scispace - formally typeset
Search or ask a question
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
More filters
Patent
Wei Jiahong1, Li Jun1, Chen Wumao1
29 Oct 2007
TL;DR: In this paper, a method, a system and an authentication server for realizing a secure assignment of a DHCP address are disclosed, which includes: sending a DHCP Discovery message via an access network; obtaining the identification information of the DHCP client and performing an authenticating to the client based on identification information; and only assigning the address to the registry after the client has passed the authentication.
Abstract: A method, a system and an authentication server for realizing a secure assignment of a DHCP address are disclosed. The method includes: sending a DHCP Discovery message via an access network; obtaining the identification information of the DHCP client and performing an authenticating to the DHCP client based on the identification information; and only assigning the address to the DHCP client has passed the authentication. Therefore, in the present invention, access authentication may be performed on a subscriber according to location information, and IP address is only assigned to the valid subscriber and terminal. Therefore, the security of the address assignment in DHCP mode may be enhanced greatly. Moreover, in the present invention, addresses may be managed by an AAA server unitedly, or the addresses may be assigned after being authenticated by the AAA server successfully.

97 citations

Patent
Lu Rong1, Jianglei Ma, Peiying Zhu, Wen Tong, Au Kelvin Kar Kin 
08 Dec 2016
TL;DR: In this article, a per-service handover for a user equipment between hypercells is described, where the user equipment is transferred from a source cell to a target cell in respect of one of uplink and downlink communications.
Abstract: Systems and methods of performing handover for a user equipment between hyper cells are provided. Handover is done on a per service basis. In some cases, a handover of one service from a source cell to target cell is performed while continuing to use the source cell, the target cell, or another cell for another service. In some cases the handover for a user equipment is from a source cell to a target cell in respect of one of uplink and downlink communications, and the user equipment continues to use the source cell for the other of uplink and downlink communications.

97 citations

Journal ArticleDOI
Haozhe Wang1, Yulei Wu1, Geyong Min1, Jie Xu2, Pengcheng Tang2 
TL;DR: Deep Reinforcement Learning is leveraged to extract knowledge from experience by interacting with the network and enable dynamic adjustment of the resources allocated to various slices in order to maximise the resource utilisation while guaranteeing the Quality-of-Service (QoS).

97 citations

Journal ArticleDOI
TL;DR: In this paper, different non-data-aided (blind) CD estimation methods for single-carrier transmission under implementation constraint conditions such as bandwidth limitation and sampling rate are presented.
Abstract: Polarization-diverse coherent demodulation allows to compensate large values of accumulated linear distortion by digital signal processing. In particular, in uncompensated links without optical dispersion compensation, the parameter of the residual chromatic dispersion (CD) is vital to set the according digital filtering function. We present different non-data-aided (blind) CD estimation methods for single-carrier transmission under implementation constraint conditions such as bandwidth limitation and sampling rate. The estimation performance for various modulation formats is compared with respect to precision and robustness for a wide range of combined channel impairments.

97 citations

Posted Content
TL;DR: This work develops an efficient continuous evolutionary approach for searching neural networks that provides a series of networks with the number of parameters ranging from 3.7M to 5.1M under mobile settings and surpasses those produced by the state-of-the-art methods on the benchmark ImageNet dataset.
Abstract: Searching techniques in most of existing neural architecture search (NAS) algorithms are mainly dominated by differentiable methods for the efficiency reason. In contrast, we develop an efficient continuous evolutionary approach for searching neural networks. Architectures in the population that share parameters within one SuperNet in the latest generation will be tuned over the training dataset with a few epochs. The searching in the next evolution generation will directly inherit both the SuperNet and the population, which accelerates the optimal network generation. The non-dominated sorting strategy is further applied to preserve only results on the Pareto front for accurately updating the SuperNet. Several neural networks with different model sizes and performances will be produced after the continuous search with only 0.4 GPU days. As a result, our framework provides a series of networks with the number of parameters ranging from 3.7M to 5.1M under mobile settings. These networks surpass those produced by the state-of-the-art methods on the benchmark ImageNet dataset.

97 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
Network Information
Related Institutions (5)
Alcatel-Lucent
53.3K papers, 1.4M citations

90% related

Bell Labs
59.8K papers, 3.1M citations

88% related

Hewlett-Packard
59.8K papers, 1.4M citations

87% related

Microsoft
86.9K papers, 4.1M citations

87% related

Intel
68.8K papers, 1.6M citations

87% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202319
202266
20212,069
20203,277
20194,570
20184,476