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19th International Conference on Industrial Engineering and Engineering Management: Engineering Economics Management

作者： Gao, Lu Liu, Quan Lou, Ping Hubei Key Laboratory of Broadband Wireless Communication Sensor Network School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9783642384417

As a new manufacturing paradigm, cloud manufacturing transforms the traditional manufacturing business model, in which distributed resources belonged to different enterprises are encapsulated into cloud services. Clients can use cloud services according to their requirements, viz., clients can build up their own ondemand production service networks. However, it is quite difficult for clients to select reliable and high-quality service nodes to organize the production service network because of the existence of malicious, fake, untrue, and dynamic service nodes in the cloud manufacturing environment. The trustworthy cloud manufacturing environment is a prerequisite to implementation of cloud manufacturing. The reliable evaluation for trust works as a compass for building the trustworthy cloud manufacturing environment. In this paper, the trust measurement is discussed in cloud manufacturing environment and the trustworthy environment will be built up with this evaluation mechanism. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Web services

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Orthogonal-triangular decomposition ghost imaging

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Chinese Physics B 2022年第8期31卷 399-404页

作者： Jin-Fen Liu Le Wang Sheng-Mei Zhao Institute of Signal Processing and Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China Nanjing Tech University Pujiang Institute Nanjing 211222China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing 210003China

Ghost imaging(GI)offers great potential with respect to conventional imaging ***,there are still some obstacles for reconstructing images with high quality,especially in the case that the orthogonal measurement matrix is impossible to *** this paper,we propose a new scheme based on the orthogonal-triangular(QR)decomposition,named QR decomposition ghost imaging(QRGI)to reconstruct a better image with good *** the scheme,we can change the randomly non-orthogonal measurement matrix into orthonormal matrix by performing QR decomposition in two cases.(1)When the random measurement matrix is square,it can be firstly decomposed into an orthogonal matrix Q and an upper triangular matrix *** let the off-diagonal values of R equal to 0.0,the diagonal elements of R equal to a constant k,where k is the average of all values of the main diagonal,so the resulting measurement matrix can be obtained.(2)When the random measurement matrix is with full rank,we firstly compute its transpose,and followed with above QR ***,the image of the object can be reconstructed by correlating the new measurement matrix and corresponding bucket *** experimental and simulation results verify the feasibility of the proposed QRGI ***,the results also show that the proposed QRGI scheme could improve the imaging quality comparing to traditional GI(TGI)and differential GI(DGI).Besides,in comparison with the singular value decomposition ghost imaging(SVDGI),the imaging quality and the reconstruction time by using QRGI are similar to those by using SVDGI,while the computing time(the time consuming on the light patterns computation)is substantially shortened.

关键词： orthogonal-triangular(QR)decomposition ghost imaging correlated imaging

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Convergence analysis and application of the central force optimization algorithm

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Science China(Information Sciences) 2015年第5期58卷 208-210页

作者： MENG Chao GONG Jing LIU SanMin SUN ZhiXin Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of Education College of Mathematics & Physics Nanjing University of Posts and Telecommunications College of Computer and Information Anhui Polytechnic University

Dear editor,The Central Force Optimization(CFO)[1]is a new heuristic *** contrast with the stochastic algorithms such as Particle Swarm Optimization(PSO)[2],Ant Colony Optimization[3]and other such algorithms[4–6],CF... 详细信息

关键词： CFO Convergence analysis and application of the central force optimization algorithm

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Federated Knowledge Distillation Enabled Image Semantic communication

Federated Knowledge Distillation Enabled Image Semantic Comm...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Xu, Xinmian Zheng, Kaipeng Dou, Haie Chen, Mingkai Wang, Lei Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China

