585 research outputs found
Multilevel Decoders Surpassing Belief Propagation on the Binary Symmetric Channel
In this paper, we propose a new class of quantized message-passing decoders
for LDPC codes over the BSC. The messages take values (or levels) from a finite
set. The update rules do not mimic belief propagation but instead are derived
using the knowledge of trapping sets. We show that the update rules can be
derived to correct certain error patterns that are uncorrectable by algorithms
such as BP and min-sum. In some cases even with a small message set, these
decoders can guarantee correction of a higher number of errors than BP and
min-sum. We provide particularly good 3-bit decoders for 3-left-regular LDPC
codes. They significantly outperform the BP and min-sum decoders, but more
importantly, they achieve this at only a fraction of the complexity of the BP
and min-sum decoders.Comment: 5 pages, in Proc. of 2010 IEEE International Symposium on Information
Theory (ISIT
Analysis and Design of Binary Message-Passing Decoders
Binary message-passing decoders for low-density parity-check (LDPC) codes are
studied by using extrinsic information transfer (EXIT) charts. The channel
delivers hard or soft decisions and the variable node decoder performs all
computations in the L-value domain. A hard decision channel results in the
well-know Gallager B algorithm, and increasing the output alphabet from hard
decisions to two bits yields a gain of more than 1.0 dB in the required signal
to noise ratio when using optimized codes. The code optimization requires
adapting the mixing property of EXIT functions to the case of binary
message-passing decoders. Finally, it is shown that errors on cycles consisting
only of degree two and three variable nodes cannot be corrected and a necessary
and sufficient condition for the existence of a cycle-free subgraph is derived.Comment: 8 pages, 6 figures, submitted to the IEEE Transactions on
Communication
Decoding of Non-Binary LDPC Codes Using the Information Bottleneck Method
Recently, a novel lookup table based decoding method for binary low-density
parity-check codes has attracted considerable attention. In this approach,
mutual-information maximizing lookup tables replace the conventional operations
of the variable nodes and the check nodes in message passing decoding.
Moreover, the exchanged messages are represented by integers with very small
bit width. A machine learning framework termed the information bottleneck
method is used to design the corresponding lookup tables. In this paper, we
extend this decoding principle from binary to non-binary codes. This is not a
straightforward extension, but requires a more sophisticated lookup table
design to cope with the arithmetic in higher order Galois fields. Provided bit
error rate simulations show that our proposed scheme outperforms the log-max
decoding algorithm and operates close to sum-product decoding.Comment: This paper has been presented at IEEE International Conference on
Communications (ICC'19) in Shangha
Iterative Quantization Using Codes On Graphs
We study codes on graphs combined with an iterative message passing algorithm
for quantization. Specifically, we consider the binary erasure quantization
(BEQ) problem which is the dual of the binary erasure channel (BEC) coding
problem. We show that duals of capacity achieving codes for the BEC yield codes
which approach the minimum possible rate for the BEQ. In contrast, low density
parity check codes cannot achieve the minimum rate unless their density grows
at least logarithmically with block length. Furthermore, we show that duals of
efficient iterative decoding algorithms for the BEC yield efficient encoding
algorithms for the BEQ. Hence our results suggest that graphical models may
yield near optimal codes in source coding as well as in channel coding and that
duality plays a key role in such constructions.Comment: 10 page
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