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Highly efficient twin-field quantum key distribution with neural networks

Science China(Information Sciences) 2023年第8期66卷 281-282页

作者： Jingyang LIU Qingqing JIANG Huajian DING Xiao MA Mingshuo SUN Jiaxin XU Chun-Hui ZHANG Shipeng XIE Jian LI Guigen ZENG Xingyu ZHOU Qin WANG Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Telecommunication and Networks National Engineering Research Center Nanjing University of Posts and Telecommunications

Quantum key distribution(QKD) allows two legitimate parties, Alice and Bob, to share a series of random bits that are secured by the laws of quantum mechanics. As a remarkable theoretical progress towards longer distances, the proposal of twin-field QKD(TF-QKD) [1] shows the capability to break a repeaterless bound and provides a key rate in the square-root scale of channel transmittance(i.e.,

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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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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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Bessel–Gaussian beam-based orbital angular momentum holography

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Chinese Physics B 2024年第1期33卷 407-413页

作者：季佳滢郑志刚朱家龙王乐王新光赵生妹 Institute of Signal Processing and Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing 210003China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210093China

Orbital angular momentum(OAM), as a new degree of freedom, has recently been applied in holography *** to the infinite helical mode index of OAM mode, a large number of holographic images can be reconstructed from an OAM-multiplexing hologram. However, the traditional design of an OAM hologram is constrained by the helical mode index of the selected OAM mode, for a larger helical mode index OAM mode has a bigger sampling distance, and the crosstalk is produced for different sampling distances for different OAM modes. In this paper, we present the design of the OAM hologram based on a Bessel–Gaussian beam, which is non-diffractive and has a self-healing property during its propagation. The Fourier transform of the Bessel–Gaussian beam is the perfect vortex mode that has the fixed ring radius for different OAM modes. The results of simulation and experiment have demonstrated the feasibility of the generation of the OAM hologram with the Bessel–Gaussian beam. The quality of the reconstructed holographic image is increased, and the security is enhanced. Additionally, the anti-interference property is improved owing to its self-healing property of the Bessel-OAM holography.

关键词： orbital angular momentum holography Bessel–Gaussian beam OAM-multiplexing hologram

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QoE oriented intelligent online learning evaluation technology in B5G scenario

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Digital communications and networks 2024年第1期10卷 7-15页

作者： Mingzi Chen Xin Wei Peizhong Xie Zhe Zhang School of Communications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications) Ministry of EducationNanjing210003China Institute of Higher Education Nanjing University of Posts and TelecommunicationsNanjing210003China

Students'demand for online learning has exploded during the post-COvID-19 pandemic ***,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always *** technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the *** that affect the Quality of Online Learning Experience(OL-QoE)become more *** get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology ***,we first formulate a general definition of OL-QoE by data analysis and experimental *** formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect ***,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE *** the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation ***,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE *** timely interventions,their OL-QoE and learning performance can be *** results verify the effectiveness oftheproposed scheme.

关键词： B5G Online learning Quality of experience

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Diffraction deep neural network based orbital angular momentum mode recognition scheme in oceanic turbulence

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Chinese Physics B 2023年第4期32卷 364-369页

作者：詹海潮陈兵彭怡翔王乐王文鼐赵生妹 Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications(NUPT)Nanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(Ministry of Education) Nanjing University of Posts and TelecommunicationsNanjing 210003China

Orbital angular momentum(OAM)has the characteristics of mutual orthogonality between modes,and has been applied to underwater wireless optical communication(UWOC)systems to increase the channel *** this work,we propose a diffractive deep neural network(DDNN)based OAM mode recognition scheme,where the DDNN is trained to capture the features of the intensity distribution of the OAM modes and output the corresponding azimuthal indices and radial *** results show that the proposed scheme can recognize the azimuthal indices and radial indices of the OAM modes accurately and *** addition,the proposed scheme can resist weak oceanic turbulence(OT),and exhibit excellent ability to recognize OAM modes in a strong OT *** DDNN-based OAM mode recognition scheme has potential applications in UWOC systems.

关键词： orbital angular momentum diffractive deep neural network mode recognition oceanic turbulence

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Diffraction deep neural network-based classification for vector vortex beams

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Chinese Physics B 2024年第3期33卷 387-392页

作者：彭怡翔陈兵王乐赵生妹 Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications (NJUPT)Nanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing 210003China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210093China

The vector vortex beam(VVB)has attracted significant attention due to its intrinsic diversity of information and has found great applications in both classical and quantum ***,a VVB is unavoidably affected by atmospheric turbulence(AT)when it propagates through the free-space optical communication environment,which results in detection errors at the *** this paper,we propose a VVB classification scheme to detect VVBs with continuously changing polarization states under AT,where a diffractive deep neural network(DDNN)is designed and trained to classify the intensity distribution of the input distorted VVBs,and the horizontal direction of polarization of the input distorted beam is adopted as the feature for the classification through the *** numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification *** energy distribution percentage remains above 95%from weak to medium AT,and the classification accuracy can remain above 95%for various strengths of *** has a faster convergence and better accuracy than that based on a convolutional neural network.

