limit of performance for Gallager codes over ISI channels. If C denotes the capacity of a binary ISI channel and if C i:i:d: denotes the maximal achievable mutual information rate when the channel inputs are independent identically distributed (i.i.d.) binary random variables (C i:i:d: C), we prove that the maximum information rate achievable by the sum-product decoder of a Gallager (coset) code is upper-bounded by C i:i:d:. The last topic investigated is
|
4388
|
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
– Pearl
- 1988
|
|
4364
|
Elements of Information Theory
– Cover, Thomas
- 1991
|
|
1258
|
Randomized Algorithms
– Motwani, Raghavan
- 1995
|
|
788
|
Near Shannon limit error-correcting coding and decoding: turbo codes
– Berrou, Glavieux, et al.
- 1993
|
|
771
|
Information Theory and Reliable Communication
– Gallager
- 1968
|
|
658
|
Optimal decoding of linear codes for minimizing symbol error rate
– Bahl, Cocke, et al.
- 1974
|
|
438
|
Factor graphs and the sum-product algorithm
– Kschischang, Frey, et al.
- 2001
|
|
356
|
Low-Density Parity-Check Codes
– Gallager
- 1963
|
|
252
|
Good error-correcting codes based on very sparse matrices
– MacKay
- 1999
|
|
204
|
A recursive approach to low complexity codes
– Tanner
- 1981
|
|
193
|
Turbo decoding as an instance of Pearl’s “belief propagation algorithm
– McEliece, MacKay, et al.
- 1996
|
|
156
|
Near Shannon limit performance of low density parity check codes,” Electron
– MacKay, Neal
- 1996
|
|
136
|
A viterbi algorithm with soft-decision outputs and its applications
– Hagenauer, Hoeher
- 1989
|
|
103
|
Iterative correction of intersymbol interference
– Douillard, Jezequel, et al.
- 1995
|
|
96
|
On the design of low-density parity-check codes within 0.0045 dB of the Shannon limit
– Chung, Forney, et al.
- 2001
|
|
94
|
The capacity of low-density parity check codes under messagepassing decoding
– Richardson, Urbanke
- 2001
|
|
87
|
Analysis of sumproduct decoding of low-density parity-check codes using a Gaussian approximation
– Chung, Richardson, et al.
- 2001
|
|
78
|
Proakis, Digital Communications
– G
- 2001
|
|
68
|
Codes and iterative decoding on general graphs
– Wiberg, Loeliger, et al.
- 1995
|
|
64
|
Analysis of random processes via and-or tree evaluation
– Luby, Mitzenmacher, et al.
- 1998
|
|
58
|
Analysis of low density codes and improved designs using irregular graphs
– Luby, Mitzenmacher, et al.
- 1998
|
|
49
|
Optimum soft-output detection for channels with intersymbol interference
– Li, Vucetic, et al.
- 1995
|
|
40
|
Codes for digital recorders
– Immink, Siegel, et al.
- 1998
|
|
29
|
On the information rate of binary-input channels with memory
– Arnold, Loeliger
- 2001
|
|
26
|
The intersymbol interference channel: Lower bounds on capacity and channel precoding loss
– Shamai, Laroia
- 1996
|
|
23
|
Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference
– Jr
- 1972
|
|
21
|
Information rates for a discrete-time Gaussian channel with intersymbol interference and stationary inputs
– Ozarow, Wyner
- 1991
|
|
17
|
Algorithm for continuous decoding of turbo codes
– Benedetto, Divsalar, et al.
- 1996
|
|
16
|
Matched spectral-null codes for partial response channels
– Karabed, Siegel
- 1991
|
|
11
|
Design of capacity-approaching low-density parity-check codes
– Richardson, Shokrollahi, et al.
- 2001
|
|
11
|
On the equivalence between SOVA and Max-Log-MAP decodings
– Fossorier, Burkert, et al.
- 1998
|
|
11
|
Low density parity check codes for magnetic recording
– Fan, Friedmann, et al.
- 1999
|
|
10
|
Thresholds for turbo codes
– Richardson, Urbanke
- 2000
|
|
9
|
Capacity and information rates of discrete-time channels with memory
– Hirt
- 1988
|
|
8
|
Improved low density parity check codes using irregular graphs and belief propagation
– Luby, Mitzenmacher, et al.
- 1998
|
|
7
|
Iterative correction of isi via equalization and decoding with priors
– Tuchler, Koetter, et al.
- 2000
|
|
3
|
The minimum description length principle for modeling magnetic recording channels,” submitted to J-SAC special issues on signal processing for high density storage channels
– Kavcic, Srinivasan
- 2001
|
|
3
|
Novel algorithm for continuous decoding of turbo codes
– Bai, X, et al.
- 1999
|
|
2
|
An intuitive justi and a simpli implementation of the MAP decoder for convolutional codes
– Viterbi
- 1998
|
|
1
|
Codes on graphs: Normal realizations," to appear in
– Jr
- 2001
|
|
1
|
Low complexity iterative decoding with decision aided equalization for magnetic recording channels
– Wu, Cio
- 2001
|
|
1
|
Gallager codes - recent results." available at http://wol.ra.phy.cam.ac.uk
– MacKay
|
|
1
|
The compound channel capacity of a class of channels
– Lapidoth, Teatar
- 1998
|
|
1
|
Turbo codes for PR4: Parallel versus seral concatenation
– Oberg, Swanson, et al.
- 1999
|