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..

(AF) 2 -S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network

Abstract: Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority. Semantic segmentation is one of the essential components of road scene perception that provides semantic information of the surrounding environment. Recently, several methods have been introduced for 3D LiDAR semantic segmentation. While they can lead to improved performance, they are either afflicted by high computational complexity, therefore are inefficient, or they lack fine details of smaller object instances. To alleviate these problems, we propose (AF)2-S3Net, an end-to-end encoder-decoder CNN network for 3D LiDAR semantic segmentation. We present a novel multibranch attentive feature fusion module in the encoder and a unique adaptive feature selection module with feature map re-weighting in the decoder. Our (AF)2-S3Net fuses the voxel-based learning and point-based learning methods into a unified framework to effectively process the potentially large 3D scene. Our experimental results show that the proposed method outperforms the state-of-the-art approaches on the large-scale nuScenes-lidarseg and SemanticKITTI benchmark, ranking 1st on both competitive public leaderboard competitions upon publication.

On Energy-Efficient Edge Caching in Heterogeneous Networks

Abstract: In this paper, we study the problem of content placement for caching at the wireless edge with the goal to maximize the energy efficiency (EE) of heterogeneous wireless networks. In particular, we consider the minimization of two fundamental metrics: the expected backhaul rate and the energy consumption. We derive both metrics in closed-form expressions, and we solve the minimization problem as a convex optimization for each, highlighting the existence of a tradeoff between the two metrics. Further, we show the advantage of encoding the data using maximum-distance separable (MDS) codes over the alternative concept of file fragmentation, with respect to both backhaul rate and energy consumption. Then, we thoroughly study the performance of the optimal MDS-encoded caching scheme in terms of overall energy consumption for an important heterogeneous network scenario. We compare our optimal strategy to several other sub-optimal caching strategies, including the caching scheme, minimizing the backhaul rate, and we analyze the effects of the system parameters on the overall performance. Our analysis can be generalized to any network topology and to any small-cell base station capability. Our results show that the optimal placement of MDS-encoded content in caches at the wireless edge increases significantly the overall EE of the heterogeneous network. This demonstrates the importance of the edge caching strategy for energy-efficient network designs.

Spectrum sharing improves the network efficiency for cellular operators

Abstract: The article describes the potential gain by spectrum sharing between cellular operators in terms of network efficiency. The focus of the study is on a specific resource sharing scenario: spectrum sharing between two operators in cellular downlink transmission. If frequency bands are allocated dynamically and exclusively to one operator - a case called orthogonal spectrum sharing - significant gains in terms of achievable throughput (spectrum sharing gains between 50 percent and 100 percent) and user satisfaction are reported for asymmetric scenarios at link and system level as well as from two hardware demonstrators. Additionally, if frequency bands are allocated simultaneously to two operators - a case called non-orthogonal spectrum sharing - further gains are reported. In order to achieve these, different enablers from hardware technologies and base station capabilities are required. However, we argue that all requirements are fulfilled in 3GPP and newer mobile standards. Therefore, the results and conclusions of this overview article encourage to seriously consider the inter-operator spectrum sharing technologies.

2 $\times$ 2-Slot Element for 60-GHz Planar Array Antenna Realized on Two Doubled-Sided PCBs Using SIW Cavity and EBG-Type Soft Surface fed by Microstrip-Ridge Gap Waveguide

Abstract: A wideband 2x2-slot element for a 60-GHz antenna array is designed by making use of two double-sided printed circuit boards (PCBs). The upper PCB contains the four radiating cavity-backed slots, where the cavity is formed in substrate-integrated waveguide (SIW) using metalized via holes. The SIW cavity is excited by a coupling slot. The excitation slot is fed by a microstrip-ridge gap waveguide formed in the air gap between the upper and lower PCBs. The lower PCB contains the microstrip line, being short-circuited to the ground plane of the lower PCB with via holes, and with additional metalized via holes alongside the microstrip line to form a stopband for parallel-platemodes in the air gap. The designed element can be used in large arrays with distribution networks realized in such microstrip-ridge gap waveguide technology. Therefore, the present paper describes a generic study in an infinite array environment, and performance is measured in terms of the active reflection coefficient S11 and the power lost in grating lobes. The study shows that the radiation characteristics of the array antenna is considerably improved by using a soft surface EBG-type SIW corrugation between each 2x2-slot element in E-plane to reduce the mutual coupling. The study is verified by measurements on a 4x4 element array surrounded by dummy elements and including a transition to rectangular waveguide WR15.

XingGAN for Person Image Generation

Abstract: We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i.e., translating the pose of a given person to a desired one. The proposed Xing generator consists of two generation branches that model the person’s appearance and shape information, respectively. Moreover, we propose two novel blocks to effectively transfer and update the person’s shape and appearance embeddings in a crossing way to mutually improve each other, which has not been considered by any other existing GAN-based image generation work. Extensive experiments on two challenging datasets, i.e., Market-1501 and DeepFashion, demonstrate that the proposed XingGAN advances the state-of-the-art performance both in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at https://github.com/Ha0Tang/XingGAN.