arXiv subjects

Ning Wang

Publications and source records attributed to Ning Wang.

At least 253 records · Page 14Linked to original sources

arXiv2022

Triple-Band Scheduling with Millimeter Wave and Terahertz Bands for Wireless Backhaul

With the explosive growth of mobile traffic demand, densely deployed small cells underlying macrocells have great potential for 5G and beyond wireless networks. In this paper, we consider the problem of supporting traffic flows with diverse QoS requirements by exploiting three high frequency bands, i.e., the 28GHz band, the E-band, and the Terahertz (THz) band. The cooperation of the three bands is helpful for maximizing the number of flows with their QoS requirements satisfied. To solve the formulated nonlinear integer programming problem, we propose a triple-band scheduling scheme which can select the optimum scheduling band for each flow among three different frequency bands. The proposed scheme also efficiently utilizes the resource to schedule flow transmissions in time slots. Extensive simulations demonstrate the superior performance of the proposed scheme over three baseline schemes with respect to the number of completed flows and the system throughput.

cs.IT↗

arXiv2022

Observation of Γ-valley moiré bands and emergent hexagonal lattice in twisted transition metal dichalcogenides

Twisted van der Waals heterostructures have recently been proposed as a condensed-matter platform for realizing controllable quantum models due to the low-energy moiré bands with specific charge distributions in moiré superlattices. Here, combining angle-resolved photoemission spectroscopy with sub-micron spatial resolution (μ-ARPES) and scanning tunneling microscopy (STM), we performed a systematic investigation on the electronic structure of 5.1° twisted bilayer WSe2 that hosts correlated insulating and zero-resistance states. Interestingly, contrary to one's expectation, moiré bands were observed only at Γ-valley but not K-valley in μ-ARPES measurements; and correspondingly, our STM measurements clearly identified the real-space honeycomb- and Kagome-shaped charge distributions at the moiré length scale associated with the Γ-valley moiré bands. These results not only reveal the unsual valley dependent moiré-modified electronic structure in twisted transition metal dichalcogenides, but also highlight the Γ-valley moiré bands as a promising platform for exploring strongly correlated physics in emergent honeycomb and Kagome lattices at different energy scales.

cond-mat.str-el↗

arXiv2022

Coverage Probability Analysis of RIS-Assisted High-Speed Train Communications

Reconfigurable intelligent surface (RIS) has received increasing attention due to its capability of extending cell coverage by reflecting signals toward receivers. This paper considers a RIS-assisted high-speed train (HST) communication system to improve the coverage probability. We derive the closed-form expression of coverage probability. Moreover, we analyze impacts of some key system parameters, including transmission power, signal-to-noise ratio threshold, and horizontal distance between base station and RIS. Simulation results verify the efficiency of RIS-assisted HST communications in terms of coverage probability.

cs.IT↗

arXiv2022

Deep Reinforcement Learning Coordinated Receiver Beamforming for Millimeter-Wave Train-ground Communications

As more and more people choose high-speed rail (HSR) as a means of transportation for short trips, there is ever growing demand of high quality of multimedia services. With its rich spectrum resources, millimeter wave (mm-wave) communications can satisfy the high network capacity requirements for HSR. Also, it is possible for receivers (RXs) to be equipped with antenna arrays in mm-wave communication systems due to its short wavelength. However, as HSRs run with high speed, the received signal power (RSP) varies rapidly over a cell and it is the lowest at the edge of the cell compared to other locations. Consequently, it is necessary to conduct research on RX beamforming for HSR in mm-wave band to improve the quality of the received signal. In this paper, we focus on RX beamforming for a mm-wave train-ground communication system. To improve the RSP, we propose an effective RX beamforming scheme based on deep reinforcement learning (DRL), and develop a deep Q-network (DQN) algorithm to train and determine the optimal RX beam direction with the purpose of maximizing average RSP. Through extensive simulations, we demonstrate that the proposed scheme has better performance than the four baseline schemes in terms of average RSP at most positions on the railway.

cs.IT↗

arXiv2022

Scheduling of UAV-assisted Millimeter Wave Communications for High-Speed Railway

To exploit richer spectrum resources for even better service quality, millimeter wave (mmWave) communication has been considered for high-speed railway (HSR) communication systems. In this paper, we focus on scheduling as many flows as possible while satisfying their QoS requirements. Due to interference, eavesdropping, or other problems, some flows may not be directly transmitted from the track-side BS. In this paper, we propose an UAV-assisted scheduling scheme which utilizes a UAV to serve as relay for such flows. The proposed scheme also utilize two mmWave bands, one for the BS links and the other for the UAV links. The proposed algorithm aims to maximize the number of flows with their QoS requirements satisfied. Simulations demonstrate that the proposed scheme achieves a superior performance on the number of completed flows and the system throughput over two baseline schemes.

