7,477 research outputs found
Capacity and Modulations with Peak Power Constraint
A practical communication channel often suffers from constraints on input
other than the average power, such as the peak power constraint. In order to
compare achievable rates with different constellations as well as the channel
capacity under such constraints, it is crucial to take these constraints into
consideration properly. In this paper, we propose a direct approach to compare
the achievable rates of practical input constellations and the capacity under
such constraints. As an example, we study the discrete-time complex-valued
additive white Gaussian noise (AWGN) channel and compare the capacity under the
peak power constraint with the achievable rates of phase shift keying (PSK) and
quadrature amplitude modulation (QAM) input constellations.Comment: 9 pages with 12 figures. Preparing for submissio
On Low Complexity Detection for QAM Isomorphic Constellations
Despite of the known gap from the Shannon's capacity, several standards are
still employing QAM or star shape constellations, mainly due to the existing
low complexity detectors. In this paper, we investigate the low complexity
detection for a family of QAM isomorphic constellations. These constellations
are known to perform very close to the peak-power limited capacity,
outperforming the DVB-S2X standard constellations. The proposed strategy is to
first remap the received signals to the QAM constellation using the existing
isomorphism and then break the log likelihood ratio computations to two one
dimensional PAM constellations. Gains larger than 0.6 dB with respect to QAM
can be obtained over the peak power limited channels without any increase in
detection complexity. Our scheme also provides a systematic way to design
constellations with low complexity one dimensional detectors. Several open
problems are discussed at the end of the paper.Comment: Submitted to IEEE GLOBECOM 201
Error Rate Analysis of Cognitive Radio Transmissions with Imperfect Channel Sensing
This paper studies the symbol error rate performance of cognitive radio
transmissions in the presence of imperfect sensing decisions. Two different
transmission schemes, namely sensing-based spectrum sharing (SSS) and
opportunistic spectrum access (OSA), are considered. In both schemes, secondary
users first perform channel sensing, albeit with possible errors. In SSS,
depending on the sensing decisions, they adapt the transmission power level and
coexist with primary users in the channel. On the other hand, in OSA, secondary
users are allowed to transmit only when the primary user activity is not
detected. Initially, for both transmission schemes, general formulations for
the optimal decision rule and error probabilities are provided for arbitrary
modulation schemes under the assumptions that the receiver is equipped with the
sensing decision and perfect knowledge of the channel fading, and the primary
user's received faded signals at the secondary receiver has a Gaussian mixture
distribution. Subsequently, the general approach is specialized to rectangular
quadrature amplitude modulation (QAM). More specifically, optimal decision rule
is characterized for rectangular QAM, and closed-form expressions for the
average symbol error probability attained with the optimal detector are derived
under both transmit power and interference constraints. The effects of
imperfect channel sensing decisions, interference from the primary user and its
Gaussian mixture model, and the transmit power and interference constraints on
the error rate performance of cognitive transmissions are analyzed
Designing Power-Efficient Modulation Formats for Noncoherent Optical Systems
We optimize modulation formats for the additive white Gaussian noise channel
with a nonnegative input constraint, also known as the intensity-modulated
direct detection channel, with and without confining them to a lattice
structure. Our optimization criteria are the average electrical and optical
power. The nonnegativity input signal constraint is translated into a conical
constraint in signal space, and modulation formats are designed by sphere
packing inside this cone. Some remarkably dense packings are found, which yield
more power-efficient modulation formats than previously known. For example, at
a spectral efficiency of 1 bit/s/Hz, the obtained modulation format offers a
0.86 dB average electrical power gain and 0.43 dB average optical power gain
over the previously best known modulation formats to achieve a symbol error
rate of 10^-6. This modulation turns out to have a lattice-based structure. At
a spectral efficiency of 3/2 bits/s/Hz and to achieve a symbol error rate of
10^-6, the modulation format obtained for optimizing the average electrical
power offers a 0.58 dB average electrical power gain over the best
lattice-based modulation and 2.55 dB gain over the best previously known
format. However, the modulation format optimized for average optical power
offers a 0.46 dB average optical power gain over the best lattice-based
modulation and 1.35 dB gain over the best previously known format.Comment: Submitted to Globecom 201
Capacity of a Nonlinear Optical Channel with Finite Memory
The channel capacity of a nonlinear, dispersive fiber-optic link is
revisited. To this end, the popular Gaussian noise (GN) model is extended with
a parameter to account for the finite memory of realistic fiber channels. This
finite-memory model is harder to analyze mathematically but, in contrast to
previous models, it is valid also for nonstationary or heavy-tailed input
signals. For uncoded transmission and standard modulation formats, the new
model gives the same results as the regular GN model when the memory of the
channel is about 10 symbols or more. These results confirm previous results
that the GN model is accurate for uncoded transmission. However, when coding is
considered, the results obtained using the finite-memory model are very
different from those obtained by previous models, even when the channel memory
is large. In particular, the peaky behavior of the channel capacity, which has
been reported for numerous nonlinear channel models, appears to be an artifact
of applying models derived for independent input in a coded (i.e., dependent)
scenario
- …