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.

Many-body Effect, Carrier Mobility, and Device Performance of Hexagonal Arsenene and Antimonene

Abstract: Two-dimensional (2D) semiconductors are very promising channel materials in next-generation field effect transistors (FETs) due to the enhanced gate electrostatics and smooth surface. Two new 2D materials, arsenene and antimonene (As and Sb analogues of graphene), have been fabricated very recently. Here, we provide the first investigation of the many-body effect, carrier mobility, and device performance of monolayer (ML) hexagonal arsenene and antimonene based on accurate ab initio methods. The quasi-particle band gaps of ML arsenene and antimonene by using the GW approximation are 2.47 and 2.38 eV, respectively. The optical band gaps of ML arsenene and antimonene from the GW-Bethe–Salpeter equation are 1.6 and 1.5 eV, with exciton binding energies of 0.9 and 0.8 eV, respectively. The carrier mobility is found to be considerably low in ML arsenene (21/66 cm2/V·s for electron/hole) and moderate in ML antimonene (150/510 cm2/V·s for electron/hole). In terms of the ab initio quantum transport simulations, t...

When Internet of Things Meets Blockchain: Challenges in Distributed Consensus

Abstract: Blockchain has been regarded as a promising technology for IoT, since it provides significant solutions for decentralized networks that can address trust and security concerns, high maintenance cost problems, and so on. The decentralization provided by blockchain can be largely attributed to the use of a consensus mechanism, which enables peer-to-peer trading in a distributed manner without the involvement of any third party. This article starts by introducing the basic concept of blockchain and illustrating why a consensus mechanism plays an indispensable role in a blockchain enabled IoT system. Then we discuss the main ideas of two famous consensus mechanisms, PoW and PoS, and list their limitations in IoT. Next, two mainstream DAG based consensus mechanisms, the Tangle and Hashgraph, are reviewed to show why DAG consensus is more suitable for IoT system than PoW and PoS. Potential issues and challenges of DAG based consensus mechanisms to be addressed in the future are discussed in the last section.

Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach

Abstract: Recent advances in the industrial Internet of things (IIoT) provide plenty of opportunities for various industries. To address the security and efficiency issues of the massive IIoT data, blockchain is widely considered as a promising solution to enable data storing/processing/sharing in a secure and efficient way. To meet the high throughput requirement, this paper proposes a novel deep reinforcement learning (DRL)-based performance optimization framework for blockchain-enabled IIoT systems, the goals of which are threefold: 1) providing a methodology for evaluating the system from the aspects of scalability, decentralization, latency, and security; 2) improving the scalability of the underlying blockchain without affecting the system's decentralization, latency, and security; and 3) designing a modulable blockchain for IIoT systems, where the block producers, consensus algorithm, block size, and block interval can be selected/adjusted using the DRL technique. Simulations results show that our proposed framework can effectively improve the performance of blockchain-enabled IIoT systems and well adapt to the dynamics of the IIoT.

Community detection in large-scale social networks

Abstract: Recent years have seen that WWW is becoming a flourishing social media which enables individuals to easily share opinions, experiences and expertise at the push of a single button. With the pervasive usage of instant messaging systems and the fundamental shift in the ease of publishing content, social network researchers and graph theory researchers are now concerned with inferring community structures by analyzing the linkage patterns among individuals and web pages. Although the investigation of community structures has motivated many diverse algorithms, most of them are unsuitable for large-scale social networks because of the computational cost. Moreover, in addition to identify the possible community structures, how to define and explain the discovered communities is also significant in many practical scenarios.In this paper, we present the algorithm ComTector(Community DeTector) which is more efficient for the community detection in large-scale social networks based on the nature of overlapping communities in the real world. This algorithm does not require any priori knowledge about the number or the original division of the communities. Because real networks are often large sparse graphs, its running time is thus O(C × Tri2), where C is the number of the detected communities and Tri is the number of the triangles in the given network for the worst case. Then we propose a general naming method by combining the topological information with the entity attributes to define the discovered communities. With respected to practical applications, ComTector is challenged with several real life networks including the Zachary Karate Club, American College Football, Scientific Collaboration, and Telecommunications Call networks. Experimental results show that this algorithm can extract meaningful communities that are agreed with both of the objective facts and our intuitions.