5,051 research outputs found
Asynchronous CDMA Systems with Random Spreading-Part I: Fundamental Limits
Spectral efficiency for asynchronous code division multiple access (CDMA)
with random spreading is calculated in the large system limit allowing for
arbitrary chip waveforms and frequency-flat fading. Signal to interference and
noise ratios (SINRs) for suboptimal receivers, such as the linear minimum mean
square error (MMSE) detectors, are derived. The approach is general and
optionally allows even for statistics obtained by under-sampling the received
signal.
All performance measures are given as a function of the chip waveform and the
delay distribution of the users in the large system limit. It turns out that
synchronizing users on a chip level impairs performance for all chip waveforms
with bandwidth greater than the Nyquist bandwidth, e.g., positive roll-off
factors. For example, with the pulse shaping demanded in the UMTS standard,
user synchronization reduces spectral efficiency up to 12% at 10 dB normalized
signal-to-noise ratio. The benefits of asynchronism stem from the finding that
the excess bandwidth of chip waveforms actually spans additional dimensions in
signal space, if the users are de-synchronized on the chip-level. The analysis
of linear MMSE detectors shows that the limiting interference effects can be
decoupled both in the user domain and in the frequency domain such that the
concept of the effective interference spectral density arises. This generalizes
and refines Tse and Hanly's concept of effective interference.
In Part II, the analysis is extended to any linear detector that admits a
representation as multistage detector and guidelines for the design of low
complexity multistage detectors with universal weights are provided
New advances in H∞ control and filtering for nonlinear systems
The main objective of this special issue is to
summarise recent advances in H∞ control and filtering
for nonlinear systems, including time-delay, hybrid and
stochastic systems. The published papers provide new
ideas and approaches, clearly indicating the advances
made in problem statements, methodologies or applications
with respect to the existing results. The special
issue also includes papers focusing on advanced and
non-traditional methods and presenting considerable
novelties in theoretical background or experimental
setup. Some papers present applications to newly
emerging fields, such as network-based control and
estimation
Time-causal and time-recursive spatio-temporal receptive fields
We present an improved model and theory for time-causal and time-recursive
spatio-temporal receptive fields, based on a combination of Gaussian receptive
fields over the spatial domain and first-order integrators or equivalently
truncated exponential filters coupled in cascade over the temporal domain.
Compared to previous spatio-temporal scale-space formulations in terms of
non-enhancement of local extrema or scale invariance, these receptive fields
are based on different scale-space axiomatics over time by ensuring
non-creation of new local extrema or zero-crossings with increasing temporal
scale. Specifically, extensions are presented about (i) parameterizing the
intermediate temporal scale levels, (ii) analysing the resulting temporal
dynamics, (iii) transferring the theory to a discrete implementation, (iv)
computing scale-normalized spatio-temporal derivative expressions for
spatio-temporal feature detection and (v) computational modelling of receptive
fields in the lateral geniculate nucleus (LGN) and the primary visual cortex
(V1) in biological vision.
We show that by distributing the intermediate temporal scale levels according
to a logarithmic distribution, we obtain much faster temporal response
properties (shorter temporal delays) compared to a uniform distribution.
Specifically, these kernels converge very rapidly to a limit kernel possessing
true self-similar scale-invariant properties over temporal scales, thereby
allowing for true scale invariance over variations in the temporal scale,
although the underlying temporal scale-space representation is based on a
discretized temporal scale parameter.
We show how scale-normalized temporal derivatives can be defined for these
time-causal scale-space kernels and how the composed theory can be used for
computing basic types of scale-normalized spatio-temporal derivative
expressions in a computationally efficient manner.Comment: 39 pages, 12 figures, 5 tables in Journal of Mathematical Imaging and
Vision, published online Dec 201
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