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..

Joint User Association and Spectrum Allocation for Small Cell Networks With Wireless Backhauls

Abstract: Heterogenous networks and massive MIMO can significantly enhance spectral and energy efficiencies of wireless networks and have recently attracted lots of attention. In this letter, we investigate joint user association and spectrum allocation to further improve spectral efficiency for massive MIMO enabled heterogeneous networks with full-duplex wireless backhauls. The optimization problem is formulated as a mixed-integer nonlinear programming problem, and therefore, is nonconvex and combinational. To solve the problem efficiently, the original optimization problem is divided into two subproblems that can be solved with low-computational-complexity methods. The simulation results confirm our theoretical analysis and demonstrate the performance improvement of the proposed algorithm.

An Energy-Efficient Nonvolatile In-Memory Computing Architecture for Extreme Learning Machine by Domain-Wall Nanowire Devices

Abstract: The data-oriented applications have introduced increased demands on memory capacity and bandwidth, which raises the need to rethink the architecture of the current computing platforms. The logic-in-memory architecture is highly promising as future logic-memory integration paradigm for high throughput data-driven applications. From memory technology aspect, as one recently introduced nonvolatile memory device, domain-wall nanowire (or race-track) not only shows potential as future power efficient memory, but also computing capacity by its unique physics of spintronics. This paper explores a novel distributed in-memory computing architecture where most logic functions are executed within the memory, which significantly alleviates the bandwidth congestion issue and improves the energy efficiency. The proposed distributed in-memory computing architecture is purely built by domain-wall nanowire, i.e., both memory and logic are implemented by domain-wall nanowire devices. As a case study, neural network-based image resolution enhancement algorithm, called DW-NN, is examined within the proposed architecture. We show that all operations involved in machine learning on neural network can be mapped to a logic-in-memory architecture by nonvolatile domain-wall nanowire. Domain-wall nanowire-based logic is customized for in machine learning within image data storage. As such, both neural network training and processing can be performed locally within the memory. The experimental results show that the domain-wall memory can reduce 92% leakage power and 16% dynamic power compared to main memory implemented by DRAM; and domain-wall logic can reduce 31% both dynamic and 65% leakage power under the similar performance compared to CMOS transistor-based logic. And system throughput in DW-NN is improved by 11.6x and the energy efficiency is improved by 56x when compared to conventional image processing system.

Coding method and coding device

Abstract: The present invention provides a coding method and a coding device. The coding method includes: coding information bits a to be coded via cyclic redundancy check CRC, then inputting the bits coded via the CRC into an interleaver determined by a construction parameter of a Polar code, where the interleaver is configured to interleave the bits coded via the CRC and output interleaved bits; and coding the output interleaved bits via the Polar code to obtain a coded Polar code. The above method is used to solve a problem in the prior art that minimum code distance of a Polar code is not large enough when the Polar code is relatively short or is of a medium length.

Multi-Cell Coordinated Scheduling and MIMO in LTE

Abstract: This paper provides an overview of multi-cell coordinated scheduling and multiple-input multiple-output (MIMO) techniques in LTE. We first discuss single-user MIMO (SU-MIMO) and multi-user MIMO (MU-MIMO) for LTE and identify potential techniques for further improving the performance of LTE, such as enhanced channel state information feedback and adaptive switching between SU-MIMO and MU-MIMO. Then, we address inter-cell interference mitigation techniques for downlink and uplink, respectively. Finally, potential research challenges and topics in the area are discussed.

An Improved Fuzzy Unequal Clustering Algorithm for Wireless Sensor Network

Abstract: This paper introduces IFUC, which is an Improved Fuzzy Unequal Clustering scheme for large scale wireless sensor networks (WSNs).It aims to balance the energy consumption and prolong the network lifetime. Our approach focuses on energy efficient clustering scheme and inter-cluster routing protocol. On the one hand, considering each node's local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine each node's chance of becoming cluster head and estimate the cluster head competence radius. On the other hand, we use Ant Colony Optimization (ACO) method to construct the energy-aware routing between cluster heads and base station. It reduces and balances the energy consumption of cluster heads and solves the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The validation experiment results have indicated that the proposed clustering scheme performs much better than many other methods such as LEACH, CHEF and EEUC.