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..

A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks

Abstract: Personalized recommender systems are playing an increasingly important role for online consumption platforms. Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences. Previous graph-based recommendation approaches process the observed user-item interaction graph as a ground-truth depiction of the relationships between users and items. However, especially in the implicit recommendation setting, all the unobserved user-item interactions are usually assumed to be negative samples. There are missing links that represent a user's future actions. In addition, there may be spurious or misleading positive interactions. To alleviate the above issue, in this work, we take a first step to introduce a principled way to model the uncertainty in the user-item interaction graph using the Bayesian Graph Convolutional Neural Network framework. We discuss how inference can be performed under our framework and provide a concrete formulation using the Bayesian Probabilistic Ranking training loss. We demonstrate the effectiveness of our proposed framework on four benchmark recommendation datasets. The proposed method outperforms state-of-the-art graph-based recommendation models. Furthermore, we conducted an offline evaluation on one industrial large-scale dataset. It shows that our proposed method outperforms the baselines, with the potential gain being more significant for cold-start users. This illustrates the potential practical benefit in real-world recommender systems.

Low Complexity Receiver for Uplink SCMA System via Expectation Propagation

Abstract: Sparse code multiple access (SCMA) scheme is considered to be one promising non-orthogonal multiple access technology for the future fifth generation (5G) communications. Due to the sparse nature, message passing algorithm (MPA) has been used at the receiver to achieve close to maximum likelihood (ML) detection performance with much lower complexity. However, the complexity order of MPA is still exponential with the size of codebook and the degree of signal superposition on a given resource element. In this paper, we propose a novel low complexity iterative receiver based on expectation propagation algorithm (EPA), which reduces the complexity order from exponential to linear. Simulation results demonstrate that the proposed EPA receiver achieves nearly the same block error rate (BLER) performance as the conventional message passing algorithm (MPA) receiver with orders less complexity.

WLAN communication device

Abstract: A technique for combining transmission bandwidths of several communication devices, such as mobile stations is disclosed. A master mobile station establishes a WLAN access point communicating with WLAN client terminals. One or more slave mobile stations may detect a predefined network identifier and join the WLAN network. The master assigns IP addresses for the client terminals and slave mobile stations. The master also resolves DNS queries in cooperation with external DNS servers. Traffic, including internet packets, between the client terminals and various internet hosts is tunneled over multiple simultaneous transmission paths between the master and a multiplexing/demultiplexing computer). The inventive bandwidth combination technique is transparent to the client terminals and the internet hosts.

Design Patterns and Extensibility of REST API for Networking Applications

Abstract: REST architectural style has become a prevalent choice for distributed resources, such as the northbound API of software-defined networking (SDN). As services often undergo frequent changes and updates, the corresponding REST APIs need to change and update accordingly. To allow REST APIs to change and evolve without breaking its clients, a REST API can be designed to facilitate hypertext-driven navigation and its related mechanisms to deal with structure changes in the API. This paper addresses the issues in hypertext-driven navigation in REST APIs from three aspects. First, we present REST Chart, a Petri-Net-based REST service description framework and language to design extensible REST APIs, and it is applied to cope with the rapid evolution of SDN northbound APIs. Second, we describe some important design patterns, such as backtracking and generator, within the REST Chart framework to navigate through large scale APIs in the RESTful architecture. Third, we present a client side differential cache mechanism to reduce the overhead of hypertext-driven navigation, addressing a major issue that affects the application of REST API. The proposed approach is applied to applications in SDN, which is integrated with a generalized SDN controller, SOX. The benefits of the proposed approach are verified in different conditions. Experimental results on SDN applications show that on average, the proposed cache mechanism reduces the overhead of using the hypertext-driven REST API by 66%, while fully maintaining the desired flexibility and extensibility of the REST API.