cs.IT↗

arXiv2022

Mobility Support for Millimeter Wave Communications: Opportunities and Challenges

Millimeter-wave (mmWave) communication technology offers a potential and promising solution to support 5G and B5G wireless networks in dynamic scenarios and applications. However, mobility introduces many challenges as well as opportunities to mmWave applications. To address these problems, we conduct a survey of the opportunities and technologies to support mmWave communications in mobile scenarios. Firstly, we summarize the mobile scenarios where mmWave communications are exploited, including indoor wireless local area network (WLAN) or wireless personal area network (WPAN), cellular access, vehicle-to-everything (V2X), high speed train (HST), unmanned aerial vehicle (UAV), and the new space-air-ground-sea communication scenarios. Then, to address users' mobility impact on the system performance in different application scenarios, we introduce several representative mobility models in mmWave systems, including human mobility, vehicular mobility, high speed train mobility and ship mobility. Next we survey the key challenges and existing solutions to mmWave applications, such as channel modeling, channel estimation, anti-blockage, and capacity improvement. Lastly, we discuss the open issues concerning mobility-aware mmWave communications that deserve further investigation. In particular, we highlight future heterogeneous mobile networks, dynamic resource management, artificial intelligence (AI) for mobility and integration of geographical information, deployment of large intelligent surface and reconfigurable antenna technology, and finally, the evolution to Terahertz (THz) communications.

cs.IT↗

arXiv2022

Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction

In the real world, long sequence time-series forecasting (LSTF) is needed in many cases, such as power consumption prediction and air quality prediction.Multi-dimensional long time series model has more strict requirements on the model, which not only needs to effectively capture the accurate long-term dependence between input and output, but also needs to capture the relationship between data of different dimensions.Recent research shows that the Informer model based on Transformer has achieved excellent performance in long time series prediction.However, this model still has some deficiencies in multidimensional prediction,it cannot capture the relationship between different dimensions well. We improved Informer to address its shortcomings in multidimensional forecasting. First,we introduce an adaptive graph neural network to capture hidden dimension dependencies in mostly time series prediction. Secondly,we integrate adaptive graph convolutional networks into various spatio-temporal series prediction models to solve the defect that they cannot capture the relationship between different dimensions. Thirdly,After experimental testing with multiple data sets, the accuracy of our framework improved by about 10\% after being introduced into the model.

cs.LG↗

arXiv2022

M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation

This paper deals with the utterance-level modalities missing problem with uncertain patterns on emotion recognition in conversation (ERC) task. Present models generally predict the speaker's emotions by its current utterance and context, which is degraded by modality missing considerably. Our work proposes a framework Missing-Modality Robust emotion Recognition (M2R2), which trains emotion recognition model with iterative data augmentation by learned common representation. Firstly, a network called Party Attentive Network (PANet) is designed to classify emotions, which tracks all the speakers' states and context. Attention mechanism between speaker with other participants and dialogue topic is used to decentralize dependence on multi-time and multi-party utterances instead of the possible incomplete one. Moreover, the Common Representation Learning (CRL) problem is defined for modality-missing problem. Data imputation methods improved by the adversarial strategy are used here to construct extra features to augment data. Extensive experiments and case studies validate the effectiveness of our methods over baselines for modality-missing emotion recognition on two different datasets.

cs.SD↗

arXiv2022

Cooperative Reflection and Synchronization Design for Distributed Multiple-RIS Communications

To reap the promised gain achieved by distributed reconfigurable intelligent surfaces (RISs)-enhanced communications in a wireless network, timing synchronization among these metasurfaces is an essential prerequisite in practice. This paper proposes a unified framework for the joint estimation of the unknown timing offsets and the RIS channel parameters, as well as the design of cooperative reflection and synchronization algorithm for the distributed multiple-RIS communication. Considering that RIS is usually a passive device with limited capability of signal processing, the individual timing offset and channel gains of each hop of the RIS links cannot be directly estimated. To make the estimation tractable, we propose to estimate the cascaded channels and timing offsets jointly by deriving a maximum likelihood estimator. Furthermore, we theoretically characterize the Cramer-Rao lower bound (CRLB) to evaluate the accuracy of this estimator. By using the proposed estimator and the derived CRLBs, an efficient resynchronization algorithm is devised jointly at the RISs and the destination to compensate the multiple timing offsets. Based on the majorization-minimization framework, the proposed algorithm admits semi-closed and closed form solutions for the RIS reflection matrices and the timing offset equalizer, respectively. Simulation results verify that our theoretical analysis well matches the numerical tests and validate the effectiveness of the proposed resynchronization algorithm.

