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..

Board-Level Functional Fault Diagnosis Using Artificial Neural Networks, Support-Vector Machines, and Weighted-Majority Voting

Abstract: Increasing integration densities and high operating speeds lead to subtle manifestation of defects at the board level. Functional fault diagnosis is, therefore, necessary for board-level product qualification. However, ambiguous diagnosis results lead to long debug times and even wrong repair actions, which significantly increase repair cost and adversely impact yield. Advanced machine-learning (ML) techniques offer an unprecedented opportunity to increase the accuracy of board-level functional diagnosis and reduce high-volume manufacturing cost through successful repair. We propose a smart diagnosis method based on two ML classification models, namely, artificial neural networks (ANNs) and support-vector machines (SVMs) that can learn from repair history and accurately localize the root cause of a failure. Fine-grained fault syndromes extracted from failure logs and corresponding repair actions are used to train the classification models. We also propose a decision machine based on weighted-majority voting, which combines the benefits of ANNs and SVMs. Three complex boards from the industry, currently in volume production, and additional synthetic data, are used to validate the proposed methods in terms of diagnostic accuracy, resolution, and quantifiable improvement over current diagnostic software.

Systems and methods for ofdm with flexible sub-carrier spacing and symbol duration

Abstract: Embodiments are provided for supporting variable sub-carrier spacing and symbol duration for transmitting OFDM or other waveform symbols and associated cyclic prefixes. The symbol duration includes the useful symbol length and its associated cyclic prefix length. The variable sub-carrier spacing and symbol duration is determined via parameters indicating the sub-carrier spacing, useful symbol length, and cyclic prefix length. An embodiment method, by a network or a network controller, includes establishing a plurality of multiple access block (MAB) types defining different combinations of sub-carrier spacing and symbol duration for waveform transmissions. The method further includes partitioning a frequency and time plane of a carrier spectrum band into a plurality of MAB regions comprising frequency-time slots for the waveform transmissions. The MAB types are then selected for the MAB regions, wherein one MAB type is assigned to one corresponding MAB region.

A Collective Neurodynamic Approach to Constrained Global Optimization

Abstract: Global optimization is a long-lasting research topic in the field of optimization, posting many challenging theoretic and computational issues. This paper presents a novel collective neurodynamic method for solving constrained global optimization problems. At first, a one-layer recurrent neural network (RNN) is presented for searching the Karush–Kuhn–Tucker points of the optimization problem under study. Next, a collective neuroydnamic optimization approach is developed by emulating the paradigm of brainstorming. Multiple RNNs are exploited cooperatively to search for the global optimal solutions in a framework of particle swarm optimization. Each RNN carries out a precise local search and converges to a candidate solution according to its own neurodynamics. The neuronal state of each neural network is repetitively reset by exchanging historical information of each individual network and the entire group. Wavelet mutation is performed to avoid prematurity, add diversity, and promote global convergence. It is proved in the framework of stochastic optimization that the proposed collective neurodynamic approach is capable of computing the global optimal solutions with probability one provided that a sufficiently large number of neural networks are utilized. The essence of the collective neurodynamic optimization approach lies in its potential to solve constrained global optimization problems in real time. The effectiveness and characteristics of the proposed approach are illustrated by using benchmark optimization problems.

System and charging control method of network convergence policy and charging control architecture

Abstract: A system and charging control method of network convergence policy and charging control architecture are disclosed, based on the QoS parameter, charging policy and user subscribing information granted by the service layer, the PCRF confirms PCC rule, and provides the PCC rule to the policy distribution function PDF. As the interface between the PCRF and PCC policy performing entity, the PDF performs the protocol conversion and information distribution. Based on the PCC rule, the PCC policy performing entity performs QoS policy of the service data flow and the detecting and charging of the service flow.