629 research outputs found
Channel combining and splitting for cutoff rate improvement
The cutoff rate of a discrete memoryless channel (DMC) is often
used as a figure of merit, alongside the channel capacity . Given a
channel consisting of two possibly correlated subchannels , , the
capacity function always satisfies , while there are
examples for which . This fact that cutoff rate can
be ``created'' by channel splitting was noticed by Massey in his study of an
optical modulation system modeled as a 'ary erasure channel. This paper
demonstrates that similar gains in cutoff rate can be achieved for general
DMC's by methods of channel combining and splitting. Relation of the proposed
method to Pinsker's early work on cutoff rate improvement and to Imai-Hirakawa
multi-level coding are also discussed.Comment: 5 pages, 7 figures, 2005 IEEE International Symposium on Information
Theory, Adelaide, Sept. 4-9, 200
Error Exponents for Variable-length Block Codes with Feedback and Cost Constraints
Variable-length block-coding schemes are investigated for discrete memoryless
channels with ideal feedback under cost constraints. Upper and lower bounds are
found for the minimum achievable probability of decoding error as
a function of constraints R, \AV, and on the transmission rate,
average cost, and average block length respectively. For given and \AV,
the lower and upper bounds to the exponent are
asymptotically equal as . The resulting reliability
function, , as a
function of and \AV, is concave in the pair (R, \AV) and generalizes
the linear reliability function of Burnashev to include cost constraints. The
results are generalized to a class of discrete-time memoryless channels with
arbitrary alphabets, including additive Gaussian noise channels with amplitude
and power constraints
Properties and Construction of Polar Codes
Recently, Ar{\i}kan introduced the method of channel polarization on which
one can construct efficient capacity-achieving codes, called polar codes, for
any binary discrete memoryless channel. In the thesis, we show that decoding
algorithm of polar codes, called successive cancellation decoding, can be
regarded as belief propagation decoding, which has been used for decoding of
low-density parity-check codes, on a tree graph. On the basis of the
observation, we show an efficient construction method of polar codes using
density evolution, which has been used for evaluation of the error probability
of belief propagation decoding on a tree graph. We further show that channel
polarization phenomenon and polar codes can be generalized to non-binary
discrete memoryless channels. Asymptotic performances of non-binary polar
codes, which use non-binary matrices called the Reed-Solomon matrices, are
better than asymptotic performances of the best explicitly known binary polar
code. We also find that the Reed-Solomon matrices are considered to be natural
generalization of the original binary channel polarization introduced by
Ar{\i}kan.Comment: Master thesis. The supervisor is Toshiyuki Tanaka. 24 pages, 3
figure
The Reliability Function of Lossy Source-Channel Coding of Variable-Length Codes with Feedback
We consider transmission of discrete memoryless sources (DMSes) across
discrete memoryless channels (DMCs) using variable-length lossy source-channel
codes with feedback. The reliability function (optimum error exponent) is shown
to be equal to where is the rate-distortion
function of the source, is the maximum relative entropy between output
distributions of the DMC, and is the Shannon capacity of the channel. We
show that, in this setting and in this asymptotic regime, separate
source-channel coding is, in fact, optimal.Comment: Accepted to IEEE Transactions on Information Theory in Apr. 201
How to Achieve the Capacity of Asymmetric Channels
We survey coding techniques that enable reliable transmission at rates that
approach the capacity of an arbitrary discrete memoryless channel. In
particular, we take the point of view of modern coding theory and discuss how
recent advances in coding for symmetric channels help provide more efficient
solutions for the asymmetric case. We consider, in more detail, three basic
coding paradigms.
The first one is Gallager's scheme that consists of concatenating a linear
code with a non-linear mapping so that the input distribution can be
appropriately shaped. We explicitly show that both polar codes and spatially
coupled codes can be employed in this scenario. Furthermore, we derive a
scaling law between the gap to capacity, the cardinality of the input and
output alphabets, and the required size of the mapper.
The second one is an integrated scheme in which the code is used both for
source coding, in order to create codewords distributed according to the
capacity-achieving input distribution, and for channel coding, in order to
provide error protection. Such a technique has been recently introduced by
Honda and Yamamoto in the context of polar codes, and we show how to apply it
also to the design of sparse graph codes.
The third paradigm is based on an idea of B\"ocherer and Mathar, and
separates the two tasks of source coding and channel coding by a chaining
construction that binds together several codewords. We present conditions for
the source code and the channel code, and we describe how to combine any source
code with any channel code that fulfill those conditions, in order to provide
capacity-achieving schemes for asymmetric channels. In particular, we show that
polar codes, spatially coupled codes, and homophonic codes are suitable as
basic building blocks of the proposed coding strategy.Comment: 32 pages, 4 figures, presented in part at Allerton'14 and published
in IEEE Trans. Inform. Theor
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