cs.IT↗

arXiv2022

Transport Model Comparison Studies of Intermediate-Energy Heavy-Ion Collisions

Transport models are the main method to obtain physics information from low to relativistic-energy heavy-ion collisions. The Transport Model Evaluation Project (TMEP) has been pursued to test the robustness of transport model predictions in reaching consistent conclusions from the same type of physical model. Calculations under controlled conditions of physical input and set-up were performed with various participating codes. These included both calculations of nuclear matter in a box with periodic boundary conditions, and more realistic calculations of heavy-ion collisions. In this intermediate review, we summarize and discuss the present status of the project. We also provide condensed descriptions of the 26 participating codes, which contributed to some part of the project. These include the major codes in use today. We review the main results of the studies completed so far. They show, that in box calculations the differences between the codes can be well understood and a convergence of the results can be reached. These studies also highlight the systematic differences between the two families of transport codes, known as BUU and QMD type codes. However, when the codes were compared in full heavy-ion collisions using different physical models, as recently for pion production, they still yielded substantially different results. This calls for further comparisons of heavy-ion collisions with controlled models and of box comparisons of important ingredients, like momentum-dependent fields, which are currently underway. We often indicate improved strategies in performing transport simulations and thus provide guidance to code developers. Results of transport simulations of heavy-ion collisions from a given code will have more significance if the code can be validated against benchmark calculations such as the ones summarized in this review.

nucl-th↗

arXiv2022

Large-Scale Multi-Document Summarization with Information Extraction and Compression

We develop an abstractive summarization framework independent of labeled data for multiple heterogeneous documents. Unlike existing multi-document summarization methods, our framework processes documents telling different stories instead of documents on the same topic. We also enhance an existing sentence fusion method with a uni-directional language model to prioritize fused sentences with higher sentence probability with the goal of increasing readability. Lastly, we construct a total of twelve dataset variations based on CNN/Daily Mail and the NewsRoom datasets, where each document group contains a large and diverse collection of documents to evaluate the performance of our model in comparison with other baseline systems. Our experiments demonstrate that our framework outperforms current state-of-the-art methods in this more generic setting.

cs.CL↗

arXiv2022

NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the super-resolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https://github.com/RenYang-home/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://github.com/RenYang-home/NTIRE22_VEnh_SR.

cs.CV↗

arXiv2022

Distributed Neural Precoding for Hybrid mmWave MIMO Communications with Limited Feedback

Hybrid precoding is a cost-efficient technique for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communications. This paper proposes a deep learning approach by using a distributed neural network for hybrid analog-and-digital precoding design with limited feedback. The proposed distributed neural precoding network, called DNet, is committed to achieving two objectives. First, the DNet realizes channel state information (CSI) compression with a distributed architecture of neural networks, which enables practical deployment on multiple users. Specifically, this neural network is composed of multiple independent sub-networks with the same structure and parameters, which reduces both the number of training parameters and network complexity. Secondly, DNet learns the calculation of hybrid precoding from reconstructed CSI from limited feedback. Different from existing black-box neural network design, the DNet is specifically designed according to the data form of the matrix calculation of hybrid precoding. Simulation results show that the proposed DNet significantly improves the performance up to nearly 50% compared to traditional limited feedback precoding methods under the tests with various CSI compression ratios.

cs.IT↗

arXiv2022

Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach

The increased throughput brought by MIMO technology relies on the knowledge of channel state information (CSI) acquired in the base station (BS). To make the CSI feedback overhead affordable for the evolution of MIMO technology (e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced to deal with the CSI compression task. Based on the separation principle in existing communication systems, DL based CSI compression is used as source coding. However, this separate source-channel coding (SSCC) scheme is inferior to the joint source-channel coding (JSCC) scheme in the finite blocklength regime. In this paper, we propose a deep joint source-channel coding (DJSCC) based framework for the CSI feedback task. In particular, the proposed method can simultaneously learn from the CSI source and the wireless channel. Instead of truncating CSI via Fourier transform in the delay domain in existing methods, we apply non-linear transform networks to compress the CSI. Furthermore, we adopt an SNR adaption mechanism to deal with the wireless channel variations. The extensive experiments demonstrate the validity, adaptability, and generality of the proposed framework.

