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Institution

Beijing University of Posts and Telecommunications

EducationBeijing, Beijing, China
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.


Papers
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Journal ArticleDOI
TL;DR: In this article, a harvest-and-forward (H2F) relay with multiple antennas is considered, where the relay harvests energy and obtains information from the source with the radio-frequent signals by jointly using the antenna selection and power splitting techniques.
Abstract: The simultaneous wireless transfer of information and power with the help of a relay equipped with multiple antennas is considered in this letter, where a “harvest-and-forward” strategy is proposed. In particular, the relay harvests energy and obtains information from the source with the radio-frequent signals by jointly using the antenna selection (AS) and power splitting (PS) techniques, and then the processed information is amplified and forwarded to the destination relying on the harvested energy. This letter jointly optimizes AS and PS to maximize the achievable rate for the proposed strategy. Considering that the joint optimization is according to the non-convex problem, a two-stage procedure is proposed to determine the optimal ratio of received signal power split for energy harvesting, and the optimized antenna set engaged in information forwarding. Simulation results confirm the accuracy of the two-stage procedure, and demonstrate that the proposed “harvest-and-forward” strategy outperforms the conventional amplify-and-forward (AF) relaying and the direct transmission.

99 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a novel CNN architecture named as ISGAN to conceal a secret gray image into a color cover image on the sender side and exactly extract the secret image out on the receiver side.
Abstract: Nowadays, there are plenty of works introducing convolutional neural networks (CNNs) to the steganalysis and exceeding conventional steganalysis algorithms. These works have shown the improving potential of deep learning in information hiding domain. There are also several works based on deep learning to do image steganography, but these works still have problems in capacity, invisibility and security. In this paper, we propose a novel CNN architecture named as ISGAN to conceal a secret gray image into a color cover image on the sender side and exactly extract the secret image out on the receiver side. There are three contributions in our work: (i) we improve the invisibility by hiding the secret image only in the Y channel of the cover image; (ii) We introduce the generative adversarial networks to strengthen the security by minimizing the divergence between the empirical probability distributions of stego images and natural images. (iii) In order to associate with the human visual system better, we construct a mixed loss function which is more appropriate for steganography to generate more realistic stego images and reveal out more better secret images. Experiment results show that ISGAN can achieve start-of-art performances on LFW, PASCAL-VOC12 and ImageNet datasets.

99 citations

Journal ArticleDOI
TL;DR: In this paper, a joint optimization problem of transmission mode selection and resource allocation for cellular V2X communications is investigated, and a deep reinforcement learning (DRL)-based decentralized algorithm is proposed to maximize the sum capacity of vehicle-to-infrastructure users while meeting the latency and reliability requirements of V2V pairs.
Abstract: Cellular vehicle-to-everything (V2X) communication is crucial to support future diverse vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehicle (V2V) links, and high signaling overhead of centralized resource allocation approaches become bottlenecks. In this article, we investigate a joint optimization problem of transmission mode selection and resource allocation for cellular V2X communications. In particular, the problem is formulated as a Markov decision process, and a deep reinforcement learning (DRL)-based decentralized algorithm is proposed to maximize the sum capacity of vehicle-to-infrastructure users while meeting the latency and reliability requirements of V2V pairs. Moreover, considering training limitation of local DRL models, a two-timescale federated DRL algorithm is developed to help obtain robust models. Wherein, the graph theory-based vehicle clustering algorithm is executed on a large timescale and in turn, the federated learning algorithm is conducted on a small timescale. The simulation results show that the proposed DRL-based algorithm outperforms other decentralized baselines, and validate the superiority of the two-timescale federated DRL algorithm for newly activated V2V pairs.

99 citations

Journal ArticleDOI
TL;DR: This article proposes a graph theory based algorithm to efficiently solve the data sharing problem, which is formulated as a maximum weighted independent set problem on the constructed conflict graph, and proposes a balanced greedy algorithm, which can make the content distribution more balanced.
Abstract: It is widely recognized that connected vehicles have the potential to further improve the road safety, transportation intelligence and enhance the in-vehicle entertainment. By leveraging the 5G enabled Vehicular Ad hoc NETworks (VANET) technology, which is referred to as 5G-VANET, a flexible software-defined communication can be achieved with ultra-high reliability, low latency, and high capacity. Many enabling applications in 5G-VANET rely on sharing mobile data among vehicles, which is still a challenging issue due to the extremely large data volume and the prohibitive cost of transmitting such data using 5G cellular networks. This article focuses on efficient cooperative data sharing in edge computing assisted 5G-VANET. First, to enable efficient cooperation between cellular communication and Dedicated Short-Range Communication (DSRC), we first propose a software-defined cooperative data sharing architecture in 5G-VANET. The cellular link allows the communications between OpenFlow enabled vehicles and the Controller to collect contextual information, while the DSRC serves as the data plane, enabling cooperative data sharing among adjacent vehicles. Second, we propose a graph theory based algorithm to efficiently solve the data sharing problem, which is formulated as a maximum weighted independent set problem on the constructed conflict graph. Specifically, considering the continuous data sharing, we propose a balanced greedy algorithm, which can make the content distribution more balanced. Furthermore, due to the fixed amount of computing resources allocated to this software-defined cooperative data sharing service, we propose an integer linear programming based decomposition algorithm to make full use of the computing resources. Extensive simulations in NS3 and SUMO demonstrate the superiority and scalability of the proposed software-defined architecture and cooperative data sharing algorithms.

98 citations

Journal ArticleDOI
TL;DR: In this paper, energy-efficient traffic grooming in IP-over-elastic optical networks with a sliceable optical transponder is studied, and three bandwidth-variable transponders (BVTs) based on their sliceability are investigated.
Abstract: The sliceable optical transponder, which can transmit/receive multiple optical flows, was recently proposed to improve a transponder's flexibility. The upper-layer traffic can be offloaded onto an optical layer with “just-enough transponder” resources. Traffic grooming evolves as the optical transponder shifts from fixed to sliceable. “Optical-layer grooming” enabled by a sliceable optical transponder can reduce the number of power-consumption components (e.g., IP ports and optical transponders). In this paper, energy-efficient traffic grooming in IP-over-elastic optical networks with a sliceable optical transponder is studied. Three bandwidth-variable transponders (BVTs) based on their sliceability, namely, non-sliceable BVTs, fully sliceable BVTs, and partially sliceable BVTs, are investigated. For each transponder, we develop energy-minimized traffic grooming integer linear programming (ILP) models and corresponding heuristic algorithms. Comprehensive comparisons are performed among the three types of transponders, and two interesting observations emerge. First, we find that significant power savings can be achieved by using a sliceable optical transponder. Second, we find that power savings do not keep improving linearly while transponder sliceability is increasing, and traditional electrical-layer grooming is still required to work together with optical-layer grooming to reduce power consumption.

98 citations


Authors

Showing all 39925 results

NameH-indexPapersCitations
Jie Zhang1784857221720
Jian Li133286387131
Ming Li103166962672
Kang G. Shin9888538572
Lei Liu98204151163
Muhammad Shoaib97133347617
Stan Z. Li9753241793
Qi Tian96103041010
Xiaodong Xu94112250817
Qi-Kun Xue8458930908
Long Wang8483530926
Jing Zhou8453337101
Hao Yu8198127765
Mohsen Guizani79111031282
Muhammad Iqbal7796123821
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202394
2022533
20213,009
20203,720
20193,817
20183,296