ISBN: (纸本)9798350351255

The transition from 5G to beyond 5G (B5G) heralds a shift towards more pervasive and intelligent communication trends. This evolution necessitates a departure from traditional information theory towards semantic communication (SemCom), propelled by artificial intelligence (AI), aimed at enhancing capacity and optimizing resources. Concurrently, image SemCom (ISC) emerges to empower image applications. However, ISC demands suitable devices and ample computing resources to support complex neural network models and their training, presenting a significant challenge. In response, we propose a federated semantic feature distillation (FedSFD) architecture to enhance the overall performance of ISC. Combining federated learning (FL) and feature distillation (FD), FedSFD facilitates the transfer of group feature knowledge. Specifically, the powerful server model refines itself by approximating the distance between its middle layer features and those of the devices via FD. Subsequently, lightweight device ISC models leverage FD to incorporate the server model's knowledge during training. This iterative process is bolstered by the information bottleneck (IB)-based loss function, enhancing image compression and reconstruction capabilities. Notably, this architecture operates without necessitating a unified model, thereby offering improved privacy protection. Simulation experiments demonstrate that compared to the baseline, our approach can achieve superior integration capability for ISC at the noisy edge. © 2024 IEEE.

关键词： Federated learning

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Improvement of middleware-based redundant reader elimination algorithm in RFID systems 4th

Improvement of middleware-based redundant reader elimination...

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4th International Conference on Electronics, communications and networks, CECNet2014

作者： Zhou, Ning Lv, Qunwu Zhou, Jianxin Hubei Key Laboratory of Broadband Wireless Communication and Sensor Network School of Information Engineering Wuhan University of Technology Wu Han China

ISBN: (纸本)9781138028302

This article gives a general introduction to some common algorithms of redundant reader elimination in Radio Frequency Identification Devices (RFID) systems, such as Redundant Reader Elimination (RRE), Layered Elimination Optimization (LEO) and Improvable-Redundant Reader Elimination (I-RRE), etc. And this article improves the Middleware-based algorithm for Redundant Reader Elimination (MRRE) which was proposed before. These improvements of the algorithms can solve the problem of cross-sections existing in many readers densely distributed in an RFID system. What’s more, when each reader identifies the same number of tags, the use of MRRE algorithms fails to judge which the redundant readers are. Therefore, large-scale tags-readers systems in the integrated development environment of Microsoft’s VS2005 are designed to simulate different scenes so as to compare the efficiency for finding redundant readers of the MRRE algorithm with that of the improved algorithm. The results show that the detecting rate of the improved algorithm increases by about 17.6% than that of a MRRE algorithm in finding redundant readers. © 2015 Taylor & Francis Group, London.

关键词： Middleware

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Free-space measurement-device-independent quantum-key-distribution protocol using decoy states with orbital angular momentum

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Chinese Physics B 2015年第12期24卷 238-245页

作者：王乐赵生妹巩龙延程维文 Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Information Physics Research Center and Department of Applied Physics Nanjing University of Posts and Telecommunications

In this paper, we propose a measurement-device-independent quantum-key-distribution（MDI-QKD） protocol using orbital angular momentum（OAM） in free space links, named the OAM-MDI-QKD protocol. In the proposed protocol,the OAM states of photons, instead of polarization states, are used as the information carriers to avoid the reference frame alignment, the decoy-state is adopted to overcome the security loophole caused by the weak coherent pulse source, and the high efficient OAM-sorter is adopted as the measurement tool for Charlie to obtain the output OAM state. Here, Charlie may be an untrusted third party. The results show that the authorized users, Alice and Bob, could distill a secret key with Charlie＇s successful measurements, and the key generation performance is slightly better than that of the polarization-based MDI-QKD protocol in the two-dimensional OAM cases. Simultaneously, Alice and Bob can reduce the number of flipping the bits in the secure key distillation. It is indicated that a higher key generation rate performance could be obtained by a high dimensional OAM-MDI-QKD protocol because of the unlimited degree of freedom on OAM states. Moreover,the results show that the key generation rate and the transmission distance will decrease as the growth of the strength of atmospheric turbulence（AT） and the link attenuation. In addition, the decoy states used in the proposed protocol can get a considerable good performance without the need for an ideal source.

关键词： measurement-device-independent quantum key distribution orbital angular momentum atmospheric turbulence decoy states

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Game-Theoretic Deep Reinforcement Learning for Task Offloading in Heterogeneous MEC Systems 16

Game-Theoretic Deep Reinforcement Learning for Task Offloadi...

