4,780 research outputs found
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
Rewriting Flash Memories by Message Passing
This paper constructs WOM codes that combine rewriting and error correction
for mitigating the reliability and the endurance problems in flash memory. We
consider a rewriting model that is of practical interest to flash applications
where only the second write uses WOM codes. Our WOM code construction is based
on binary erasure quantization with LDGM codes, where the rewriting uses
message passing and has potential to share the efficient hardware
implementations with LDPC codes in practice. We show that the coding scheme
achieves the capacity of the rewriting model. Extensive simulations show that
the rewriting performance of our scheme compares favorably with that of polar
WOM code in the rate region where high rewriting success probability is
desired. We further augment our coding schemes with error correction
capability. By drawing a connection to the conjugate code pairs studied in the
context of quantum error correction, we develop a general framework for
constructing error-correction WOM codes. Under this framework, we give an
explicit construction of WOM codes whose codewords are contained in BCH codes.Comment: Submitted to ISIT 201
Multiresolution vector quantization
Multiresolution source codes are data compression algorithms yielding embedded source descriptions. The decoder of a multiresolution code can build a source reproduction by decoding the embedded bit stream in part or in whole. All decoding procedures start at the beginning of the binary source description and decode some fraction of that string. Decoding a small portion of the binary string gives a low-resolution reproduction; decoding more yields a higher resolution reproduction; and so on. Multiresolution vector quantizers are block multiresolution source codes. This paper introduces algorithms for designing fixed- and variable-rate multiresolution vector quantizers. Experiments on synthetic data demonstrate performance close to the theoretical performance limit. Experiments on natural images demonstrate performance improvements of up to 8 dB over tree-structured vector quantizers. Some of the lessons learned through multiresolution vector quantizer design lend insight into the design of more sophisticated multiresolution codes
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
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