1,684 research outputs found
Low-Complexity Sub-band Digital Predistortion for Spurious Emission Suppression in Noncontiguous Spectrum Access
Noncontiguous transmission schemes combined with high power-efficiency
requirements pose big challenges for radio transmitter and power amplifier (PA)
design and implementation. Due to the nonlinear nature of the PA, severe
unwanted emissions can occur, which can potentially interfere with neighboring
channel signals or even desensitize the own receiver in frequency division
duplexing (FDD) transceivers. In this article, to suppress such unwanted
emissions, a low-complexity sub-band DPD solution, specifically tailored for
spectrally noncontiguous transmission schemes in low-cost devices, is proposed.
The proposed technique aims at mitigating only the selected spurious
intermodulation distortion components at the PA output, hence allowing for
substantially reduced processing complexity compared to classical linearization
solutions. Furthermore, novel decorrelation based parameter learning solutions
are also proposed and formulated, which offer reduced computing complexity in
parameter estimation as well as the ability to track time-varying features
adaptively. Comprehensive simulation and RF measurement results are provided,
using a commercial LTE-Advanced mobile PA, to evaluate and validate the
effectiveness of the proposed solution in real world scenarios. The obtained
results demonstrate that highly efficient spurious component suppression can be
obtained using the proposed solutions
High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis
Independent Component Analysis (ICA) is a dimensionality reduction technique
that can boost efficiency of machine learning models that deal with probability
density functions, e.g. Bayesian neural networks. Algorithms that implement
adaptive ICA converge slower than their nonadaptive counterparts, however, they
are capable of tracking changes in underlying distributions of input features.
This intrinsically slow convergence of adaptive methods combined with existing
hardware implementations that operate at very low clock frequencies necessitate
fundamental improvements in both algorithm and hardware design. This paper
presents an algorithm that allows efficient hardware implementation of ICA.
Compared to previous work, our FPGA implementation of adaptive ICA improves
clock frequency by at least one order of magnitude and throughput by at least
two orders of magnitude. Our proposed algorithm is not limited to ICA and can
be used in various machine learning problems that use stochastic gradient
descent optimization
Location-Verification and Network Planning via Machine Learning Approaches
In-region location verification (IRLV) in wireless networks is the problem of
deciding if user equipment (UE) is transmitting from inside or outside a
specific physical region (e.g., a safe room). The decision process exploits the
features of the channel between the UE and a set of network access points
(APs). We propose a solution based on machine learning (ML) implemented by a
neural network (NN) trained with the channel features (in particular, noisy
attenuation values) collected by the APs for various positions both inside and
outside the specific region. The output is a decision on the UE position
(inside or outside the region). By seeing IRLV as an hypothesis testing
problem, we address the optimal positioning of the APs for minimizing either
the area under the curve (AUC) of the receiver operating characteristic (ROC)
or the cross entropy (CE) between the NN output and ground truth (available
during the training). In order to solve the minimization problem we propose a
twostage particle swarm optimization (PSO) algorithm. We show that for a long
training and a NN with enough neurons the proposed solution achieves the
performance of the Neyman-Pearson (N-P) lemma.Comment: Accepted for Workshop on Machine Learning for Communications, June 07
2019, Avignon, Franc
Discrete-Time Chaotic-Map Truly Random Number Generators: Design, Implementation, and Variability Analysis of the Zigzag Map
In this paper, we introduce a novel discrete chaotic map named zigzag map
that demonstrates excellent chaotic behaviors and can be utilized in Truly
Random Number Generators (TRNGs). We comprehensively investigate the map and
explore its critical chaotic characteristics and parameters. We further present
two circuit implementations for the zigzag map based on the switched current
technique as well as the current-mode affine interpolation of the breakpoints.
