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

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

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

In recent years, deep learning has become a breakthrough technique in assisting medical image diagnosis. Supervised learning using convolutional neural networks (CNN) provides state-of-the-art performance and has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Active Learning (AL) methods have been widely applied in natural image classification tasks to reduce annotation costs by selecting more valuable examples from the unlabeled data pool. However, their application in medical image segmentation tasks is limited, and there is currently no effective and universal AL-based method specifically designed for 3D medical image segmentation. To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks. We extensively validated our proposed active learning method on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our PCDAL can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The codes of this study are available at https://github.com/ortonwang/PCDAL