arXiv subjects

Zheng Shi

Publications and source records attributed to Zheng Shi.

At least 73 records · Page 4Linked to original sources

arXiv2022

Outage Performance Analysis of Full-Correlated Rayleigh MIMO Channels

The outage performance of multiple-input multiple-output (MIMO) technique has received intensive attention to meet the stringent requirement of reliable communications for 5G applications, e.g., mission-critical machine-type communication (cMTC). To account for spatial correlation effects at both transmit and receive sides, the full-correlated Rayleigh MIMO fading channels are modeled according to Kronecker correlation structure in this paper. The outage probability is expressed as a weighted sum of the generalized Fox's H functions. The simple analytical result empowers asymptotic outage analysis at high signal-to-noise ratio (SNR), which not only reveal helpful insights into understanding the behavior of fading effects, but also offer useful design guideline for MIMO configurations. Particularly, the negative impact of the spatial correlation on the outage probability is revealed by using the concept of majorization, and the asymptotic outage probability is proved to be a monotonically increasing and convex function of the transmission rate. In the end, the analytical results are validated through extensive numerical experiments.

cs.IT↗

arXiv2022

On Secure NOMA-CDRT Systems with Physical Layer Network Coding

This paper proposes a new scheme to enhance the secrecy performance of a NOMA-based coordinated direct relay transmission system (NOMA-CDRT) with an untrusted relay. The physical-layer network coding and the non-orthogonal multiple access scheme are combined to improve the spectrum efficiency. Furthermore, inter-user interference and friendly jamming signals are utilized to suppress the eavesdropping ability of the untrusted relay without affecting the acceptance quality of legitimate users. Specifically, the far user in the first slot and the near user in the second slot act as jammers to generate jamming signals to ensure secure transmissions of the confidential signals. We investigate the secrecy performance of the proposed scheme in NOMA-CDRT systems and derive the closed-form expression for the ergodic secrecy sum rate. The asymptotic analysis at high signal-to-noise ratio is performed to obtain more insights. Finally, simulation results are presented to demonstrate the effectiveness of the proposed scheme and the correctness of the theoretical analysis.

cs.IT↗

arXiv2022

AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods

We present AI-SARAH, a practical variant of SARAH. As a variant of SARAH, this algorithm employs the stochastic recursive gradient yet adjusts step-size based on local geometry. AI-SARAH implicitly computes step-size and efficiently estimates local Lipschitz smoothness of stochastic functions. It is fully adaptive, tune-free, straightforward to implement, and computationally efficient. We provide technical insight and intuitive illustrations on its design and convergence. We conduct extensive empirical analysis and demonstrate its strong performance compared with its classical counterparts and other state-of-the-art first-order methods in solving convex machine learning problems.

cs.LG↗

arXiv2022

Extreme Age of Information for Wireless-Powered Communication Systems

The extreme or maximum age of information (AoI) is analytically studied for wireless communication systems. In particular, a wireless powered single-antenna source node and a receiver (connected to the power grid) equipped with multiple antennas are considered when operated under independent Rayleigh-faded channels. Via the extreme value theory and its corresponding statistical features, we demonstrate that the extreme AoI converges to the Gumbel distribution whereas its corresponding parameters are obtained in straightforward closed-form expressions. Capitalizing on this result, the risk of the extreme AoI realization is analytically evaluated according to some relevant performance metrics, while some useful engineering insights are manifested.

cs.IT↗

arXiv2021

Sum-Rate Maximization of RIS-Aided Multi-User MIMO Systems With Statistical CSI

This paper investigates a reconfigurable intelligent surface (RIS)-aided multi-user multiple-input multiple-output (MIMO) system by considering only the statistical channel state information (CSI) at the base station (BS). We aim to maximize its sum-rate via the joint optimization of beamforming at the BS and phase shifts at the RIS. However, the multi-user MIMO transmissions and the spatial correlations make the optimization cumbersome. For tractability, a deterministic approximation is derived for the sum-rate under a large number of the reflecting elements. By adopting the approximate sum-rate for maximization, the optimal designs of the transmit beamforming and the phase shifts can be decoupled and solved in closed-forms individually. More specifically, the global optimality of the transmit beamforming can be guaranteed by using the water-filling algorithm and a sub-optimal solution of phase shifts can be obtained by using the projected gradient ascent (PGA) algorithm. By comparing to the case of the instantaneous CSI assumed at the BS, the proposed algorithm based on statistical CSI can achieve comparable performance but with much lower complexity and signaling overhead, which is more affordable and appealing for practical applications. Moreover, the impact of spatial correlation is thoroughly examined by using majorization theory.

