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Constrained Coding and Soft Iterative Decoding (Paperback, Softcover reprint of the original 1st ed. 2001)
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Constrained Coding and Soft Iterative Decoding (Paperback, Softcover reprint of the original 1st ed. 2001)
Series: The Springer International Series in Engineering and Computer Science, 627
Expected to ship within 10 - 15 working days
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Constrained Coding and Soft Iterative Decoding is the first work to
combine the issues of constrained coding and soft iterative
decoding (e.g., turbo and LDPC codes) from a unified point of view.
Since constrained coding is widely used in magnetic and optical
storage, it is necessary to use some special techniques (modified
concatenation scheme or bit insertion) in order to apply soft
iterative decoding. Recent breakthroughs in the design and decoding
of error-control codes (ECCs) show significant potential for
improving the performance of many communications systems. ECCs such
as turbo codes and low-density parity check (LDPC) codes can be
represented by graphs and decoded by passing probabilistic (a.k.a.
`soft') messages along the edges of the graph. This message-passing
algorithm yields powerful decoders whose performance can approach
the theoretical limits on capacity. This exposition uses `normal
graphs,' introduced by Forney, which extend in a natural manner to
block diagram representations of the system and provide a simple
unified framework for the decoding of ECCs, constrained codes, and
channels with memory. Soft iterative decoding is illustrated by the
application of turbo codes and LDPC codes to magnetic recording
channels. For magnetic and optical storage, an issue arises in the
use of constrained coding, which places restrictions on the
sequences that can be transmitted through the channel; the use of
constrained coding in combination with soft ECC decoders is
addressed by the modified concatenation scheme also known as
`reverse concatenation.' Moreover, a soft constraint decoder yields
additional coding gain from the redundancy in the constraint, which
may be of practical interest in the case of optical storage. In
addition, this monograph presents several other research results
(including the design of sliding-block lossless compression codes,
and the decoding of array codes as LDPC codes). Constrained Coding
and Soft Iterative Decoding will prove useful to students,
researchers and professional engineers who are interested in
understanding this new soft iterative decoding paradigm and
applying it in communications and storage systems.
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