328 research outputs found
Impact of Residual Transmit RF Impairments on Training-Based MIMO Systems
Radio-frequency (RF) impairments, that exist intimately in wireless
communications systems, can severely degrade the performance of traditional
multiple-input multiple-output (MIMO) systems. Although compensation schemes
can cancel out part of these RF impairments, there still remains a certain
amount of impairments. These residual impairments have fundamental impact on
the MIMO system performance. However, most of the previous works have neglected
this factor. In this paper, a training-based MIMO system with residual transmit
RF impairments (RTRI) is considered. In particular, we derive a new channel
estimator for the proposed model, and find that RTRI can create an irreducible
estimation error floor. Moreover, we show that, in the presence of RTRI, the
optimal training sequence length can be larger than the number of transmit
antennas, especially in the low and high signal-to-noise ratio (SNR) regimes.
An increase in the proposed approximated achievable rate is also observed by
adopting the optimal training sequence length. When the training and data
symbol powers are required to be equal, we demonstrate that, at high SNRs,
systems with RTRI demand more training, whereas at low SNRs, such demands are
nearly the same for all practical levels of RTRI.Comment: Accepted for publication at the IEEE International Conference on
Communications (ICC 2014), 6 pages, 5 figure
On the MIMO Capacity with Residual Transceiver Hardware Impairments
Radio-frequency (RF) impairments in the transceiver hardware of communication
systems (e.g., phase noise (PN), high power amplifier (HPA) nonlinearities, or
in-phase/quadrature-phase (I/Q) imbalance) can severely degrade the performance
of traditional multiple-input multiple-output (MIMO) systems. Although
calibration algorithms can partially compensate these impairments, the
remaining distortion still has substantial impact. Despite this, most prior
works have not analyzed this type of distortion. In this paper, we investigate
the impact of residual transceiver hardware impairments on the MIMO system
performance. In particular, we consider a transceiver impairment model, which
has been experimentally validated, and derive analytical ergodic capacity
expressions for both exact and high signal-to-noise ratios (SNRs). We
demonstrate that the capacity saturates in the high-SNR regime, thereby
creating a finite capacity ceiling. We also present a linear approximation for
the ergodic capacity in the low-SNR regime, and show that impairments have only
a second-order impact on the capacity. Furthermore, we analyze the effect of
transceiver impairments on large-scale MIMO systems; interestingly, we prove
that if one increases the number of antennas at one side only, the capacity
behaves similar to the finite-dimensional case. On the contrary, if the number
of antennas on both sides increases with a fixed ratio, the capacity ceiling
vanishes; thus, impairments cause only a bounded offset in the capacity
compared to the ideal transceiver hardware case.Comment: Accepted for publication at the IEEE International Conference on
Communications (ICC 2014), 7 pages, 6 figure
Developing Fairness Rules for Talent Intelligence Management System
Talent management is an important business strategy, but inherently expensive due to the unique, subjective, and developing nature of each talent. Applying artificial intelligence (AI) to analyze large-scale data, talent intelligence management system (TIMS) is intended to address the talent management problems of organizations. While TIMS has greatly improved the efficiency of talent management, especially in the processes of talent selection and matching, high-potential talent discovery and talent turnover prediction, it also brings new challenges. Ethical issues, such as how to maintain fairness when designing and using TIMS, are typical examples. Through the Delphi study in a leading global AI company, this paper proposes eight fairness rules to avoid fairness risks when designing TIMS
Alternating Deep Low Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications
Exponential function is a fundamental signal form in general signal
processing and biomedical applications, such as magnetic resonance spectroscopy
and imaging. How to reduce the sampling time of these signals is an important
problem. Sub-Nyquist sampling can accelerate signal acquisition but bring in
artifacts. Recently, the low rankness of these exponentials has been applied to
implicitly constrain the deep learning network through the unrolling of low
rank Hankel factorization algorithm. However, only depending on the implicit
low rank constraint cannot provide the robust reconstruction, such as sampling
rate mismatches. In this work, by introducing the explicit low rank prior to
constrain the deep learning, we propose an Alternating Deep Low Rank approach
(ADLR) that utilizes deep learning and optimization solvers alternately. The
former solver accelerates the reconstruction while the latter one corrects the
reconstruction error from the mismatch. The experiments on both general
exponential functions and realistic biomedical magnetic resonance data show
that, compared with the state-of-the-art methods, ADLR can achieve much lower
reconstruction error and effectively alleviates the decrease of reconstruction
quality with sampling rate mismatches.Comment: 14 page
Spectral element method for modeling Lamb wave interaction with open and closed crack
Lamb wave-based structural health monitoring is one of the most widely used damage detection techniques. For quantitatively identifying the damage, damage features that Lamb waves carry may need to be carefully studied by numerical simulation. In this paper, spectral element method (SEM) is used to simulate Lamb wave interaction with open and closed crack. Cracked spectral element models are established for open and closed cracks, respectively. Results calculated by SEM are compared with the conventional finite element method to verify the proposed model. Some simulations are conducted to study different damage features between open and closed crack models. Wave reflection and transmission ratios with different crack depths are also quantitatively analyzed. Damage features obtained are used to conduct a simple experiment to identify the location and size of the crack
Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
Medical image segmentation methods often rely on fully supervised approaches
to achieve excellent performance, which is contingent upon having an extensive
set of labeled images for training. However, annotating medical images is both
expensive and time-consuming. Semi-supervised learning offers a solution by
leveraging numerous unlabeled images alongside a limited set of annotated ones.
In this paper, we introduce a semi-supervised medical image segmentation method
based on the mean-teacher model, referred to as Dual-Decoder Consistency via
Pseudo-Labels Guided Data Augmentation (DCPA). This method combines consistency
regularization, pseudo-labels, and data augmentation to enhance the efficacy of
semi-supervised segmentation. Firstly, the proposed model comprises both
student and teacher models with a shared encoder and two distinct decoders
employing different up-sampling strategies. Minimizing the output discrepancy
between decoders enforces the generation of consistent representations, serving
as regularization during student model training. Secondly, we introduce mixup
operations to blend unlabeled data with labeled data, creating mixed data and
thereby achieving data augmentation. Lastly, pseudo-labels are generated by the
teacher model and utilized as labels for mixed data to compute unsupervised
loss. We compare the segmentation results of the DCPA model with six
state-of-the-art semi-supervised methods on three publicly available medical
datasets. Beyond classical 10\% and 20\% semi-supervised settings, we
investigate performance with less supervision (5\% labeled data). Experimental
outcomes demonstrate that our approach consistently outperforms existing
semi-supervised medical image segmentation methods across the three
semi-supervised settings
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