Huawei

About: Huawei is a company organization based out in Shenzhen, China. It is known for research contribution in the topics: Terminal (electronics) & Signal. 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..

VehiCloud: Cloud Computing Facilitating Routing in Vehicular Networks

Abstract: Establishing reliable routing among highly mobile vehicles is a challenging problem in vehicular networks. Towards this issue, we present VehiCloud, a novel cloud computing architecture that leverages emerging cloud computing technologies to deal with unreliable inter-vehicle communications and extend the restricted computational capabilities of mobile devices. A way-point information framework (WIF) is devised within the VehiCloud architecture, aiming to provide routing service for vehicular network, where each vehicle serves as a mobile service node and predicts its future locations by generating way point messages, which describe the trajectory of the vehicle's movement. A decision module in VehiCloud collects vehicles' way points and makes routing decisions for inter-vehicle communication. Selected paths of the routing are globally optimized in terms of message delivery ratio by respecting the constraints of end-to-end delay and communication cost. Our implementation of VehiCloud and real-road experiments demonstrate that it is practical and efficient to address fundamental routing problems for vehicular networks.

A capacity-based codebook design method for sparse code multiple access systems

Abstract: Sparse code multiple access (SCMA) is a novel non-orthogonal multiple access scheme, which exploits a multidimensional constellation based on the non-orthogonal spreading technique. The SCMA multi-user codebook design is the bottleneck of system performance and there is no design guideline from the perspective of capacity. In this paper, a novel SCMA codebook design scheme is proposed to maximize the sum rate, where we transfer the design of multi-dimensional constellation sets to the optimizing of series of 1-dimensional complex codewords. Specifically, a basic M-order pulse-amplitude modulation (PAM) is optimized and then the angles of rotation between the input 1-dimensional constellation and the basic M-PAM constellation can be obtained within a feasible calculation complexity to improve the sum-rate. Finally, the series of 1-dimensional complex codewords are combined to construct multi-dimensional codebooks based on Latin square criterion. Numerical results illustrate that the proposed codebook outperforms the existing codebook by 1.3 dB over AWGN channel and 1.1 dB over Rayleigh channel in terms of bit error rate (BER) performance.

A device, system and method for automatically configuring application terminals in home network

Abstract: A device, a system, and a method for automatically configuring application terminals in a home network are disclosed. The method is as follows. A home gateway receives identifier information of an application terminal sent by the application terminal through a Dynamic Host Configuration Protocol (DHCP). The identifier information of the application terminal is sent to a public network through a DHCP message, so as to determine a DHCP server corresponding to an automatic configuration server (ACS) of the application terminal. Address information of the ACS of the application terminal is obtained from the DHCP server on the public network through a DHCP message. The address information is transmitted to the application terminal, so that the application terminal obtains configuration information from the ACS according to the address information to perform an auto-configuration. Thus, a problem that the application terminal in a home network cannot directly obtain an ACS Universal Resource Locator (URL) through a DHCP message is solved, so that an auto-configuration of the application terminal is realized.

Multi-Objective Optimization Based on Brain Storm Optimization Algorithm

Abstract: In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, the authors propose a new multi-objective brain storm optimization algorithm in which the clustering strategy is applied in the objective space instead of in the solution space in the original brain storm optimization algorithm for solving single objective optimization problems. Two versions of multi-objective brain storm optimization algorithm with different characteristics of diverging operation were tested to validate the usefulness and effectiveness of the proposed algorithm. Experimental results show that the proposed multi-objective brain storm optimization algorithm is a very promising algorithm, at least for solving these tested multi-objective optimization problems.