cs.IT↗

arXiv2021

Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information

We present a novel adaptive optimization algorithm for large-scale machine learning problems. Equipped with a low-cost estimate of local curvature and Lipschitz smoothness, our method dynamically adapts the search direction and step-size. The search direction contains gradient information preconditioned by a well-scaled diagonal preconditioning matrix that captures the local curvature information. Our methodology does not require the tedious task of learning rate tuning, as the learning rate is updated automatically without adding an extra hyperparameter. We provide convergence guarantees on a comprehensive collection of optimization problems, including convex, strongly convex, and nonconvex problems, in both deterministic and stochastic regimes. We also conduct an extensive empirical evaluation on standard machine learning problems, justifying our algorithm's versatility and demonstrating its strong performance compared to other start-of-the-art first-order and second-order methods.

cs.LG↗

arXiv2021

Pseudo Landau levels, negative strain resistivity, and enhanced thermopower in twisted graphene nanoribbons

As a canonical response to the applied magnetic field, the electronic states of a metal are fundamentally reorganized into Landau levels. In Dirac metals, Landau levels can be expected without magnetic fields, provided that an inhomogeneous strain is applied to spatially modulate electron hoppings in a similar way as the Aharonov-Bohm phase. We here predict that a twisted zigzag nanoribbon of graphene exhibits strain-induced pseudo Landau levels of unexplored but analytically solvable dispersions at low energies. The presence of such dispersive pseudo Landau levels results in a negative strain resistivity characterizing the $(1+1)$-dimensional chiral anomaly if partially filled and can greatly enhance the thermopower when fully filled.

cond-mat.mes-hall↗

arXiv2021

Centimeter-Wave Free-Space Time-of-Flight Imaging

Depth cameras are emerging as a cornerstone modality with diverse applications that directly or indirectly rely on measured depth, including personal devices, robotics, and self-driving vehicles. Although time-of-flight (ToF) methods have fueled these applications, the precision and robustness of ToF methods is limited by relying on photon time-tagging or modulation after photo-conversion. Successful optical modulation approaches have been restricted fiber-coupled modulation with large coupling losses or interferometric modulation with sub-cm range, and the precision gap between interferometric methods and ToF methods is more than three orders of magnitudes. In this work, we close this gap and propose a computational imaging method for all-optical free-space correlation before photo-conversion that achieves micron-scale depth resolution with robustness to surface reflectance and ambient light with conventional silicon intensity sensors. To this end, we solve two technical challenges: modulating at GHz rates and computational phase unwrapping. We propose an imaging approach with resonant polarization modulators and devise a novel optical dual-pass frequency-doubling which achieves high modulation contrast at more than 10GHz. At the same time, centimeter-wave modulation together with a small modulation bandwidth render existing phase unwrapping methods ineffective. We tackle this problem with a neural phase unwrapping method that exploits that adjacent wraps are often highly correlated. We validate the proposed method in simulation and experimentally, where it achieves micron-scale depth precision. We demonstrate precise depth sensing independently of surface texture and ambient light and compare against existing analog demodulation methods, which we outperform across all tested scenarios.

cs.CV↗

arXiv2021

ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media

Adverse weather conditions, including snow, rain, and fog, pose a major challenge for both human and computer vision. Handling these environmental conditions is essential for safe decision making, especially in autonomous vehicles, robotics, and drones. Most of today's supervised imaging and vision approaches, however, rely on training data collected in the real world that is biased towards good weather conditions, with dense fog, snow, and heavy rain as outliers in these datasets. Without training data, let alone paired data, existing autonomous vehicles often limit themselves to good conditions and stop when dense fog or snow is detected. In this work, we tackle the lack of supervised training data by combining synthetic and indirect supervision. We present ZeroScatter, a domain transfer method for converting RGB-only captures taken in adverse weather into clear daytime scenes. ZeroScatter exploits model-based, temporal, multi-view, multi-modal, and adversarial cues in a joint fashion, allowing us to train on unpaired, biased data. We assess the proposed method on in-the-wild captures, and the proposed method outperforms existing monocular descattering approaches by 2.8 dB PSNR on controlled fog chamber measurements.

