arXiv subjects

Li Wei

Publications and source records attributed to Li Wei.

At least 73 records · Page 4Linked to original sources

arXiv2022

Multi-User Wireless Communications with Holographic MIMO Surfaces: A Convenient Channel Model and Spectral Efficiency Analysis

The multi-user Holographic Multiple-Input and Multiple-Output Surface (MU-HMIMOS) paradigm, which is capable of realizing large continuous apertures with minimal power consumption and of shaping radio wave propagation at will, has been recently considered as an energy-efficient solution for future wireless networks. The tractable channel modeling of MU-HMIMOS signal propagation is one of the most critical challenges, mainly due to the coupling effect induced by the excessively large number of closely spaced patch antennas. In this paper, we focus on this challenge for downlink communications and model the electromagnetic channel in the wavenumber domain using the Fourier plane wave representation. Based on the proposed model, we devise a Zero-Forcing (ZF) precoding scheme, capitalizing on the sampled channel variance that depends on the number and spacing of the HMIMOS patch antennas, and perform a spectral efficiency analysis. Our simulation results showcase that the more patch antennas and the larger their spacing is, the performance of the considered MU-HMIMOS system improves. In addition, it is demonstrated that our theoretical performance expressions approximate sufficiently well the simulated spectral efficiency, even for the highly correlated cases, thus verifying the effectiveness and robustness of the presented analytical framework.

cs.IT↗

arXiv2021

Bidirectional Approximate Message Passing for RIS-Assisted Multi-User MISO Communications

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and lowpower configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted multi-user wireless communication system and present a joint channel estimation and signal recovery algorithm in this paper. Specifically, we propose a bidirectional approximate message passing algorithm that applies the Taylor series expansion and Gaussian approximation to simplify the sum-product algorithm in the formulated problem. Our simulation results show that the proposed algorithm shows the superiority over a state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed algorithms.

cs.IT↗

arXiv2021

Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective

In this work we study the problem of measuring the fairness of a machine learning model under noisy information. Focusing on group fairness metrics, we investigate the particular but common situation when the evaluation requires controlling for the confounding effect of covariate variables. In a practical setting, we might not be able to jointly observe the covariate and group information, and a standard workaround is to then use proxies for one or more of these variables. Prior works have demonstrated the challenges with using a proxy for sensitive attributes, and strong independence assumptions are needed to provide guarantees on the accuracy of the noisy estimates. In contrast, in this work we study using a proxy for the covariate variable and present a theoretical analysis that aims to characterize weaker conditions under which accurate fairness evaluation is possible. Furthermore, our theory identifies potential sources of errors and decouples them into two interpretable parts $γ$ and $ε$. The first part $γ$ depends solely on the performance of the proxy such as precision and recall, whereas the second part $ε$ captures correlations between all the variables of interest. We show that in many scenarios the error in the estimates is dominated by $γ$ via a linear dependence, whereas the dependence on the correlations $ε$ only constitutes a lower order term. As a result we expand the understanding of scenarios where measuring model fairness via proxies can be an effective approach. Finally, we compare, via simulations, the theoretical upper-bounds to the distribution of simulated estimation errors and show that assuming some structure on the data, even weak, is key to significantly improve both theoretical guarantees and empirical results.

cs.LG↗

arXiv2020

Joint Channel Estimation and Signal Recovery in RIS-Assisted Multi-User MISO Communications

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Channel estimation and signal recovery in RISbased systems are among the most critical technical challenges, due to the large number of unknown variables referring to the RIS unit elements and the transmitted signals. In this paper, we focus on the downlink of a RIS-assisted multi-user Multiple Input Single Output (MISO) communication system and present a joint channel estimation and signal recovery scheme based on the PARAllel FACtor (PARAFAC) decomposition. This decomposition unfolds the cascaded channel model and facilitates signal recovery using the Bilinear Generalized Approximate Message Passing (BiG-AMP) algorithm. The proposed method includes an alternating least squares algorithm to iteratively estimate the equivalent matrix, which consists of the transmitted signals and the channels between the base station and RIS, as well as the channels between the RIS and the multiple users. Our selective simulation results show that the proposed scheme outperforms a benchmark scheme that uses genie-aided information knowledge. We also provide insights on the impact of different RIS parameter settings on the proposed scheme.

