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..

Optimal resource allocation in uplink SC-FDMA systems

Abstract: We present algorithms for resource allocation in Single Carrier Frequency Division Multiple Access (SC-FDMA) systems, which is the uplink multiple access scheme considered in the Third Generation Partnership Project-Long Term Evolution (3GPP-LTE) standard. Unlike the well-studied problem of Orthogonal Frequency Division Multiple Access (OFDMA) resource allocation, the "subchannel adjacency" restriction, whereby users can only be assigned multiple subchannels that are adjacent to each other, makes the problem much harder to solve. We present a novel reformulation of this problem as a pure binary-integer program called the set partitioning problem, which is a well studied problem in operations research. We also present a greedy heuristic algorithm that approaches the optimal performance in cases of practical interest. We present simulation results for 3GPP-LTE uplink scenarios.

Unsupervised Person Re-Identification via Softened Similarity Learning

Abstract: Person re-identification (re-ID) is an important topic in computer vision. This paper studies the unsupervised setting of re-ID, which does not require any labeled information and thus is freely deployed to new scenarios. There are very few studies under this setting, and one of the best approach till now used iterative clustering and classification, so that unlabeled images are clustered into pseudo classes for a classifier to get trained, and the updated features are used for clustering and so on. This approach suffers two problems, namely, the difficulty of determining the number of clusters, and the hard quantization loss in clustering. In this paper, we follow the iterative training mechanism but discard clustering, since it incurs loss from hard quantization, yet its only product, image-level similarity, can be easily replaced by pairwise computation and a softened classification task. With these improvements, our approach becomes more elegant and is more robust to hyper-parameter changes. Experiments on two image-based and video-based datasets demonstrate state-of-the-art performance under the unsupervised re-ID setting.

System and method for virtualized functions in control and data planes

Abstract: Methods and apparatus for management of network slices in a communication network such as a 5 th generation wireless communication network are provided. Management planes may be provided which are separate from the plurality of network slices. A connection manager residing in a management plane receives an indication that a mobile device is to be associated with the communication network. The connection manager may reside at an access node or in the core network. A network slice is determined, and the connection manager transmits instructions, to one or more network nodes, to associate the mobile device with the network slice. The instructions may be provided to a local connection manager. The slice may be requested explicitly by the mobile device, or determined based on device and/or network requirements.

The Foundation of the Mobile and Wireless Communications System for 2020 and Beyond: Challenges, Enablers and Technology Solutions

Abstract: In 2020, mobile and wireless traffic volume is expected to increase thousand-fold over 2010 figures. Moreover, an increase in the number of wirelessly-connected devices to counts in the tens of billions will have a profound impact on society. Massive machine communication, forming the basis for the Internet of Things, will make our everyday life more efficient, comfortable and safer, through a wide range of applications including traffic safety and medical services. The variety of applications and data traffic types will be significantly larger than today, and will result in more diverse requirements on services, devices and networks. METIS is set up by leading global players to prepare the migration towards tomorrow's multi-purpose global communication infrastructure, serving humans and things. The main objective of METIS is to lay the foundation for this future global mobile and wireless communications system, and to generate a global consensus here. In particular, METIS will provide new solutions which fit the needs beyond 2020.

QoS-Aware Adaptive Routing in Multi-layer Hierarchical Software Defined Networks: A Reinforcement Learning Approach

Abstract: Software-defined networks (SDNs) have been recognized as the next-generation networking paradigm that decouples the data forwarding from the centralized control. To realize the merits of dedicated QoS provisioning and fast route (re-)configuration services over the decoupled SDNs, various QoS requirements in packet delay, loss, and throughput should be supported by an efficient transportation with respect to each specific application. In this paper, a QoS-aware adaptive routing (QAR) is proposed in the designed multi-layer hierarchical SDNs. Specifically, the distributed hierarchical control plane architecture is employed to minimize signaling delay in large SDNs via three-levels design of controllers, i.e., the super, domain (or master), and slave controllers. Furthermore, QAR algorithm is proposed with the aid of reinforcement learning and QoS-aware reward function, achieving a time-efficient, adaptive, QoS-provisioning packet forwarding. Simulation results confirm that QAR outperforms the existing learning solution and provides fast convergence with QoS provisioning, facilitating the practical implementations in large-scale software service-defined networks.