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Analysis and Optimization on Partition-Based Caching and Delivery in Satellite-Terrestrial Edge Computing networks

China Communications 2023年第3期20卷 252-285页

作者： Peng Wang Xing Zhang Jiaxin Zhang Shuang Zheng Wenhao Liu Wireless Signal Processing and Network Laboratory Key Laboratory of Universal Wireless CommunicationMinistry of EducationSchool of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China

As a viable component of 6G wireless communication architecture,satellite-terrestrial networks support efficient file delivery by leveraging the innate broadcast ability of satellite and the enhanced powerful file transmission approaches of multi-tier terrestrial *** the paper,we introduce edge computing technology into the satellite-terrestrial network and propose a partition-based cache and delivery strategy to make full use of the integrated resources and reducing the backhaul *** on the interference effect from varied nodes in different geographical distances,we derive the file successful transmission probability of the typical user and by utilizing the tool of stochastic *** the constraint of nodes cache space and file sets parameters,we propose a near-optimal partition-based cache and delivery strategy by optimizing the asymptotic successful transmission probability of the typical *** complex nonlinear programming problem is settled by jointly utilizing standard particle-based swarm optimization(PSO)method and greedy based multiple knapsack choice problem(MKCP)optimization *** results show that compared with the terrestrial only cache strategy,Ground Popular Strategy,Satellite Popular Strategy,and Independent and identically distributed popularity strategy,the performance of the proposed scheme improve by 30.5%,9.3%,12.5%and 13.7%.

关键词： edge computing satellite terrestrial net-works caching deployment stochastic geometry 6G networks

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FedACT:An adaptive chained training approach for federated learning in computing power networks

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Digital Communications and networks 2024年第6期10卷 1576-1589页

作者： Min Wei Qianying Zhao Bo Lei Yizhuo Cai Yushun Zhang Xing Zhang Wenbo Wang China Telecom Research Institute Beijing 102209China Wireless Signal Processing and Network Laboratory Beijing University of Posts and TelecommunicationsBeijing 100876China

Federated Learning(FL)is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data ***,the traditional FL model in communication scenarios,whether for uplink or downlink communications,may give rise to several network problems,such as bandwidth occupation,additional network latency,and bandwidth *** this paper,we propose an adaptive chained training approach(Fed ACT)for FL in computing power ***,a Computation-driven Clustering Strategy(CCS)is *** server clusters clients by task processing delays to minimize waiting delays at the central ***,we propose a Genetic-Algorithm-based Sorting(GAS)method to optimize the order of clients participating in ***,based on the table lookup and forwarding rules of the Segment Routing over IPv6(SRv6)protocol,the sorting results of GAS are written into the SRv6 packet header,to control the order in which clients participate in model *** conduct extensive experiments on two datasets of CIFAR-10 and MNIST,and the results demonstrate that the proposed algorithm offers improved accuracy,diminished communication costs,and reduced network delays.

关键词： Computing power network(CPN) Federated learning(FL) Segment routing IPv6(SRv6) Communication overheads Model accuracy

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An Enhanced Resilience and Throughput BBV-based Method for UAV networks 24

An Enhanced Resilience and Throughput BBV-based Method for U...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Sun, Jinghong Chu, Yueyan Shi, Ce Guo, Wenbin Beijing University of Posts and Telecommunications Wireless Signal Processing and Network Laboratory Key Laboratory of Universal Wireless Communications Ministry of Education Beijing China

ISBN: (纸本)9798350363760

In harsh environments such as battlefield operations and emergency communications, communication rate and network resilience significantly impact the performance of UAV swarm networks. This paper proposes a scalable UAV network construction scheme named UMBTO, which aims to maximize communication rates while enhancing network resilience. By considering the communication range of UAVs and introducing triangular linking methods to improve the Barrat, Barthelemy, and Vespignani (BBV) model, suitable links are established for each new UAV, endowing the network with stronger resilience against random failures. Furthermore, considering the existing links and relative positions between UAVs, we formulate a system-wide link communication rate (SLCR) maximization problem. By dynamically adjusting the deployment positions of UAVs to maximize SLCR, the scheme ensures effective information exchange within the swarm, enhancing collaborative efficiency. The constructed network is scalable, allowing new UAVs to be seamlessly integrated without the need for cumbersome steps to rebuild the network topology. Evaluation and simulation results demonstrate that the proposed scheme constructs UAV networks with stronger resilience and better link communication rates compared to traditional BBV networks. © 2024 IEEE.

