Huawei

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..

Method for transmitting control information, user equipment and base station

Abstract: Embodiments of the present application provide a method for transmitting control information, a user equipment and a base station. The method includes: acquiring a resource index of a physical uplink control channel PUCCH; acquiring a sequence index of an orthogonal sequence of the PUCCH according to the resource index, and acquiring the orthogonal sequence according to the sequence index; acquiring a cyclic shift of a reference signal of the PUCCH according to the sequence index; and transmitting the UCI to the base station on the PUCCH according to the orthogonal sequence and the cyclic shift. In the embodiments of the present application, a cyclic shift of a reference signal of a PUCCH channel for transmitting UCI is acquired according to a sequence index, and the UCI is transmitted on the PUCCH according to the cyclic shift and a corresponding orthogonal sequence, which can enhance transmission performance of the UCI.

Modeling and Analysis of Series-None Compensation for Wireless Power Transfer Systems With a Strong Coupling

Abstract: In some applications of wireless power transfer, such as wireless charging of consumer electronics and electric buses, the coupling between the transmitter and the receiver is strong in order to improve efficiency. With a strong coupling, the compensation on the receiver side can be eliminated to achieve a high-level integration of the receiver device and a smaller receiver-side loss, forming a series-none (SN) topology, which has not been fully investigated before. The commonly used first harmonic approximation method for the SN topology has discrepancies with a low-power output and cannot fully reveal the characteristics of the SN topology. Therefore, this paper establishes a model of the SN topology and derives the analytical expressions of the transmitter and receiver currents based on differential equations. The distortion of the receiver current is studied. The coil-to-coil efficiency is obtained and its maximum condition is investigated. The SN topology offers approximately the same efficiency as the SS topology when the coupling is strong and the coil quality factors are large. Theoretical calculations and experimental results confirm the analysis.

VENUS: Vertex-centric streamlined graph computation on a single PC

Abstract: Recent studies show that disk-based graph computation on just a single PC can be as highly competitive as cluster-based computing systems on large-scale problems. Inspired by this remarkable progress, we develop VENUS, a disk-based graph computation system which is able to handle billion-scale problems efficiently on a commodity PC. VENUS adopts a novel computing architecture that features vertex-centric “streamlined” processing - the graph is sequentially loaded and the update functions are executed in parallel on the fly. VENUS deliberately avoids loading batch edge data by separating read-only structure data from mutable vertex data on disk. Furthermore, it minimizes random IOs by caching vertex data in main memory. The streamlined processing is realized with efficient sequential scan over massive structure data and fast feeding a large number of update functions. Extensive evaluation on large real-world and synthetic graphs has demonstrated the efficiency of VENUS. For example, VENUS takes just 8 minutes with hard disk for PageRank on the Twitter graph with 1.5 billion edges. In contrast, Spark takes 8.1 minutes with 50 machines and 100 CPUs, and GraphChi takes 13 minutes using fast SSD drive.

CRF-CNN: Modeling Structured Information in Human Pose Estimation

Abstract: Deep convolutional neural networks (CNN) have achieved great success. On the other hand, modeling structural information has been proved critical in many vision problems. It is of great interest to integrate them effectively. In a classical neural network, there is no message passing between neurons in the same layer. In this paper, we propose a CRF-CNN framework which can simultaneously model structural information in both output and hidden feature layers in a probabilistic way, and it is applied to human pose estimation. A message passing scheme is proposed, so that in various layers each body joint receives messages from all the others in an efficient way. Such message passing can be implemented with convolution between features maps in the same layer, and it is also integrated with feedforward propagation in neural networks. Finally, a neural network implementation of end-to-end learning CRF-CNN is provided. Its effectiveness is demonstrated through experiments on two benchmark datasets.

Spatially Correlated Content Caching for Device-to-Device Communications

Abstract: We study optimal geographic content placement for device-to-device ( ${\mathsf {D2D}}$ ) networks in which each file’s popularity follows the Zipf distribution. The locations of the ${\mathsf {D2D}}$ users (caches) are modeled by a Poisson point process and have limited communication range and finite storage. Inspired by the Matern hard-core (type II) point process that captures pairwise interactions between nodes, we devise a novel spatially correlated caching strategy called hard-core placement ( ${\mathsf {HCP}}$ ) such that the ${\mathsf {D2D}}$ nodes caching the same file are never closer to each other than the exclusion radius . The exclusion radius plays the role of a substitute for caching probability. We derive and optimize the exclusion radii to maximize the hit probability , which is the probability that a given ${\mathsf {D2D}}$ node can find a desired file at another node’s cache within its communication range. Contrasting it with independent content placement, which is used in most prior work, our ${\mathsf {HCP}}$ strategy often yields a significantly higher cache hit probability. We further demonstrate that the ${\mathsf {HCP}}$ strategy is effective for small cache sizes and a small communication radius, which are likely conditions for ${\mathsf {D2D}}$ .