Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

Spherical nebulons and Bäcklund transformation for a space or laboratory un-magnetized dusty plasma with symbolic computation

Abstract: Plasmas and dust are a couple of the ubiquitous elements of the Universe. The nonlinear dust-acoustic-wave propagation in a space or laboratory un-magnetized dusty plasma is hereby considered, which is described by a spherical Kadomtsev-Petviashvili equation. By virtue of computerized symbolic computation, we obtain the analytically-expressed spherical nebulons and an auto-Backlund transformation for the electrostatic potential in such a plasma. With figures, we discuss the features of the nebulon structures, and propose some (3+1)-dimensional, possibly-observable nebulonic effects for the future space/laboratory plasma experiments.

Plasmon-induced transparency in asymmetric T-shape single slit.

Abstract: By utilizing a dielectric-film-coated asymmetric T-shape single slit, comprising two grooves of slightly detuned widths immediately contacting with a single nanoslit, the plasmon-induced transparency was experimentally demonstrated. Because of the symmetry breaking in the unit-cell structure, the scattered lights from the two grooves with slightly detuned widths interfere destructively, leading to the plasmon-induced transparency. As a result, a response spectrum with nearly the same interference contrast but a much narrower bandwidth emerges in the unit-cell structure with the footprint of only about 0.9 μm(2), compared with that in the symmetric T-shape single slit. These pronounced features in the structure, such as the increased quality factor, ultracompact size, easy fabrication, and experimental observation, have significant applications in ultracompact plasmonic devices.

Caching based socially-aware D2D communications in wireless content delivery networks: a hypergraph framework

Abstract: The emergence of the D2D communications paradigm has transformed the way in which cellular networks are operated. Previous work in D2D communications, however, mainly focused on interference management and capacity maximization of both D2D links and cellular links. Recent literature has observed that the QoE can be greatly enhanced by caching contents in mobile devices, with a carefully designed caching strategy. In this article, we propose a novel hypergraph framework that designs the caching based D2D communication scheme by taking social ties among users and common interests into consideration. We first present the considered features of the D2D transmission scheme, social characteristics consideration, and interest similarity impact. The key concepts of hypergraph and related techniques, such as hypergraph coloring and multidimensional matching, are explained. Then some design issues with simulation results in the proposed framework are discussed in detail, which show the potential of the proposed approach. By jointly considering social ties, common interests, and the D2D transmission scheme, we believe the proposed framework explores new opportunities and future directions in caching based socially-aware D2D communications.

Graph Contrastive Learning with Adaptive Augmentation

Abstract: Recently, contrastive learning (CL) has emerged as a successful method for unsupervised graph representation learning. Most graph CL methods first perform stochastic augmentation on the input graph to obtain two graph views and maximize the agreement of representations in the two views. Despite the prosperous development of graph CL methods, the design of graph augmentation schemes -- a crucial component in CL -- remains rarely explored. We argue that the data augmentation schemes should preserve intrinsic structures and attributes of graphs, which will force the model to learn representations that are insensitive to perturbation on unimportant nodes and edges. However, most existing methods adopt uniform data augmentation schemes, like uniformly dropping edges and uniformly shuffling features, leading to suboptimal performance. In this paper, we propose a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological and semantic aspects of the graph. Specifically, on the topology level, we design augmentation schemes based on node centrality measures to highlight important connective structures. On the node attribute level, we corrupt node features by adding more noise to unimportant node features, to enforce the model to recognize underlying semantic information. We perform extensive experiments of node classification on a variety of real-world datasets. Experimental results demonstrate that our proposed method consistently outperforms existing state-of-the-art baselines and even surpasses some supervised counterparts, which validates the effectiveness of the proposed contrastive framework with adaptive augmentation.

QoI-Aware Multitask-Oriented Dynamic Participant Selection With Budget Constraints

Abstract: By using increasingly popular smartphones, participatory sensing systems can collect comprehensive sensory data to retrieve context-aware information for different applications (or sensing tasks). However, new challenges arise when selecting the most appropriate participants when considering their different incentive requirements, associated sensing capabilities, and uncontrollable mobility, to best satisfy the quality-of-information (QoI) requirements of multiple concurrent tasks with different budget constraints. This paper proposes a multitask-oriented participant selection strategy called “DPS,” which is used to tackle the aforementioned challenges, where three key design elements are proposed. First is the QoI satisfaction metric, where the required QoI metrics of the collected data are quantified in terms of data granularity and quantity. Second is the multitask-orientated QoI optimization problem for participant selection, where task budgets are treated as the constraint, and the goal is to select a minimum subset of participants to best provide the QoI satisfaction metrics for all tasks. The optimization problem is then converted to a nonlinear knapsack problem and is solved by our proposed dynamic participant selection (DPS) strategy. Third is how to compute the expected amount of collected data by all (candidate) participants, where a probability-based movement model is proposed to facilitate such computation. Real and extensive trace-based simulations show that, given the same budget, the proposed participant selection strategy can achieve far better QoI satisfactions for all tasks than selecting participants randomly or through the reversed-auction-based approaches.