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16th International Conference on wireless communications and Signal Processing, WCSP 2024

作者： Yu, Han Dou, Haie Xu, Xinmian Cui, Jingwu Wang, Lei Liu, Chen Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China

ISBN: (纸本)9798350390643

The emergence of new applications has increased the demand of mobile users for edge computing. Therefore, the optimization of base station load balancing and task offloading is particularly important. In this paper, a two-tier heterogeneous mobile edge computing (MEC) system based on Stackelberg Game (SG) is studied. The introduction of SG can improve user association flexibility while reducing base station load. The introduction of network slicing can classify users according to different types, which increases the universality of the proposed framework. To solve the above problem, we map an optimization problem of efficiency, computation delay and computation energy consumption. Due to the non-convexity of the optimization problem, the Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm based on SG is proposed in this paper. The gradient descent strategy can converge faster and obtain the optimal solution of user association policy and unload policy. Through simulation, it is verified that the proposed scheme can obtain lower computation delay and computation energy consumption on the basis of solving the problem of base station overload. © 2024 IEEE.

关键词： Deep reinforcement learning

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Utility Maximization for NFV-Based Tactile Internet

Utility Maximization for NFV-Based Tactile Internet

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2023 IEEE/CIC International Conference on communications in China, ICCC 2023

作者： Xu, Xinmian Dou, Haie Chen, Mingkai Wang, Lei Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China

ISBN: (纸本)9798350345384

In recent years, there has been significant attention and development in the Tactile Internet (TI) as a new type of network that supports typical applications such as remote healthcare and virtual reality. However, the demand for ultra-low latency and the inflexibility of traditional network architecture have presented serious challenges. In this paper, we propose a utility function for TI based on 5G network function virtualization (NFV) technology to evaluate network performance. The utility function considers the transmission of haptic and video data flows, as well as their latency and cost. We then formulate a network optimization problem with the utility function as the objective, strictly constraining latency and cost. To address this problem, we design two algorithms: the VNF deployment and routing of haptic and video flows (DRHV) algorithm tackles the VNF deployment and path selection issue, while the deep deterministic policy gradient(DDPG)-based traffic scheduling (DTS) algorithm allocates traffic resources and saves costs. Simulation results demonstrate that our algorithms achieve excellent optimization performance under the given constraints, and parameter changes align with the expected relationships and trends. © 2023 IEEE.

关键词： network function virtualization

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Distributed Deep Learning for Cooperative Computation Offloading in Low Earth Orbit Satellite networks

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China communications 2022年第4期19卷 230-243页

作者： Qingqing Tang Zesong Fei Bin Li School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of EducationNanjing 210003China

Low earth orbit(LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the“ubiquitous connection” of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading(DDLCCO) algorithm, where multiple parallel deep neural networks(DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.

关键词： LEO satellite networks computation offloading deep neural networks

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Reinforcement Learning from Human-like Feedback Enhances Semantic communication with Multimodal LLMs 16

Reinforcement Learning from Human-like Feedback Enhances Sem...

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16th International Conference on wireless communications and Signal Processing, WCSP 2024

作者： Zhang, Xinyu He, Xitao Chen, Mingkai Wang, Lei Ministry of Education Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China

ISBN: (纸本)9798350390643

Semantic communication is an emerging intelligent paradigm that provides some special solutions for the various future applications. With the rise of large language model (LLM), it may enhance well on semantic communication. However, how to balance on the accuracy and the efficiency of compression remains an urgent issue to semantic communication with LLMs. In this paper, we design a multimodal large language models (MLLMs)-enabled semantic communication system specifically designed for image transmission, where the reinforcement learning from human-like feedback (RLHLF) framework is proposed to apply reasoning and self-reflection capabilities of MLLMs to enhance the quality of semantic extraction and image generation. For one thing, we first optimize the image semantic information at the transmitter by RLHLF framework. For another thing, the generated images are optimized at the receiver by RLHLF framework. Simulation results show that our proposed system provides bandwidth savings and enables graceful performance improvement as the channel quality improves, which proves the potential of MLLMs for future semantic communication. © 2024 IEEE.

关键词： Channel coding

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