cs.IT↗

arXiv2020

Improvement of Classification in One-Stage Detector

RetinaNet proposed Focal Loss for classification task and improved one-stage detectors greatly. However, there is still a gap between it and two-stage detectors. We analyze the prediction of RetinaNet and find that the misalignment of classification and localization is the main factor. Most of predicted boxes, whose IoU with ground-truth boxes are greater than 0.5, while their classification scores are lower than 0.5, which shows that the classification task still needs to be optimized. In this paper we proposed an object confidence task for this problem, and it shares features with classification task. This task uses IoUs between samples and ground-truth boxes as targets, and it only uses losses of positive samples in training, which can increase loss weight of positive samples in classification task training. Also the joint of classification score and object confidence will be used to guide NMS. Our method can not only improve classification task, but also ease misalignment of classification and localization. To evaluate the effectiveness of this method, we show our experiments on MS COCO 2017 dataset. Without whistles and bells, our method can improve AP by 0.7% and 1.0% on COCO validation dataset with ResNet50 and ResNet101 respectively at same training configs, and it can achieve 38.4% AP with two times training time. Code is at: http://github.com/chenzuge1/RetinaNet-Conf.git.

cs.CV↗

arXiv2020

One-dimensional van der Waals heterostructures as efficient metal-free oxygen electrocatalysts

Two-dimensional covalent organic frameworks (2D-COFs) are an emerging family of catalytical materials with well-defined molecular structures. The stacking of 2D nanosheets and large intrinsic bandgaps significantly impair their performance. Here, we report coaxial one-dimensional van der Waals heterostructures (1D vdWHs) comprised of a carbon nanotube (CNT) core and a thickness tunable thienothiophene-pyrene COF shell using a solution based in situ wrapping method. Density functional theory calculations and in-operando and ex-situ spectroscopic analysis show that the carbon-sulfur region in the thienothiophene groups is the active catalytic site. The unique coaxial structure enables controllable n-doping from the CNT core to the COF shell depending on COF shell thickness, which lowers the bandgap and work function of COF. Consequently, the charge transfer barrier between the active catalytic site and adsorbed oxygen intermediates becomes lower, resulting in a dramatic enhancement in their catalytic activity for oxygen redox reactions. It enables a high-performance rechargeable zinc-air battery with a specific capacity of 696 mAh gZn-1 under a high current density of 40 mA cm-2 and excellent cycling stability. 1D vdWHs open the door to create multi-dimensional vdWHs for exploring fundamental physics and chemistry, as well as practical applications in electrochemistry, electronics, photonics, and beyond.

cond-mat.mtrl-sci↗

arXiv2020

Hybrid Beamforming for RIS-Empowered Multi-hop Terahertz Communications: A DRL-based Method

Wireless communication in the TeraHertz band (0.1--10 THz) is envisioned as one of the key enabling technologies for the future six generation (6G) wireless communication systems. However, very high propagation attenuations and molecular absorptions of THz frequencies often limit the signal transmission distance and coverage range. Benefited from the recent breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing smart radio propagation environment, we propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies. We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss. Simulation results show that our proposed scheme is able to improve 50\% more coverage range of THz communications compared with the benchmarks. Furthermore, it is also shown that our proposed DRL-based method is a state-of-the-art method to solve the NP-bard beamforming problem, especially when the signals at RIS-empowered THz communication networks experience multiple hops.

eess.SP↗

arXiv2020

TEAM: An Taylor Expansion-Based Method for Generating Adversarial Examples

Although Deep Neural Networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples.Adversarial training is one of the most effective methods to improve the robustness of DNNs, and it is generally considered as solving a saddle point problem that minimizes risk and maximizes perturbation.Therefore, powerful adversarial examples can effectively replicate the situation of perturbation maximization to solve the saddle point problem.The method proposed in this paper approximates the output of DNNs in the input neighborhood by using the Taylor expansion, and then optimizes it by using the Lagrange multiplier method to generate adversarial examples. If it is used for adversarial training, the DNNs can be effectively regularized and the defects of the model can be improved.

cs.LG↗

arXiv2020

Parallel Factor Decomposition Channel Estimation in RIS-Assisted Multi-User MISO Communication

