421 research outputs found
Conditions for a Monotonic Channel Capacity
Motivated by results in optical communications, where the performance can
degrade dramatically if the transmit power is sufficiently increased, the
channel capacity is characterized for various kinds of memoryless vector
channels. It is proved that for all static point-to-point channels, the channel
capacity is a nondecreasing function of power. As a consequence, maximizing the
mutual information over all input distributions with a certain power is for
such channels equivalent to maximizing it over the larger set of input
distributions with upperbounded power. For interference channels such as
optical wavelength-division multiplexing systems, the primary channel capacity
is always nondecreasing with power if all interferers transmit with identical
distributions as the primary user. Also, if all input distributions in an
interference channel are optimized jointly, then the achievable sum-rate
capacity is again nondecreasing. The results generalizes to the channel
capacity as a function of a wide class of costs, not only power.Comment: This is an updated and expanded version of arXiv:1108.039
On the BICM Capacity
Optimal binary labelings, input distributions, and input alphabets are
analyzed for the so-called bit-interleaved coded modulation (BICM) capacity,
paying special attention to the low signal-to-noise ratio (SNR) regime. For
8-ary pulse amplitude modulation (PAM) and for 0.75 bit/symbol, the folded
binary code results in a higher capacity than the binary reflected gray code
(BRGC) and the natural binary code (NBC). The 1 dB gap between the additive
white Gaussian noise (AWGN) capacity and the BICM capacity with the BRGC can be
almost completely removed if the input symbol distribution is properly
selected. First-order asymptotics of the BICM capacity for arbitrary input
alphabets and distributions, dimensions, mean, variance, and binary labeling
are developed. These asymptotics are used to define first-order optimal (FOO)
constellations for BICM, i.e. constellations that make BICM achieve the Shannon
limit -1.59 \tr{dB}. It is shown that the \Eb/N_0 required for reliable
transmission at asymptotically low rates in BICM can be as high as infinity,
that for uniform input distributions and 8-PAM there are only 72 classes of
binary labelings with a different first-order asymptotic behavior, and that
this number is reduced to only 26 for 8-ary phase shift keying (PSK). A general
answer to the question of FOO constellations for BICM is also given: using the
Hadamard transform, it is found that for uniform input distributions, a
constellation for BICM is FOO if and only if it is a linear projection of a
hypercube. A constellation based on PAM or quadrature amplitude modulation
input alphabets is FOO if and only if they are labeled by the NBC; if the
constellation is based on PSK input alphabets instead, it can never be FOO if
the input alphabet has more than four points, regardless of the labeling.Comment: Submitted to the IEEE Transactions on Information Theor
On the symbol error probability of regular polytopes
An exact expression for the symbol error probability of the four-dimensional
24-cell in Gaussian noise is derived. Corresponding expressions for other
regular convex polytopes are summarized. Numerically stable versions of these
error probabilities are also obtained
Influence of Behavioral Models on Multiuser Channel Capacity
In order to characterize the channel capacity of a wavelength channel in a
wavelength-division multiplexed (WDM) system, statistical models are needed for
the transmitted signals on the other wavelengths. For example, one could assume
that the transmitters for all wavelengths are configured independently of each
other, that they use the same signal power, or that they use the same
modulation format. In this paper, it is shown that these so-called behavioral
models have a profound impact on the single-wavelength achievable information
rate. This is demonstrated by establishing, for the first time, upper and lower
bounds on the maximum achievable rate under various behavioral models, for a
rudimentary WDM channel model
Signal Shaping for BICM at Low SNR
The mutual information of bit-interleaved coded modulation (BICM) systems,
sometimes called the BICM capacity, is investigated at low signal-to-noise
ratio (SNR), i.e., in the wideband regime. A new linear transform that depends
on bits' probabilities is introduced. This transform is used to prove the
asymptotical equivalence between certain BICM systems with uniform and
nonuniform input distributions. Using known results for BICM systems with a
uniform input distribution, we completely characterize the combinations of
input alphabet, input distribution, and binary labeling that achieve the
Shannon limit -1.59 dB. The main conclusion is that a BICM system achieves the
Shannon limit at low SNR if and only if it can be represented as a zero-mean
linear projection of a hypercube, which is the same condition as for uniform
input distributions. Hence, probabilistic shaping offers no extra degrees of
freedom to optimize the low-SNR mutual information of BICM systems, in addition
to what is provided by geometrical shaping. These analytical conclusions are
confirmed by numerical results, which also show that for a fixed input
alphabet, probabilistic shaping of BICM can improve the mutual information in
the low and medium SNR range over any coded modulation system with a uniform
input distribution
Achievable Rates for Four-Dimensional Coded Modulation with a Bit-Wise Receiver
We study achievable rates for four-dimensional (4D) constellations for
spectrally efficient optical systems based on a (suboptimal) bit-wise receiver.
We show that PM-QPSK outperforms the best 4D constellation designed for uncoded
transmission by approximately 1 dB. Numerical results using LDPC codes validate
the analysis
Nonlinear fiber capacity
In this semi-tutorial presentation, we review fundamental information theory for links with and without memory, in the linear and nonlinear regimes. A comparison between channel models with long (but finite) memory and infinite memory yields an unexpected result
Voronoi regions for binary linear block codes
The Voronoi regions of a block code govern many aspects of the code's performance on a Gaussian channel, and they are fundamental instruments in, for example, error probability analysis and soft-decision decoding. The article presents an efficient method for finding the boundaries of the Voronoi regions for an arbitrary binary linear block code. Two theoretical results together lead to the Voronoi regions. First, it is shown that the question of the Voronoi neighborship can be reduced into testing a simpler relation, called the Gabriel neighborship. Second, a fast method of recognising Gabriel neighbors is proposed. These results are finally employed to describe the Voronoi regions for the Golay codes and several BCH codes, including Hamming codes
Bounds on the Per-Sample Capacity of Zero-Dispersion Simplified Fiber-Optical Channel Models
A number of simplified models, based on perturbation theory, have been
proposed for the fiber-optical channel and have been extensively used in the
literature. Although these models are mainly developed for the low-power
regime, they are used at moderate or high powers as well. It remains unclear to
what extent the capacity of these models is affected by the simplifying
assumptions under which they are derived. In this paper, we consider single
channel data transmission based on three continuous-time optical models i) a
regular perturbative channel, ii) a logarithmic perturbative channel, and iii)
the stochastic nonlinear Schr\"odinger (NLS) channel. We apply two simplifying
assumptions on these channels to obtain analytically tractable discrete-time
models. Namely, we neglect the channel memory (fiber dispersion) and we use a
sampling receiver. These assumptions bring into question the physical relevance
of the models studied in the paper. Therefore, the results should be viewed as
a first step toward analyzing more realistic channels. We investigate the
per-sample capacity of the simplified discrete-time models. Specifically, i) we
establish tight bounds on the capacity of the regular perturbative channel; ii)
we obtain the capacity of the logarithmic perturbative channel; and iii) we
present a novel upper bound on the capacity of the zero-dispersion NLS channel.
Our results illustrate that the capacity of these models departs from each
other at high powers because these models yield different capacity pre-logs.
Since all three models are based on the same physical channel, our results
highlight that care must be exercised in using simplified channel models in the
high-power regime
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