关键词： vector vortex beam diffractive deep neural network classification atmospheric turbulence

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Multimodal fusion recognition for digital twin

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Digital communications and networks 2024年第2期10卷 337-346页

作者： Tianzhe Zhou Xuguang Zhang Bing Kang Mingkai Chen Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China

The digital twin is the concept of transcending reality,which is the reverse feedback from the real physical space to the virtual digital *** hold great prospects for this emerging *** order to realize the upgrading of the digital twin industrial chain,it is urgent to introduce more modalities,such as vision,haptics,hearing and smell,into the virtual digital space,which assists physical entities and virtual objects in creating a closer ***,perceptual understanding and object recognition have become an urgent hot topic in the digital *** surface material classification schemes often achieve recognition through machine learning or deep learning in a single modality,ignoring the complementarity between multiple *** order to overcome this dilemma,we propose a multimodal fusion network in our article that combines two modalities,visual and haptic,for surface material *** the one hand,the network makes full use of the potential correlations between multiple modalities to deeply mine the modal semantics and complete the data *** the other hand,the network is extensible and can be used as a universal architecture to include more *** show that the constructed multimodal fusion network can achieve 99.42%classification accuracy while reducing complexity.

关键词： Digital twin Multimodal fusion Object recognition Deep learning Transfer learning

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A teacher-student based attention network for fine-grainedimage recognition

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Digital communications and networks 2025年第1期11卷 52-59页

作者： Ang Li Xueyi Zhang Peilin Li Bin Kang Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(Ministry of Education) Nanjing University of Posts and TelecommunicationsNanjing210003China School of Communications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing210003China School of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing210003China

Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car *** order to highlight visual differences,existing FGIR works often follow two steps:discriminative sub-region localization and local feature ***,these works pay less attention on global context *** neglect a fact that the subtle visual difference in challenging scenarios can be highlighted through exploiting the spatial relationship among different subregions from a global view ***,in this paper,we consider both global and local information for FGIR,and propose a collab.rative teacher-student strategy to reinforce and unity the two types of *** framework is implemented mainly by convolutional neural network,referred to Teacher-Student Based Attention Convolutional Neural network(T-S-ACNN).For fine-grained local information,we choose the classic Multi-Attention network(MA-Net)as our baseline,and propose a type of boundary constraint to further reduce background noises in the local attention *** this way,the discriminative sub-regions tend to appear in the area occupied by fine-grained objects,leading to more accurate sub-region *** fine-grained global information,we design a graph convolution based Global Attention network(GA-Net),which can combine extracted local attention maps from MA-Net with non-local techniques to explore spatial relationship among *** last,we develop a collab.rative teacher-student strategy to adaptively determine the attended roles and optimization modes,so as to enhance the cooperative reinforcement of MA-Net and *** experiments on CUB-200-2011,Stanford Cars and FGVC Aircraft datasets illustrate the promising performance of our framework.

关键词： Fine-grained image recognition Collab.rative teacher-student strategy Multi-attention Global attention

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Cloud-Edge Collab.ration via Preference-based Reinforcement Learning in Semantic communications 11

Cloud-Edge Collaboration via Preference-based Reinforcement ...

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11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Xu, Yikai Chen, Mingkai Zhang, Zhe Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China

ISBN: (纸本)9798350345346

With the development of next-generation mobile communication, semantic communication is considered as a novel technology that focuses on conveying the meaning of a message to the intended users. The key of semantic communication is that the receiver and sender should have the similar background knowledge, hence the semantic knowledge base is the corner-stone of semantic communication. However, how to select an appropriate content as the background knowledge from huge amount of knowledge base data is a challenge in realizing a semantic communication system. Caching partial semantic knowledge bases at edge servers in advance is a promising approach to overcome the above issue. In this paper, we propose a novel semantic knowledge base deployment architecture, in which the specific content of the semantic knowledge base cached at the edge server is determined by the user's personal preferences, fact relevance, and the sender-receiver matching degree. The semantic knowledge base deployment architecture is affected by conditions such as latency and storage capacity of the edge server. Based on preference-based reinforcement learning (PbRL), we propose a policy ranking algorithm to drive the replacement of the categorical cache for semantic knowledge bases to maximize the hit ratio and the sender-receiver matching degree under the constraints of latency and caching capacity. Simulation results show that our algorithm can obtain higher knowledge base hit ratio and matching degree. © 2023 IEEE.

关键词： Reinforcement learning

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