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low power configuration enabling massive connectivity and low latency communications. Channel estimation in RIS-based systems is one of the most critical challenges due to the large number of reflecting unit elements and their distinctive hardware constraints. In this paper, we focus on the downlink of a RIS-assisted multi-user Multiple Input Single Output (MISO) communication system and present a method based on the PARAllel FACtor (PARAFAC) decomposition to unfold the resulting cascaded channel model. The proposed method includes an alternating least squares algorithm to iteratively estimate the channel between the base station and RIS, as well as the channels between RIS and users. Our selective simulation results show that the proposed iterative channel estimation method outperforms a benchmark scheme using genie-aided information. We also provide insights on the impact of different RIS settings on the proposed algorithm.

cs.IT↗

arXiv2019

Model-Driven Channel Estimation for OFDM Systems Based on Image Super-Resolution Network

In this paper, we propose a model-driven channel estimation method utilizing a convolutional neural network (CNN) derived from image super-resolution (SR). Instead of completely abandoning traditional communication modules as data-driven approaches always do, we first obtain rough channel matrix by the simplest least square (LS) channel estimation, and then use a CNN-based SR network to learn the subtle connections within channel matrix for accuracy improvement. The results show that our proposed method significantly outperforms the linear minimum mean squared error (LMMSE) estimation and has potential in spectrum saving. The impacts of network structure and SNR mismatch problem are also investigated in this paper.

eess.SP↗

arXiv2019

Low-temperature electroluminescence excitation mapping of excitons and trions in short channel monochiral carbon nanotube device

Single walled carbon nanotubes as emerging quantum-light sources may fill a technological gap in silicon photonics due to their potential use as near infrared, electrically driven, classical or non-classical emitters. Unlike in photoluminescence, where nanotubes are excited with light, electrical excitation of single tubes is challenging and heavily influenced by device fabrication, architecture and biasing conditions. Here we present electroluminescence spectroscopy data of ultra short channel devices made from (9,8) carbon nanotubes emitting in the telecom band. Emissions are stable under current biasing and no quenching is observed down to 10 nm gap size. Low-temperature electroluminescence spectroscopy data also reported exhibits cold emission and linewidths down to 2 meV at 4 K. Electroluminescence excitation maps give evidence that carrier recombination is the mechanism for light generation in short channels. Excitonic and trionic emissions can be switched on and off by gate voltage and corresponding emission efficiency maps were compiled. Insights are gained into the influence of acoustic phonons on the linewidth, absence of intensity saturation and exciton exciton annihilation, environmental effects like dielectric screening and strain on the emission wavelength, and conditions to suppress hysteresis and establish optimum operation conditions.

physics.app-ph↗

arXiv2019

Automorphism groups of smooth cubic threefolds

In this paper, we classify groups which faithfully act on smooth cubic threefolds. It turns out that there are exactly $6$ maximal ones and we describe them with explicit examples of target cubic threefolds.

math.AG↗

arXiv2019

Fairness in Recommendation Ranking through Pairwise Comparisons

Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information. As such it is important to ask: what are the possible fairness risks, how can we quantify them, and how should we address them? In this paper we offer a set of novel metrics for evaluating algorithmic fairness concerns in recommender systems. In particular we show how measuring fairness based on pairwise comparisons from randomized experiments provides a tractable means to reason about fairness in rankings from recommender systems. Building on this metric, we offer a new regularizer to encourage improving this metric during model training and thus improve fairness in the resulting rankings. We apply this pairwise regularization to a large-scale, production recommender system and show that we are able to significantly improve the system's pairwise fairness.

cs.CY↗

arXiv2018

ScaleSimulator: A Fast and Cycle-Accurate Parallel Simulator for Architectural Exploration

Design of next generation computer systems should be supported by simulation infrastructure that must achieve a few contradictory goals such as fast execution time, high accuracy, and enough flexibility to allow comparison between large numbers of possible design points. Most existing architecture level simulators are designed to be flexible and to execute the code in parallel for greater efficiency, but at the cost of scarified accuracy. This paper presents the ScaleSimulator simulation environment, which is based on a new design methodology whose goal is to achieve near cycle accuracy while still being flexible enough to simulate many different future system architectures and efficient enough to run meaningful workloads. We achieve these goals by making the parallelism a first-class citizen in our methodology. Thus, this paper focuses mainly on the ScaleSimulator design points that enable better parallel execution while maintaining the scalability and cycle accuracy of a simulated architecture. The paper indicates that the new proposed ScaleSimulator tool can (1) efficiently parallelize the execution of a cycle-accurate architecture simulator, (2) efficiently simulate complex architectures (e.g., out-of-order CPU pipeline, cache coherency protocol, and network) and massive parallel systems, and (3) use meaningful workloads, such as full simulation of OLTP benchmarks, to examine future architectural choices.

