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..

Bayesian graph convolutional neural networks for semi-supervised classification

Abstract: Recently, techniques for applying convolutional neural networks to graph-structured data have emerged. Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion. Although the performance has been impressive, the current implementations have limited capability to incorporate uncertainty in the graph structure. Almost all GCNNs process a graph as though it is a ground-truth depiction of the relationship between nodes, but often the graphs employed in applications are themselves derived from noisy data or modelling assumptions. Spurious edges may be included; other edges may be missing between nodes that have very strong relationships. In this paper we adopt a Bayesian approach, viewing the observed graph as a realization from a parametric family of random graphs. We then target inference of the joint posterior of the random graph parameters and the node (or graph) labels. We present the Bayesian GCNN framework and develop an iterative learning procedure for the case of assortative mixed-membership stochastic block models. We present the results of experiments that demonstrate that the Bayesian formulation can provide better performance when there are very few labels available during the training process.

Backhauling in Heterogeneous Cellular Networks: Modeling and Tradeoffs

Abstract: Consumer demand for data has increased tremendously over the last years, and small cell networks are increasingly being considered as one of the key technologies to cope with this demand. Small cell network deployments within the conventional macro cellular networks are creating a significant amount of heterogeneity compared to traditional cellular networks. Nevertheless, the backhaul link is often the bottleneck in terms of system performance and cost. In this paper, we consider the backhaul issue in heterogeneous cellular networks and propose a hierarchical network model using superimposed independent homogeneous Poisson point processes. We derive the total expected delay by taking into account retransmissions over the wireless link, as well as the backhaul delay incurred from both wired and wireless backhaul. For the total expected deployment cost, we take into account infrastructure cost and the construction cost of wired backhaul deployment. Specifically, we are able to characterize the behavior of delay and deployment cost using our simple and tractable model. Furthermore, we propose a delay-based access control policy, which can provide better latency performance especially in dense deployments. Our theoretical framework provides a fundamental understanding of the tradeoff between wired and wireless backhaul and the effect on deployment cost and system performance in heterogeneous cellular networks.

The XG-PON System: Cost Effective 10 Gb/s Access

Abstract: The ten gigabit passive optical network (XG-PON) system is the newest member of the ITU-T family of passive optical network standards. XG-PON is the result of a 3 year project involving the full service access network (FSAN) group and ITU-T study group 15 (SG15) question 2. This paper reviews the deliberations that led to the selection of the XG-PON system, and then explains the three primary layers of the system: physical, protocol, and management. The paper concludes with information on standards and implementations of the system, and on future work in this area.

A Compact Dual-Band Dual-Polarized Antenna With Filtering Structures for Sub-6 GHz Base Station Applications

Abstract: This letter presents a novel compact dual-band dual-polarized antenna with filtering structures for Sub-6 GHz base station applications. It is operating at the LTE (2500–2690 MHz) and Sub-6 GHz of 5G (3300–3600 MHz) bands, where the center frequency of upper-band (UB) is 1.32 times of lower-band (LB). Mutual coupling between LB and UB is suppressed by the introduction of the filtering stubs near the feeding lines. A sufficiently high isolation between LB and UB (>25 dB) is also obtained. The size of the proposed antenna is only 0.43 ${{\rm{\lambda }}_1} \times \text{0.43}$ ${{\rm{\lambda }}_1} \times \text{0.26}$ ${{\rm{\lambda }}_{1}}$ ( ${{\rm{\lambda }}_1}$ is the free-wavelength at 2.6 GHz). The half power beam widths are 65° ± 5° over both bands at horizontal planes. A four-element array, which consists of four hybrid antennas (the UB element is nested in the LB element) with a small array width (only 105 mm) is also studied. The proposed antenna and its array are suitable for wireless communication applications.

Optimizing Content Caching to Maximize the Density of Successful Receptions in Device-to-Device Networking

Abstract: Device-to-device ( $ \sf {D2D}$ ) communication is a promising approach to optimize the utilization of air interface resources in 5G networks, since it allows decentralized opportunistic short-range communication. For $ \sf {D2D}$ to be useful, mobile nodes must possess content that other mobiles want. Thus, intelligent caching techniques are essential for $ \sf {D2D}$ . In this paper, we use results from stochastic geometry to derive the probability of successful content delivery in the presence of interference and noise. We employ a general transmission strategy, where multiple files are cached at the users and different files can be transmitted simultaneously throughout the network. We then formulate an optimization problem, and find the caching distribution that maximizes the density of successful receptions (DSR) under a simple transmission strategy, where a single file is transmitted at a time throughout the network. We model file requests by a Zipf distribution with exponent $\gamma _{r}$ , which results in an optimal caching distribution that is also a Zipf distribution with exponent $\gamma _{c}$ , which is related to $\gamma _{r}$ through a simple expression involving the path loss exponent. We solve the optimal content placement problem for more general demand profiles under Rayleigh, Ricean, and Nakagami small-scale fading distributions. Our results suggest that it is required to flatten the request distribution to optimize the caching performance. We also develop strategies to optimize content caching for the more general case with multiple files, and bound the DSR for that scenario.