cs.CV↗

arXiv2020

Poor man's scaling: $XYZ$ Coqblin--Schrieffer model revisited

We derive the third-order poor man's scaling equation for a generic Hamiltonian describing a quantum impurity embedded into an itinerant electron gas. We show that the $XYZ$ Coqblin--Schrieffer model introduced by one of us earlier is algebraically renormalizable in the sense that the form of the Hamiltonian is preserved along the scaling trajectory, write down the scaling equations for the model, and analyze the renormalization group flows in the cases of both constant and pseudogap densities of states.

cond-mat.mes-hall↗

arXiv2020

Long distance coherence of Majorana wires

Theoretically, a pair of Majorana bound states in a topological superconductor forms a single fermionic level even at large separations, implying that the parity information is stored nonlocally. The nonlocality leads to a long-distance coherence for electrons tunneling through a Coulomb blockaded Majorana wire [Fu, Phys. Rev. Lett. 104, 056402 (2010)], an effect that can be observed, e.g., in an interferometer. Here, we examine theoretically the coherent electron transfer, taking into account that tunneling implies the long-distance transfer of charge, which is carried by one-dimensional plasmons. We show that the charge dynamics does not affect the coherence of the electron tunneling process in a topological superconductor consisting of a semiconductor wire proximitized by a single bulk superconductor. The coherence may be strongly suppressed, however, if the topological superconductivity derives from a semiconductor wire proximitized by a granular superconductor.

cond-mat.mes-hall↗

arXiv2020

Disorder correction to the minimal conductance of a nodal-point semimetal

We consider the disorder-induced correction to the minimal conductance of an anisotropic two-dimensional Dirac node or a three-dimensional Weyl node. An analytical expression is derived for the correction $δG$ to the conductance of a finite-size sample by an arbitrary potential, without taking the disorder average, in second-order perturbation theory. Considering a generic model of a short-range disorder potential, this result is used to compute the probability distribution $P(δG)$, which is compared to the numerically exact distribution obtained using the scattering matrix approach. We show that $P(δG)$ is Gaussian when the sample has a large width-to-length ratio, and study how the expectation value, the standard deviation, and the probability of finding $δG < 0$ depend on the anisotropy of the dispersion.

cond-mat.mes-hall↗

arXiv2020

Asymptotic Outage Analysis of Spatially Correlated Rayleigh MIMO Channels

The outage performance of multiple-input multiple-output (MIMO) technique has received intense attention in order to ensure the reliability requirement for mission-critical machine-type communication (cMTC) applications. In this paper, the outage probability is asymptotically studied for MIMO channels to thoroughly investigate the transmission reliability. To fully capture the spatial correlation effects, the MIMO fading channel matrix is modelled according to three types of Kronecker correlation structure, i.e., independent, semi-correlated and full-correlated Rayleigh MIMO channels. The outage probabilities under all three Kronecker models are expressed as representations of the weighted sum of the generalized Fox's H functions. The simple analytical results empower the asymptotic outage analyses at high signal-to-noise ratio (SNR), which are conducted not only to reveal helpful insights into understanding the behavior of fading effects, but also to offer useful design guideline for MIMO configurations. Particularly, the asymptotic outage probability is proved to be a monotonically increasing and convex function of the transmission rate. In the absence of the channel state information (CSI), the transmitter tends to equally allocate the total transmit power among its antennas to enhance the system reliability especially in high SNR regime. In the end, the analytical results are validated through extensive numerical experiments.

cs.IT↗

arXiv2020

Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations

Discovering the underlying behavior of complex systems is an important topic in many science and engineering disciplines. In this paper, we propose a novel neural network framework, finite difference neural networks (FDNet), to learn partial differential equations from data. Specifically, our proposed finite difference inspired network is designed to learn the underlying governing partial differential equations from trajectory data, and to iteratively estimate the future dynamical behavior using only a few trainable parameters. We illustrate the performance (predictive power) of our framework on the heat equation, with and without noise and/or forcing, and compare our results to the Forward Euler method. Moreover, we show the advantages of using a Hessian-Free Trust Region method to train the network.