cs.DC↗

arXiv2017

Closed-form, robust and accurate multi-frequency phase unwrapping: frequency design and algorithm

A closed-form algorithm, named "concerto", is proposed for phase-based distance estimation in multi-frequency phase unwrapping (MFPU) system. The concerto method consists of three coherent estimation stages,i.e., initial modified BW estimation, residual error estimation and LS estimation , each of which has a closed-form expression and cooperates closely with each other like a concerto. Due to a specially designed frequency pattern, concerto is reliable, accurate, and computationally simple. Meanwhile, measurement frequency selection is an easier task. Performance comparisons with beat wavelength (BW), excess fractions (EF) and Chinese remainder theorem (CRT) method confirm that our method outperforms these methods both in accuracy and reliability and can asymptotically achieve the Cramer-Rao bound (CRB).

cs.IT↗

arXiv2017

On the Unambiguous Distance of Multi-Carrier Phase Ranging with Random Hopped Frequencie

In a multi-carrier phase ranging (MPR) system, the distance that radio signal travels is estimated through phase shift of multiple carrier frequencies. Due to phase ambiguity, a unique estimation can only be obtained within the unambiguous distance (UD), which depends on the carrier frequencies used for ranging. Without external information, the maximum measurable distance of an MPR system is defined by its UD. The MPR system employing random frequency hopping (FH) waveform has a strong anti-jamming capability and sees promising potentials in many fields. However, it is challenging to depict its measurable distance. Different from current MPR system employing deterministic frequencies, the carrier frequency under the FH waveform hops randomly within the occupied bandwidth. Consequently, the UD is a random variable. In this paper, we try to find a deterministic value to depict the UD of an MPR system under the random spaced frequencies (RSF) configuration, serving as a metric of its measurable distance in engineering applications. It is safe to adopt the lower bound of the random UD as the metric, but the measurable distance may be drastically underestimated. Alternatively, we propose to adopt the upper bound of the random UD as the metric, because when the RSF set contains more than a dozen of carriers, i) we prove the probability that the random UD obtains its upper bound is very close to 1 if phase noise is not introduced; ii) simulations show that the upper bound can also be obtained reliably in the presence of phase noise. As a comparison, the practical measurable range of the MPR system under the traditional linearly spaced frequencies (LSF) configuration is only a fraction of its theoretical UD because the UD is more sensitive to phase error.

cs.NI↗

arXiv2017

Multipath Error Correction in Radio Interferometric Positioning Systems

The radio interferometric positioning system (RIPS) is an accurate node localization method featuring a novel phase-based ranging process. Multipath is the limiting error source for RIPS in ground-deployed scenarios or indoor applications. There are four distinct channels involved in the ranging process for RIPS. Multipath reflections affect both the phase and amplitude of the ranging signal for each channel. By exploiting untapped amplitude information, we put forward a scheme to estimate each channel's multipath profile, which is then subsequently used to correct corresponding errors in phase measurements. Simulations show that such a scheme is very effective in reducing multipath phase errors, which are essentially brought down to the level of receiver noise under moderate multipath conditions. It is further demonstrated that ranging errors in RIPS are also greatly reduced via the proposed scheme.

cs.IT↗

arXiv2016

Full Duplex Joint Sensing for Opportunistic Access in Spectrum-Heterogeneous Cognitive Radio Networks

In cognitive radio networks (CRN), secondary users (SUs) can share spectrum with licensed primary users (PUs). Because an SU receiver (SU-Rx) does not always share the same view of spectrum availability as the corresponding SU transmitter (SU-Tx), spectrum sensing conducted only by an SU transmitter tends to be overly sensitive to guarantee safe spectrum access at the price of SU inefficiency. In this letter, we propose a joint spectrum sensing mechanism, named Full-Duplex Joint Sensing (FJDS), to relax sensitivity of SU detection and improve SU throughput. FDJS employs instantaneous feedback enabled by in-band full duplex communication to facilitate the sharing of spectral information from SU-Rx to SU-Tx. The joint detection problem in FDJS is modeled as non-linear optimization and solved by a binary searching algorithm. Simulation results show that FDJS could improve SU throughput as well as meeting PU interruption constraints in a wide range of parameter settings.

cs.NI↗