cs.IT↗

arXiv2022

Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification

The surrogate model-based uncertainty quantification method has drawn much attention in many engineering fields. Polynomial chaos expansion (PCE) and deep learning (DL) are powerful methods for building a surrogate model. However, PCE needs to increase the expansion order to improve the accuracy of the surrogate model, which causes more labeled data to solve the expansion coefficients, and DL also requires a lot of labeled data to train the deep neural network (DNN). First of all, this paper proposes the adaptive arbitrary polynomial chaos (aPC) and proves two properties about the adaptive expansion coefficients. Based on the adaptive aPC, a semi-supervised deep adaptive arbitrary polynomial chaos expansion (Deep aPCE) method is proposed to reduce the training data cost and improve the surrogate model accuracy. For one hand, the Deep aPCE method uses two properties of the adaptive aPC to assist in training the DNN based on only a small amount of labeled data and many unlabeled data, significantly reducing the training data cost. On the other hand, the Deep aPCE method adopts the DNN to fine-tune the adaptive expansion coefficients dynamically, improving the Deep aPCE model accuracy with lower expansion order. Besides, the Deep aPCE method can directly construct accurate surrogate models of the high dimensional stochastic systems without complex dimension-reduction and model decomposition operations. Five numerical examples and an actual engineering problem are used to verify the effectiveness of the Deep aPCE method.

cs.LG↗

arXiv2022

RIS-Assisted Quasi-Static Broad Coverage for Wideband mmWave Massive MIMO Systems

Reconfigurable intelligent surfaces (RISs) can establish favorable wireless environments to combat the severe attenuation and blockages in millimeter-wave (mmWave) bands. However, to achieve the optimal enhancement of performance, the instantaneous channel state information (CSI) needs to be estimated at the cost of a large overhead that scales with the number of RIS elements and the number of users. In this paper, we design a quasi-static broad coverage at the RIS with the reduced overhead based on the statistical CSI. We propose a design framework to synthesize the power pattern reflected by the RIS that meets the customized requirements of broad coverage. For the communication of broadcast channels, we generalize the broad coverage of the single transmit stream to the scenario of multiple streams. Moreover, we employ the quasi-static broad coverage for a multiuser orthogonal frequency division multiplexing access (OFDMA) system, and derive the analytical expression of the downlink rate, which is proved to increase logarithmically with the power gain reflected by the RIS. By taking into account the overhead of channel estimation, the proposed quasi-static broad coverage even outperforms the design method that optimizes the RIS phases using the instantaneous CSI. Numerical simulations are conducted to verify these observations.

cs.IT↗

arXiv2022

Nodal and nematic superconducting phases in NbSe2 monolayers from competing superconducting channels

Transition metal dichalcogenides like 2H-NbSe2 in their two-dimensional (2D) form exhibit Ising superconductivity with the quasiparticle spins are firmly pinned in the direction perpendicular to the basal plane. This enables them to withstand exceptionally high magnetic fields beyond the Pauli limit for superconductivity. Using field-angle-resolved magnetoresistance experiments for fields rotated in the basal plane we investigate the field-angle dependence of the upper critical field (Hc2), which directly reflects the symmetry of the superconducting order parameter. We observe a six-fold nodal symmetry superposed on a two-fold symmetry. This agrees with theoretical predictions of a nodal topological superconducting phase near Hc2, together with a nematic superconducting state. We demonstrate that in NbSe2 such unconventional superconducting states can arise from the presence of several competing superconducting channels.

cond-mat.supr-con↗

arXiv2022

The bipartite Turan number and spectral extremum for linear forests

The bipartite Turán number of a graph $H$, denoted by $ex(m,n; H)$, is the maximum number of edges in any bipartite graph $G=(X,Y; E)$ with $|X|=m$ and $|Y|=n$ which does not contain $H$ as a subgraph. In this paper, we determined $ex(m,n; F_{\ell})$ for arbitrary $\ell$ and appropriately large $n$ with comparing to $m$ and $\ell$, where $F_\ell$ is a linear forest which consists of $\ell$ vertex disjoint paths. Moreover, the extremal graphs have been characterized. Furthermore, these results are used to obtain the maximum spectral radius of bipartite graphs which does not contain $F_{\ell}$ as a subgraph and characterize all extremal graphs which attain the maximum spectral radius.

math.CO↗