Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems

Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as a potential candidate for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method

Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments

Hybrid massive MIMO structures with reduced hardware complexity and power consumption have been widely studied as a potential candidate for millimeter wave (mmWave) communications. Channel estimators that require knowledge of the array response, such as those using compressive sensing (CS) methods, may suffer from performance degradation when array-inherent impairments bring unknown phase errors and gain errors to the antenna elements. In this paper, we design matrix completion (MC)-based channel estimation schemes which are robust against the array-inherent impairments. We first design an open-loop training scheme that can sample entries from the effective channel matrix randomly and is compatible with the phase shifter-based hybrid system. Leveraging the low-rank property of the effective channel matrix, we then design a channel estimator based on the generalized conditional gradient (GCG) framework and the alternating minimization (AltMin) approach. The resulting estimator is immune to array-inherent impairments and can be implemented to systems with any array shapes for its independence of the array response. In addition, we extend our design to sample a transformed channel matrix following the concept of inductive matrix completion (IMC), which can be solved efficiently using our proposed estimator and achieve similar performance with a lower requirement of the dynamic range of the transmission power per antenna. Numerical results demonstrate the advantages of our proposed MC-based channel estimators in terms of estimation performance, computational complexity and robustness against array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS channel estimator.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

The revealed preference theory of stable matchings with one-sided preferences

Published in Games and Economic Behavior, 2020. DOI: 10.1016/j.geb.2020.08.015</p

Improved Three-step Phase Shifting Profilometry Using Digital Fringe Pattern Projection

In this paper, an improved method for three-step phase shifting profilometry (PSP) is presented to eliminate the errors introduced by the second order harmonic when digital projection are used to generate fringe patterns. Firstly, the error caused by the second order harmonic is theoretically analyzed. Then based on the error analysis and principle of PSP, we propose a novel approach, called improved threestep phase shifting profilometry (I3PSP), to eliminate the influence of the second order harmonic. Finally, simulations are performed to verify the effectiveness of the proposed I3PSP, which demonstrate that the reconstruction accuracy of using three-step PSP has been significantly improved by the proposed I3PSP

Learning Spiking Neural Network from Easy to Hard task

Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL

Shift estimation method based fringe pattern profilometry and performance comparison

In this paper, we present and study two approaches to fringe pattern profilometry (FPP) technique. Based on generalized analysis model for fringe pattern profilometry (FPP), Inverse Function based Shift Estimation (IFSE) and Gradient-based Shift Estimation (GSE) are proposed to calculate the shift between the projected and deformed fringe patterns. Further, computer simulations are utilized to compare the performance between these two methods. Meanwhile, we also compare these two algorithms with Phase Shift profilometry (PSP). It can be seen that both of these two shift estimation algorithms can significantly improve the measurement accuracy when the fringe patterns are nonlinearly distorted

Special Libraries, August 1933

Volume 24, Issue 7https://scholarworks.sjsu.edu/sla_sl_1933/1006/thumbnail.jp