In practice, implementation variations can deteriorate the quality of the
output sequence as a result of variation of the chaotic map parameters. In
order to quantify the impact of variations on the map performance, we model the
variations using a combination of theoretical analysis and Monte-Carlo
simulations on the circuits. We demonstrate that even in the presence of the
map variations, a TRNG based on the zigzag map passes all of the NIST 800-22
statistical randomness tests using simple post processing of the output data.Comment: To appear in Analog Integrated Circuits and Signal Processing (ALOG
Untrained, physics-informed neural networks for structured illumination microscopy
In recent years there has been great interest in using deep neural networks
(DNN) for super-resolution image reconstruction including for structured
illumination microscopy (SIM). While these methods have shown very promising
results, they all rely on data-driven, supervised training strategies that need
a large number of ground truth images, which is experimentally difficult to
realize. For SIM imaging, there exists a need for a flexible, general, and
open-source reconstruction method that can be readily adapted to different
forms of structured illumination. We demonstrate that we can combine a deep
neural network with the forward model of the structured illumination process to
reconstruct sub-diffraction images without training data. The resulting
physics-informed neural network (PINN) can be optimized on a single set of
diffraction limited sub-images and thus doesn't require any training set. We
show with simulated and experimental data that this PINN can be applied to a
wide variety of SIM methods by simply changing the known illumination patterns
used in the loss function and can achieve resolution improvements that match
well with theoretical expectations.Comment: Preprint for journal submission. 21 Pages. 5 main text figures. 6
supplementary figure
Spatial Multiplexing of QPSK Signals with a Single Radio: Antenna Design and Over-the-Air Experiments
The paper describes the implementation and performance analysis of the first
fully-operational beam-space MIMO antenna for the spatial multiplexing of two
QPSK streams. The antenna is composed of a planar three-port radiator with two
varactor diodes terminating the passive ports. Pattern reconfiguration is used
to encode the MIMO information onto orthogonal virtual basis patterns in the
far-field. A measurement campaign was conducted to compare the performance of
the beam-space MIMO system with a conventional 2-by-?2 MIMO system under
realistic propagation conditions. Propagation measurements were conducted for
both systems and the mutual information and symbol error rates were estimated
from Monte-Carlo simulations over the measured channel matrices. The results
show the beam-space MIMO system and the conventional MIMO system exhibit
similar finite-constellation capacity and error performance in NLOS scenarios
when there is sufficient scattering in the channel. In comparison, in LOS
channels, the capacity performance is observed to depend on the relative
polarization of the receiving antennas.Comment: 31 pages, 23 figure
Magma and fluid migration at Yellowstone Caldera in the last three decades inferred from InSAR, leveling and gravity measurements
We studied the Yellowstone caldera geological unrest between 1977 and 2010 by investigating
temporal changes in differential Interferometric Synthetic Aperture Radar (InSAR), precise spirit leveling and
gravity measurements. The analysis of the 1992–2010 displacement time series, retrieved by applying the SBAS
InSAR technique, allowed the identification of three areas of deformation: (i) the Mallard Lake (ML) and Sour
Creek (SC) resurgent domes, (ii) a region close to the Northern Caldera Rim (NCR), and (iii) the eastern Snake
River Plain (SRP). While the eastern SRP shows a signal related to tectonic deformation, the other two regions
are influenced by the caldera unrest. We removed the tectonic signal from the InSAR displacements, and we
modeled the InSAR, leveling, and gravity measurements to retrieve the best fitting source parameters. Our
findings confirmed the existence of different distinct sources, beneath the brittle-ductile transition zone, which
have been intermittently active during the last three decades. Moreover, we interpreted our results in the light
of existing seismic tomography studies. Concerning the SC dome, we highlighted the role of hydrothermal
fluids as the driving force behind the 1977–1983 uplift; since 1983–1993 the deformation source transformed
into a deeper one with a higher magmatic component. Furthermore, our results support the magmatic nature
of the deformation source beneath ML dome for the overall investigated period. Finally, the uplift at NCR is
interpreted as magma accumulation, while its subsidence could either be the result of fluids migration outside
the caldera or the gravitational adjustment of the source from a spherical to a sill-like geometr
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