69 research outputs found
Myths and Realities of Rateless Coding
Fixed-rate and rateless channel codes are generally treated separately in the related research literature and so, a novice in the field inevitably gets the impression that these channel codes are unrelated. By contrast, in this treatise, we endeavor to further develop a link between the traditional fixed-rate codes and the recently developed rateless codes by delving into their underlying attributes. This joint treatment is beneficial for two principal reasons. First, it facilitates the task of researchers and practitioners, who might be familiar with fixed-rate codes and would like to jump-start their understanding of the recently developed concepts in the rateless reality. Second, it provides grounds for extending the use of the well-understood code design tools — originally contrived for fixed-rate codes — to the realm of rateless codes. Indeed, these versatile tools proved to be vital in the design of diverse fixed-rate-coded communications systems, and thus our hope is that they will further elucidate the associated performance ramifications of the rateless coded schemes
Raptor Codes in the Low SNR Regime
In this paper, we revisit the design of Raptor codes for binary input
additive white Gaussian noise (BIAWGN) channels, where we are interested in
very low signal to noise ratios (SNRs). A linear programming degree
distribution optimization problem is defined for Raptor codes in the low SNR
regime through several approximations. We also provide an exact expression for
the polynomial representation of the degree distribution with infinite maximum
degree in the low SNR regime, which enables us to calculate the exact value of
the fractions of output nodes of small degrees. A more practical degree
distribution design is also proposed for Raptor codes in the low SNR regime,
where we include the rate efficiency and the decoding complexity in the
optimization problem, and an upper bound on the maximum rate efficiency is
derived for given design parameters. Simulation results show that the Raptor
code with the designed degree distributions can approach rate efficiencies
larger than 0.95 in the low SNR regime.Comment: Submitted to the IEEE Transactions on Communications. arXiv admin
note: text overlap with arXiv:1510.0772
Zero-rate feedback can achieve the empirical capacity
The utility of limited feedback for coding over an individual sequence of
DMCs is investigated. This study complements recent results showing how limited
or noisy feedback can boost the reliability of communication. A strategy with
fixed input distribution is given that asymptotically achieves rates
arbitrarily close to the mutual information induced by and the
state-averaged channel. When the capacity achieving input distribution is the
same over all channel states, this achieves rates at least as large as the
capacity of the state averaged channel, sometimes called the empirical
capacity.Comment: Revised version of paper originally submitted to IEEE Transactions on
Information Theory, Nov. 2007. This version contains further revisions and
clarification
Rateless Coding for Gaussian Channels
A rateless code-i.e., a rate-compatible family of codes-has the property that
codewords of the higher rate codes are prefixes of those of the lower rate
ones. A perfect family of such codes is one in which each of the codes in the
family is capacity-achieving. We show by construction that perfect rateless
codes with low-complexity decoding algorithms exist for additive white Gaussian
noise channels. Our construction involves the use of layered encoding and
successive decoding, together with repetition using time-varying layer weights.
As an illustration of our framework, we design a practical three-rate code
family. We further construct rich sets of near-perfect rateless codes within
our architecture that require either significantly fewer layers or lower
complexity than their perfect counterparts. Variations of the basic
construction are also developed, including one for time-varying channels in
which there is no a priori stochastic model.Comment: 18 page
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