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..

Advanced modulation formats for 400-Gbps short-reach optical inter-connection

Abstract: Besides the long-haul optical networks covering over thousands of kilometers for backbone transmission, short reach optical networks (SR-ONs) are widely deployed in metro-area for aggregation and accessing. The SR-ONs include the metro optical transport networks (Metro-OTN), optical access networks or other optical inter-connection systems with even shorter distance. As predicted, the growing bandwidth demanding from SR-ONs will be much more than that from the long-haul optical networks in the near future. Besides, there are tremendous amounts of optical terminals and end-users in SR-ONs compared with the long-haul transmission systems and thus will induce large cost and huge energy consumption. So, the power and cost efficiency should be the key consideration for SR-ONs besides the transmission performance. To improve the power and cost efficiency in SR-ONs, advanced modulations and detection techniques based on low power, low cost and integrated optical modulators should be utilized. In this paper, different advanced modulation formats have been discussed. 56Gbps PAM4, 112Gbps poly-binary and 100Gbps DMT that can be used to realize 400-Gbps SR-ONs for different applications have also been demonstrated respectively. In addition, low-cost and low-power opto-electronic components suitable for SR-ONs, the impairments induced by all kinds of defects and bandwidth limitation of opto-electronic components and the corresponding compensation techniques based on DSP algorithms have also been discussed in the experiments.

Locally Linear Hashing for Extracting Non-linear Manifolds

Abstract: Previous efforts in hashing intend to preserve data variance or pairwise affinity, but neither is adequate in capturing the manifold structures hidden in most visual data. In this paper, we tackle this problem by reconstructing the locally linear structures of manifolds in the binary Hamming space, which can be learned by locality-sensitive sparse coding. We cast the problem as a joint minimization of reconstruction error and quantization loss, and show that, despite its NP-hardness, a local optimum can be obtained efficiently via alternative optimization. Our method distinguishes itself from existing methods in its remarkable ability to extract the nearest neighbors of the query from the same manifold, instead of from the ambient space. On extensive experiments on various image benchmarks, our results improve previous state-of-the-art by 28-74% typically, and 627% on the Yale face data.

MSCap: Multi-Style Image Captioning With Unpaired Stylized Text

Abstract: In this paper, we propose an adversarial learning network for the task of multi-style image captioning (MSCap) with a standard factual image caption dataset and a multi-stylized language corpus without paired images. How to learn a single model for multi-stylized image captioning with unpaired data is a challenging and necessary task, whereas rarely studied in previous works. The proposed framework mainly includes four contributive modules following a typical image encoder. First, a style dependent caption generator to output a sentence conditioned on an encoded image and a specified style. Second, a caption discriminator is presented to distinguish the input sentence to be real or not. The discriminator and the generator are trained in an adversarial manner to enable more natural and human-like captions. Third, a style classifier is employed to discriminate the specific style of the input sentence. Besides, a back-translation module is designed to enforce the generated stylized captions are visually grounded, with the intuition of the cycle consistency for factual caption and stylized caption. We enable an end-to-end optimization of the whole model with differentiable softmax approximation. At last, we conduct comprehensive experiments using a combined dataset containing four caption styles to demonstrate the outstanding performance of our proposed method.

Resource Optimization in Device-to-Device Cellular Systems Using Time-Frequency Hopping

Abstract: We develop a flexible and accurate framework for device-to-device (D2D) communication in the context of a conventional cellular network, which allows for time-frequency resources to be either shared or orthogonally partitioned between the two networks. Using stochastic geometry, we provide accurate expressions for SINR distributions and average rates, under an assumption of interference randomization via time and/or frequency hopping, for both dedicated and shared spectrum approaches. We obtain analytical results in closed or semi-closed form in high SNR regime, that allow us to easily explore the impact of key parameters (e.g., the load and hopping probabilities) on the network performance. In particular, unlike other models, the expressions we obtain are tractable, i.e., they can be efficiently optimized without extensive simulation. Using these, we optimize the hopping probabilities for the D2D links, i.e., how often they should request a time or frequency slot. This can be viewed as an optimized lower bound to other more sophisticated scheduling schemes. We also investigate the optimal resource partitions between D2D and cellular networks when the dedicated spectrum approach is used.

CSO: cross stratum optimization for optical as a service

Abstract: Data center interconnection with elastic optical networks is a promising scenario to meet the high burstiness and high-bandwidth requirements of services. Many data center services require lower delay and higher availability with end-to-end guaranteed QoS, which involves both application and transport network resources. However, in the current mode of operation, the control of elastic optical networks and data centers is separately deployed. Enabling even limited interworking among these separated control systems requires the adoption of complex and inelastic interfaces among the various networks, and this solution is not efficient enough to provide the required QoS. In this article, we present a novel cross stratum optimization (CSO) architecture in elastic data center optical interconnection. The proposed architecture can allow global optimization and control across elastic optical transport network and data center application stratum heterogeneous resources to meet the QoS requirement with the objective of optical as a service (OaaS). The functional modules of CSO architecture, including the core elements of application and transport controllers, are described in detail. The cooperation procedure in CSO-based service provisioning and cross stratum service resilience modes is investigated. The overall feasibility and efficiency of the proposed architecture is also experimentally demonstrated on our OaaS testbed with four OpenFlow-enabled elastic optical nodes, and compared to MFA, ALB, and CSO-DGLB service provisioning schemes in terms of path setup/release/adjustment latency, blocking probability, and resource occupation rate. Numerical results are given and analyzed based on the testbed. Some future discussion and exploration issues are presented in the conclusion.