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Institution

Huawei

CompanyShenzhen, China
About: Huawei is a company organization based out in Shenzhen, China. It is known for research contribution in the topics: Terminal (electronics) & Node (networking). The organization has 41417 authors who have published 44698 publications receiving 343496 citations. The organization is also known as: Huawei Technologies & Huawei Technologies Co., Ltd..


Papers
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TL;DR: It is demonstrated that polar code with 24-bit CRC decoded by the proposed adaptive SC-List decoder with very large maximum list size can achieve a frame error rate FER ≤ 10-3{-3} at Eb/No = 1.1dB, which is about 0.25dB from the information theoretic limit at this block length.
Abstract: In this letter, we propose an adaptive SC (Successive Cancellation)-List decoder for polar codes with CRC. This adaptive SC-List decoder iteratively increases the list size until the decoder outputs contain at least one survival path which can pass CRC. Simulation shows that the adaptive SC-List decoder provides significant complexity reduction. We also demonstrate that polar code (2048, 1024) with 24-bit CRC decoded by our proposed adaptive SC-List decoder with very large list size can achieve a frame error rate FER=0.001 at Eb/No=1.1dB, which is about 0.2dB from the information theoretic limit at this block length.

245 citations

Journal ArticleDOI
TL;DR: A design-oriented transient stability analysis of the grid-forming VSCs is presented, revealing that the PSC and the basic droop control can retain a stable operation as long as there are equilibrium points, due to their noninertial transient responses, while thedroop control with LPFs and the VSG control can be destabilized even if the equilibrium points exist.
Abstract: Driven by the large-scale integration of distributed power resources, grid-connected voltage-source converters (VSCs) are increasingly required to operate as grid-forming units to regulate the system voltage/frequency and emulate the inertia. While various grid-forming control schemes have been reported, their transient behaviors under large-signal disturbances are still not fully explored. This article addresses this issue by presenting a design-oriented transient stability analysis of the grid-forming VSCs. First, four typical grid-forming control schemes, namely, the power-synchronization control (PSC), the basic droop control, the droop control with low-pass filters (LPFs), and the virtual synchronous generator (VSG) control, are systematically reviewed, whose dynamics are characterized by a general large-signal model. Based on this model, a comparative analysis on the transient stabilities of different control schemes is then carried out. It reveals that the PSC and the basic droop control can retain a stable operation as long as there are equilibrium points, due to their noninertial transient responses, while the droop control with LPFs and the VSG control can be destabilized even if the equilibrium points exist, due to the lack of damping on their inertial transient responses. With the phase portrait, the underlying stability mechanism is explicitly elaborated, and the quantitative impacts of the controller gains and the virtual inertia are clearly identified. Subsequently, controller design guidelines are proposed to enhance the system damping as well as the transient stability. Finally, experimental results are provided to verify the theoretical analysis.

244 citations

Journal ArticleDOI
01 Aug 2018
TL;DR: The theoretical foundations of continuous‐variable quantum key distribution (CV‐QKD) with Gaussian modulation are reviewed and the essential relations from scratch in a pedagogical way and a set of new original noise models are presented to get an estimate of how well a given set of hardware will perform in practice.
Abstract: Quantum key distribution using weak coherent states and homodyne detection is a promising candidate for practical quantum-cryptographic implementations due to its compatibility with existing telecom equipment and high detection efficiencies. However, despite the actual simplicity of the protocol, the security analysis of this method is rather involved compared to discrete-variable QKD. In this article we review the theoretical foundations of continuous-variable quantum key distribution (CV-QKD) with Gaussian modulation and rederive the essential relations from scratch in a pedagogical way. The aim of this paper is to be as comprehensive and self-contained as possible in order to be well intelligible even for readers with little pre-knowledge on the subject. Although the present article is a theoretical discussion of CV-QKD, its focus lies on practical implementations, taking into account various kinds of hardware imperfections and suggesting practical methods to perform the security analysis subsequent to the key exchange. Apart from a review of well known results, this manuscript presents a set of new original noise models which are helpful to get an estimate of how well a given set of hardware will perform in practice.

244 citations

Proceedings ArticleDOI
02 Sep 2018
TL;DR: The proposed self-attentive speaker embedding system is compared with a strong DNN embedding baseline on NIST SRE 2016 and it is found that the self-ATTentive embeddings achieve superior performance.
Abstract: This paper introduces a new method to extract speaker embeddings from a deep neural network (DNN) for text-independent speaker verification. Usually, speaker embeddings are extracted from a speaker-classification DNN that averages the hidden vectors over the frames of a speaker; the hidden vectors produced from all the frames are assumed to be equally important. We relax this assumption and compute the speaker embedding as a weighted average of a speaker’s frame-level hidden vectors, and their weights are automatically determined by a self-attention mechanism. The effect of multiple attention heads are also investigated to capture different aspects of a speaker’s input speech. Finally, a PLDA classifier is used to compare pairs of embeddings. The proposed self-attentive speaker embedding system is compared with a strong DNN embedding baseline on NIST SRE 2016. We find that the self-attentive embeddings achieve superior performance. Moreover, the improvement produced by the self-attentive speaker embeddings is consistent with both short and long testing utterances.

243 citations

Proceedings ArticleDOI
Zili Yi1, Qiang Tang1, Shekoofeh Azizi1, Daesik Jang1, Zhan Xu1 
14 Jun 2020
TL;DR: A Contextual Residual Aggregation mechanism that can produce high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network.
Abstract: Recently data-driven image inpainting methods have made inspiring progress, impacting fundamental image editing tasks such as object removal and damaged image repairing. These methods are more effective than classic approaches, however, due to memory limitations they can only handle low-resolution inputs, typically smaller than 1K. Meanwhile, the resolution of photos captured with mobile devices increases up to 8K. Naive up-sampling of the low-resolution inpainted result can merely yield a large yet blurry result. Whereas, adding a high-frequency residual image onto the large blurry image can generate a sharp result, rich in details and textures. Motivated by this, we propose a Contextual Residual Aggregation (CRA) mechanism that can produce high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network. Since convolutional layers of the neural network only need to operate on low-resolution inputs and outputs, the cost of memory and computing power is thus well suppressed. Moreover, the need for high-resolution training datasets is alleviated. In our experiments, we train the proposed model on small images with resolutions 512 × 512 and perform inference on high-resolution images, achieving compelling inpainting quality. Our model can inpaint images as large as 8K with considerable hole sizes, which is intractable with previous learning-based approaches. We further elaborate on the light-weight design of the network architecture, achieving real-time performance on 2K images on a GTX 1080 Ti GPU. Codes are available at: https://github. com/Ascend-Huawei/Ascend-Canada/tree/ master/Models/Research_HiFIll_Model

240 citations


Authors

Showing all 41483 results

NameH-indexPapersCitations
Yu Huang136149289209
Xiaoou Tang13255394555
Xiaogang Wang12845273740
Shaobin Wang12687252463
Qiang Yang112111771540
Wei Lu111197361911
Xuemin Shen106122144959
Li Chen105173255996
Lajos Hanzo101204054380
Luca Benini101145347862
Lei Liu98204151163
Tao Wang97272055280
Mohamed-Slim Alouini96178862290
Qi Tian96103041010
Merouane Debbah9665241140
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202319
202266
20212,069
20203,277
20194,570
20184,476