113,862 research outputs found
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
Joint source-channel coding with feedback
This paper quantifies the fundamental limits of variable-length transmission
of a general (possibly analog) source over a memoryless channel with noiseless
feedback, under a distortion constraint. We consider excess distortion, average
distortion and guaranteed distortion (-semifaithful codes). In contrast to
the asymptotic fundamental limit, a general conclusion is that allowing
variable-length codes and feedback leads to a sizable improvement in the
fundamental delay-distortion tradeoff. In addition, we investigate the minimum
energy required to reproduce source samples with a given fidelity after
transmission over a memoryless Gaussian channel, and we show that the required
minimum energy is reduced with feedback and an average (rather than maximal)
power constraint.Comment: To appear in IEEE Transactions on Information Theor
Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels
A method is proposed, called channel polarization, to construct code
sequences that achieve the symmetric capacity of any given binary-input
discrete memoryless channel (B-DMC) . The symmetric capacity is the highest
rate achievable subject to using the input letters of the channel with equal
probability. Channel polarization refers to the fact that it is possible to
synthesize, out of independent copies of a given B-DMC , a second set of
binary-input channels such that, as becomes
large, the fraction of indices for which is near 1
approaches and the fraction for which is near 0
approaches . The polarized channels are
well-conditioned for channel coding: one need only send data at rate 1 through
those with capacity near 1 and at rate 0 through the remaining. Codes
constructed on the basis of this idea are called polar codes. The paper proves
that, given any B-DMC with and any target rate , there
exists a sequence of polar codes such that
has block-length , rate , and probability of
block error under successive cancellation decoding bounded as P_{e}(N,R) \le
\bigoh(N^{-\frac14}) independently of the code rate. This performance is
achievable by encoders and decoders with complexity for each.Comment: The version which appears in the IEEE Transactions on Information
Theory, July 200
Characterization of Information Channels for Asymptotic Mean Stationarity and Stochastic Stability of Non-stationary/Unstable Linear Systems
Stabilization of non-stationary linear systems over noisy communication
channels is considered. Stochastically stable sources, and unstable but
noise-free or bounded-noise systems have been extensively studied in
information theory and control theory literature since 1970s, with a renewed
interest in the past decade. There have also been studies on non-causal and
causal coding of unstable/non-stationary linear Gaussian sources. In this
paper, tight necessary and sufficient conditions for stochastic stabilizability
of unstable (non-stationary) possibly multi-dimensional linear systems driven
by Gaussian noise over discrete channels (possibly with memory and feedback)
are presented. Stochastic stability notions include recurrence, asymptotic mean
stationarity and sample path ergodicity, and the existence of finite second
moments. Our constructive proof uses random-time state-dependent stochastic
drift criteria for stabilization of Markov chains. For asymptotic mean
stationarity (and thus sample path ergodicity), it is sufficient that the
capacity of a channel is (strictly) greater than the sum of the logarithms of
the unstable pole magnitudes for memoryless channels and a class of channels
with memory. This condition is also necessary under a mild technical condition.
Sufficient conditions for the existence of finite average second moments for
such systems driven by unbounded noise are provided.Comment: To appear in IEEE Transactions on Information Theor
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