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..

PoC of SCMA-Based Uplink Grant-Free Transmission in UCNC for 5G

Abstract: A 5G mobile network has evolved from a cell centric radio access network to a user centric one. A user-centric no-cell (UCNC) framework has been proposed, which enables physical layer coordination over a large number of transmission and reception points for each mobile user to always experience cell-center-like communications. This paper presents some key technologies in the UCNC framework. The focus is on proof of concept of UL grant-free transmissions in the UCNC architecture. Massive connectivity supporting a huge number of devices is one of the three important scenarios in 5G networks. The challenges to support massive connectivity lie in the cost of signaling overhead and transmission latency. An uplink grant-free transmission based on sparse code multiple access (SCMA) design is proposed. The proposed scheme can provide different levels of overloading to efficiently meet the massive connectivity requirements. Grant-free transmission conducted in RRC connected state can reduce signaling overhead, while in energy conserved operation (ECO) state, it can significantly reduce the user plane transmission latency. Extensive laboratory testing has been conducted in a collaborative project of Huawei and Telefonica to verify the performance matrices. Taking LTE as a baseline for comparison purposes, it is shown that the signaling overhead by using uplink grant-free transmission can be reduced by around 80% and the user plane transmission latency in ECO state can be reduced by around 93%. Moreover, massive connectivity is efficiently supported with the SCMA scheme that yields around 230% gain over OFDMA in terms of supported active users.

Joint Demosaicing and Denoising With Self Guidance

Abstract: Usually located at the very early stages of the computational photography pipeline, demosaicing and denoising play important parts in the modern camera image processing. Recently, some neural networks have shown the effectiveness in joint demosaicing and denoising (JDD). Most of them first decompose a Bayer raw image into a four-channel RGGB image and then feed it into a neural network. This practice ignores the fact that the green channels are sampled at a double rate compared to the red and the blue channels. In this paper, we propose a self-guidance network (SGNet), where the green channels are initially estimated and then works as a guidance to recover all missing values in the input image. In addition, as regions of different frequencies suffer different levels of degradation in image restoration. We propose a density-map guidance to help the model deal with a wide range of frequencies. Our model outperforms state-of-the-art joint demosaicing and denoising methods on four public datasets, including two real and two synthetic data sets. Finally, we also verify that our method obtains best results in joint demosaicing , denoising and super-resolution.

Inter-cell interference coordination through adaptive soft frequency reuse in LTE networks

Abstract: In 3GPP Long Term Evolution (LTE) networks, the frequency reuse schemes such as fractional frequency reuse (FFR) and soft frequency reuse (SFR) are used to improve system capacity. The allocation of transmit power and subcarriers to each cell in these schemes are fixed prior to network deployment. This limits the potential performance of these frequency reuse schemes. In this paper, we propose to improve the capacity of SFR scheme by jointly optimizing subcarrier and power allocation in multi-cell LTE networks. An iterative algorithm that can adaptively vary the number of major subcarriers and adjust the transmit power for each cell according to wireless traffic loads is proposed. Simulation results show that the proposed algorithm outperforms the existing Reuse 1, FFR and static SFR schemes in both system throughput and cell edge user performance.

Situation Recognition with Graph Neural Networks

Abstract: We address the problem of recognizing situations in images. Given an image, the task is to predict the most salient verb (action), and fill its semantic roles such as who is performing the action, what is the source and target of the action, etc. Different verbs have different roles (e.g. attacking has weapon), and each role can take on many possible values (nouns). We propose a model based on Graph Neural Networks that allows us to efficiently capture joint dependencies between roles using neural networks defined on a graph. Experiments with different graph connectivities show that our approach that propagates information between roles significantly outperforms existing work, as well as multiple baselines. We obtain roughly 3-5% improvement over previous work in predicting the full situation. We also provide a thorough qualitative analysis of our model and influence of different roles in the verbs.

Impact of network slicing on 5G Radio Access Networks

Abstract: Network slicing addresses the deployment of multiple logical networks as independent business operations on a common physical infrastructure. The concept has initially been proposed for the 5th Generation (5G) core network (CN) however, it has not been investigated yet what network slicing would represent to the design of the 5G radio access network (RAN). The paper explains how network slicing may impact several aspects of the 5G RAN design such as the protocol architecture, the design of network functions (NFs) and the management framework that needs to support both the management of the infrastructure to be shared among the slices and the slice operation.