关键词： Information dissemination

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Gray code based gradient-free optimization algorithm for parameterized quantum circuit

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Chinese Physics B 2024年第2期33卷 189-194页

作者：张安琪武春辉赵生妹 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 Education) Nanjing University of Posts and TelecommunicationsNanjing 210003China

A Gray code based gradient-free optimization(GCO)algorithm is proposed to update the parameters of parameterized quantum circuits(PQCs)in this *** parameter of PQCs is encoded as a binary string,named as a gene,and a genetic-based method is adopted to select the *** individuals in the offspring are decoded in Gray code way to keep Hamming distance,and then are evaluated to obtain the best one with the lowest cost value in each *** algorithm is performed iteratively for all parameters one by one until the cost value satisfies the stop condition or the number of iterations is *** GCO algorithm is demonstrated for classification tasks in Iris and MNIST datasets,and their performance are compared by those with the Bayesian optimization algorithm and binary code based optimization *** simulation results show that the GCO algorithm can reach high accuracies steadily for quantum classification ***,the GCO algorithm has a robust performance in the noise environment.

关键词： gradient-free optimization Gray code genetic-based method

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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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Single-qubit quantum classifier based on gradient-free optimization algorithm

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Chinese Physics B 2023年第10期32卷 241-247页

作者：张安琪王可伦吴逸华赵生妹 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

A single-qubit quantum classifier(SQC)based on a gradient-free optimization(GFO)algorithm,named the GFO-based SQC,is proposed to overcome the effects of barren plateaus caused by quantum ***,a rotation gate R_(X)(φ)is applied on the single-qubit binary quantum classifier,and the training data and parameters are loaded intoφin the form of vector *** cost function is decreased by finding the value of each parameter that yields the minimum expectation value of measuring the quantum *** algorithm is performed iteratively for all parameters one by one until the cost function satisfies the stop *** proposed GFO-based SQC is demonstrated for classification tasks in Iris and MNIST datasets and compared with the Adam-based SQC and the quantum support vector machine(QSVM).Furthermore,the performance of the GFO-based SQC is discussed when the rotation gate in the quantum device is under different types of *** simulation results show that the GFO-based SQC can reach a high accuracy in reduced ***,the proposed GFO algorithm can quickly complete the training process of the ***,the GFO-based SQC has a good performance in noisy environments.

关键词： single-qubit quantum classifier gradient-free parameters optimizing barren plateau quantum noise

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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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Precision flatness measurement based on orbital angular momentum

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Chinese Physics B 2025年第5期34卷 415-422页

作者： Feifei Han Zhiwan Wang Le Wang Shengmei Zhao 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

We propose a method to measure the flatness of an object with a petal-like pattern generated by the interference of the measured orbital angular momentum(OAM)beam and the reference OAM beam which carries the opposite OAM *** calculating the difference between the petal rotation angle without/with the object,the thickness information of the object,and then the flatness information,can be ***,the direction of the object’s flatness can be determined by the petal’s clockwise/counterclockwise *** theoretically analyze the relationship between the object’s thickness and petal rotation angle,and verify the proposed method by *** experimental results show that the proposed method is a high precision flatness measurement and can obtain the convex/concave property of the *** the 1.02 mm glass sample,the mean deviation of the flatness is 1.357×10^(-8) and the variance is 0.242×10^(-16).For the 0.50 mm glass sample,the mean deviation of the flatness is 1.931×10^(-8) and the variance is 2.405×10^(-16).Two different topological charges are adopted for the 2.00 mm glass sample,and their flatness deviations are 0.239×10^(-8)(ℓ=1)and 0.246×10^(-8)(ℓ=2),where their variances are 0.799×10^(-18)(ℓ=1)and 0.775×10^(-18)(ℓ=2),*** is shown that the flatness measured by the proposed method is the same for the same sample when different topological charges are *** results indicate that the proposed method may provide a high flatness measurement,and will be a promising way to measure the flatness.

关键词： orbital angular momentum flatness measurement interference petal rotation angle

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Generation of orbital angular momentum hologram using a modified U-net

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Chinese Physics B 2024年第3期33卷 401-407页

作者：郑志刚韩菲菲王乐赵生妹 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 Education) Nanjing University of Posts and TelecommunicationsNanjing 210003China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210093China

Orbital angular momentum(OAM)holography has become a promising technique in information encryption,data storage and opto-electronic computing,owing to the infinite topological charge of one single OAM mode and the orthogonality of different OAM *** this paper,we propose a novel OAM hologram generation method based on a densely connected U-net(DCU),where the densely connected convolution blocks(DCB)replace the convolution blocks of the ***,the reconstruction process of the OAM hologram is integrated into DCU as its output layer,so as to eliminate the requirement to prepare training data for the OAM hologram,which is required by conventional neural networks through an iterative *** experimental and simulation results show that the OAM hologram can rapidly be generated with the well-trained DCU,and the reconstructed image's quality from the generated OAM hologram is significantly improved in comparison with those from the Gerchberg-Saxton generation method,the Gerchberg-Saxton based generation method and the U-net *** addition,a 10-bit OAM multiplexing hologram scheme is numerically demonstrated to have a high capacity with OAM hologram.

关键词： orbital angular momentum(OAM) holography OAM holography deep learning

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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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