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..

Exploiting Cross-Sentence Context for Neural Machine Translation

Abstract: In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.

5G: The Convergence of Wireless Communications

Abstract: As the rollout of 4G mobile communication networks takes place, representatives of industry and academia have started to look into the technological developments toward the next generation (5G). Several research projects involving key international mobile network operators, infrastructure manufacturers, and academic institutions, have been launched recently to set the technological foundations of 5G. However, the architecture of future 5G systems, their performance, and mobile services to be provided have not been clearly defined. In this paper, we put forth the vision for 5G as the convergence of evolved versions of current cellular networks with other complementary radio access technologies. Therefore, 5G may not be a single radio access interface but rather a "network of networks". Evidently, the seamless integration of a variety of air interfaces, protocols, and frequency bands, requires paradigm shifts in the way networks cooperate and complement each other to deliver data rates of several Gigabits per second with end-to-end latency of a few milliseconds. We provide an overview of the key radio technologies that will play a key role in the realization of this vision for the next generation of mobile communication networks. We also introduce some of the research challenges that need to be addressed.

Dynamic Radio Resource Slicing for a Two-Tier Heterogeneous Wireless Network

Abstract: In this paper, a dynamic radio resource slicing framework is presented for a two-tier heterogeneous wireless network Through software-defined networking-enabled wireless network function virtualization, radio spectrum resources of heterogeneous wireless networks are partitioned into different bandwidth slices for different base stations (BSs) This framework facilitates spectrum sharing among heterogeneous BSs and achieves differentiated quality-of-service (QoS) provisioning for data service and machine-to-machine service in the presence of network load dynamics To determine the set of optimal bandwidth slicing ratios and optimal BS-device association patterns, a network utility maximization problem is formulated with the consideration of different traffic statistics and QoS requirements, location distribution for end devices, varying device locations, load conditions in each cell, and intercell interference For tractability, the optimization problem is transformed to a biconcave maximization problem An alternative concave search (ACS) algorithm is then designed to solve for a set of partial optimal solutions Simulation results verify the convergence property and display low complexity of the ACS algorithm It is demonstrated that the proposed radio resource slicing framework outperforms the two other resource slicing schemes in terms of low communication overhead, high spectrum utilization, and high aggregate network utility

Beyond 100-Gb/s Transmission Over 80-km SMF Using Direct-Detection SSB-DMT at C-Band

Abstract: For short-reach links, direct detection offers the advantages of low cost and low complexity. Discrete multitone (DMT) is a promising format due to its high spectral efficiency, flexibility and tolerance to chromatic dispersion (CD). In this study, we experimentally demonstrate a beyond 100-Gb/s DMT transmission over 80-km single mode fiber (SMF) without CD compensation. Using dual-drive Mach–Zehnder modulator-assisted single-sideband modulation, CD-induced power fading is eliminated after direct detection. Trellis coder modulation (TCM) is used to increase the Euclidean distance of the constellation points and nonlinearity equalization (NLE) is employed to mitigate system nonlinearities. Both TCM and NLE algorithms have contributions to improve the system performance. The experimental results show that high capacities up to 122, 110 and 105 Gb/s are achieved with bit error rate at 4.5 × 10−3 for back to back, 40- and 80-km SMF transmissions, respectively. The required OSNR after 80-km SMF transmission is 34.2 dB. To the best of our knowledge, this study reports the lowest required OSNR and highest capacity for C-band direct-detection transmission over 80-km SMF.

A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs

Abstract: In this paper, a QoS-aware traffic classification framework for software defined networks is proposed. Instead of identifying specific applications in most of the previous work of traffic classification, our approach classifies the network traffic into different classes according to the QoS requirements, which provide the crucial information to enable the fine-grained and QoS-aware traffic engineering. The proposed framework is fully located in the network controller so that the real-time, adaptive, and accurate traffic classification can be realized by exploiting the superior computation capacity, the global visibility, andthe inherent programmability of the network controller. More specifically, the proposed framework jointly exploits deep packet inspection (DPI) and semi-supervised machine learning so that accurate traffic classification can be realized, while requiring minimal communications between the network controller and the SDN switches. Based on the real Internet data set, the simulation results show the proposed classification framework can provide good performance in terms of classification accuracy and communication costs