stat.ML↗

arXiv2020

Twist-induced magnon Landau levels in honeycomb magnets

Lattice deformation resulting from elastic strain is known to spatially modulate the wave function overlap of the atoms on the lattice and can drastically alter the properties of the quasiparticles. Here we elaborate that a twist lattice deformation in two-dimensional honeycomb quantum magnet nanoribbons is equivalent to an elastic gauge field giving rise to magnon Landau quantization. When the ground state is ferromagnetic, dispersive Dirac-Landau levels are induced in the center of magnon bands, while for antiferromagnetic nanoribbons, the twist results in dispersive equidistant Landau levels at the top of magnon bands. The dispersions for both types of Landau levels are derived in the framework of the band theory.

cond-mat.mes-hall↗

arXiv2019

FD-Net with Auxiliary Time Steps: Fast Prediction of PDEs using Hessian-Free Trust-Region Methods

Discovering the underlying physical behavior of complex systems is a crucial, but less well-understood topic in many engineering disciplines. This study proposes a finite-difference inspired convolutional neural network framework to learn hidden partial differential equations from given data and iteratively estimate future dynamical behavior. The methodology designs the filter sizes such that they mimic the finite difference between the neighboring points. By learning the governing equation, the network predicts the future evolution of the solution by using only a few trainable parameters. In this paper, we provide numerical results to compare the efficiency of the second-order Trust-Region Conjugate Gradient (TRCG) method with the first-order ADAM optimizer.

cs.LG↗

arXiv2019

Kondo effect due to a hydrogen impurity in graphene: a multichannel Kondo problem with diverging hybridization

We consider the Kondo effect arising from a hydrogen impurity in graphene. As a first approximation, the strong covalent bond to a carbon atom removes that carbon atom without breaking the $C_{3}$ rotation symmetry, and we only retain the Hubbard interaction on the three nearest neighbors of the removed carbon atom which then behave as magnetic impurities. These three impurity spins are coupled to three conduction channels with definite helicity, two of which support a diverging local density of states (LDOS) $\propto 1/ [ | ω\ | \ln ^{2}( Λ/| ω\ | \ ) \ ] $ near the Dirac point $ω\rightarrow 0$ even though the bulk density of states vanishes linearly. We study the resulting 3-impurity multi-channel Kondo model using the numerical renormalization group method. For weak potential scattering, the ground state of the Kondo model is a particle-hole symmetric spin-$1/2$ doublet, with ferromagnetic coupling between the three impurity spins; for moderate potential scattering, the ground state becomes a particle-hole asymmetric spin singlet, with antiferromagnetic coupling between the three impurity spins. This behavior is inherited by the Anderson model containing the hydrogen impurity and all four carbon atoms in its vicinity.

cond-mat.str-el↗

arXiv2019

Magnon quantum anomalies in Weyl ferromagnets

When subjected to parallel electric field $\mathbf E$ and magnetic field $\mathbf B$, Weyl semimetals exhibit the exotic transport property known as the chiral anomaly due to the pumping of electrons between Weyl cones of opposite chiralities. When one or both electromagnetic (EM) fields are replaced by strain-induced chiral pseudo-electromagnetic (pseudo-EM) fields, other types of quantum anomalies occur. In the present paper, we will show that such quantum anomalies can be reproduced in a completely different system -- a Weyl ferromagnet whose magnonic structure remarkably encodes Weyl physics. By analytical and numerical calculations, we will show that the magnon bands can be Landau-quantized by either an inhomogeneous electric field $\mathbf E$ or a chiral pseudo-electric field $\mathbf e$ induced by a torsional strain, and magnons can be pumped along bands by either an inhomogeneous magnetic field $\mathbf B$ or a chiral pseudo-magnetic field $\mathbf b$ due to a dynamic uniaxial strain. We list the magnon quantum anomalies and the associated anomalous spin and heat currents in the Weyl ferromagnet, and show they are distinct from their Weyl semimetal counterparts.

cond-mat.mes-hall↗