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Outage Evaluation for STAR-RIS Assisted Satellite-UAV-Terrestrial NOMA networks With Imperfect CSI

IEEE Internet of Things Journal 2025年

作者： Luo, Wenwei Zhang, Jiliang Song, Jize Liu, Xin Xie, Yiyuan Xu, Jiayou Pan, Gaofeng Southwest University Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing School of Electronic and Information Engineering Chongqing400715 China Shanghai Institute of Satellite Engineering Shanghai201109 China Beijing Institute of Technology School of Cyberspace Security Beijing100081 China

Due to some transmission delays and channel estimation errors, it is always difficult to acquire perfect channel state information (CSI) in practical communications. Therefore, in this study, taking the practical scenario of imperfect CSI into consideration, a simultaneously transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS)-assisted satellite-unmanned aerial vehicle (UAV)-terrestrial non-orthogonal multiple access (NOMA) network is investigated, in which the UAV is assumed to be randomly located in a spherical cap space while the users are randomly located in an inner circular and an outer annular plane on the ground. Specifically, a satellite source first sends NOMA signals to a UAV relay, which then forwards them to both ground users via a STAR-RIS through reflection as well as transmission. In addition, considering that satellite-UAV link is subject to shadowed-Rician while other links are subject to Nakagami-m distribution, the cumulative distribution functions and probability density functions of channel gains in the presence of imperfect CSI are derived. Furthermore, in practical application scenarios, the positions of the UAV and both users may be randomly located in some regions. Therefore, three randomly distributed scenarios are considered: 1) The users are randomly located while the UAV's position is fixed;2) The UAV is randomly located while both users are in fixed locations;and 3) Both are randomly located. Under those three scenarios, both analytical and asymptotic expressions of outage probability (OP) for two users as well as the system OP are derived using the stochastic geometry approach, and their accuracy is confirmed with Monte-Carlo simulations. © 2014 IEEE.

关键词： Distribution functions

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High Contrast Ratio 4-to-2 Plasmonic Encoder Utilizing Cascaded Ring Resonators 6

High Contrast Ratio 4-to-2 Plasmonic Encoder Utilizing Casca...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Chen, Yan Xie, Yiyuan Chai, Junxiong Ye, Yichen College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing400715 China

ISBN: (纸本)9798350353594

In this paper, a novel plasmonic 4-to-2 encoder comprising cascaded ring resonators coated with graphene layers and two metal-insulator-metal (MIM) waveguides. The encoding function operates at 850 nm by modulating the intensity of pump beam focused on the graphene layer to manipulate the state of the plasmonic resonators. The performance of the encoder is analyzed utilizing the finite-difference time-domain method, demonstrating a high contrast ratio of 15.1 dB. Thus, this efficient 4-to-2 optical encoder is well-suited for integration into optical integrated computing devices. © 2024 IEEE.

关键词： Semiconductor insulator boundaries

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Impact of Crosstalk and Loss on the Training Process of Integrated Photonic Neural networks 6

Impact of Crosstalk and Loss on the Training Process of Inte...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Pang, Jing Su, Ye Xie, Yiyuan College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing400715 China

ISBN: (纸本)9798350353594

Integrated photonic neural networks (IPNNs), with their ability to process large-scale data and parallelize multiple computational tasks, have shown impressive promise for applications in the field of artificial intelligence. However, due to some hard-to-ignore non-ideal characteristics of the underlying silicon photonic MZI devices constituting the IPNN, these non-ideal characteristics can cause the input signal to be affected by crosstalk noise and optical loss during transmission, thus affecting the training and inference accuracy of the IPNN. In order to solve these problems, this paper establishes an analytical model of IPNN affected by crosstalk noise and loss during the training process, covering component level, layer level and system level. Simulations using this model show that the inference accuracy of IPNN decreases by about 65% when the crosstalk noise in the training phase of IPNN reaches -20 dB, while the inference accuracy of IPNN decreases by about 55% when the optical loss reaches -0.5 dB. © 2024 IEEE.

关键词： Crosstalk

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Sparse random compressive sensing based data aggregation in wireless sensor networks

Sparse random compressive sensing based data aggregation in ...

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作者： Yin, Li Liu, Cuiye Guo, Songtao Yang, Yuanyuan Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY11794 United States

In wireless sensor networks (WSNs), the volume of data is increasing at an unpredictable rate, which inevitably leads to high spatial-temporal correlation. To eliminate data redundancy, some researchers have proposed many data aggregation methods. However, a few of aggregation approaches can handle energy consumption and latency simultaneously. Therefore, in this paper, we propose an efficient algorithm, called Delay-Minimum Energy-Balanced (DMEB) data aggregation, which benefits from the superiority of the sparse random measurement matrix and minimum delay algorithm. Owing to the sparsity characteristics of the measurement matrix, only the nodes whose corresponding elements in the matrix are non-zero take part in the measurement. Each measurement can form an aggregation tree with minimum delay. After a sink node receives all the measurements, original readings can be recovered precisely. In addition, we adopt a novel scheduling method to avoid information interference. Experiment results demonstrate that, under recovering the original data accurately, the proposed data aggregation algorithm can not only shorten delay in data collection process but also reduce communication cost and prolong network lifetime during data transmission process. © 2018 John Wiley & Sons, Ltd.

关键词： Compressed sensing

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Multi-criteria collaborative filtering based on matrix filling 4

Multi-criteria collaborative filtering based on matrix filli...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Ye, Li Wu, Chunming Qiang, Baohua Xie, Wu College of Computer Information Science Southwest University Chongqing China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781538683392

In multi-criteria collaborative filtering, the data sparsity is a critical factor affecting the effectiveness of the algorithm. Existing solutions generally remove low-efficiency data through dimensionality reduction, decomposition, etc. While at lower data dimension, they also lose original data information, which impacts the accuracy of predicted ratings. This paper adopts a new processing strategy and proposes to use the matrix filling method to handle the missing data. Firstly, an improved similarity algorithm combined with the Jaccard Similarity Coefficient is used to calculate the predicted rating. Secondly, the Reliable Factor is introduced to prevent the error of filling data. Finally, multiple linear regression is employed for aggregating the ratings of the criteria to eventually yield preferred predicted ratings. A series of experiments show that the proposed method can precisely reflect the user's similarity and achieve better results in predicted ratings, in contrast to traditional methods. © 2018 IEEE.

关键词： Collaborative filtering

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Reliability Analysis of Optical Neural networks with Non-Ideal Signal Transmission

SSRN

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SSRN 2024年

作者： Xie, Yiyuan Fu, Pengju Su, Ye Ye, Yichen Chai, Junxiong Jiang, Xiao Yang, Hongyu College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China Key Laboratory of Networks and Cloud Computing Security of Universities Chongqing China

Machine learning plays a significant role in various fields. As a fundamental part of machine learning, matrix operations are one of the most important computational parts of neural networks. However, the advancement of Moore’s law is hindered by limitations in the number and size of transistors on electronic chips. Therefore, the implementation of optical neural network (ONN) using photonic devices to accomplish matrix-vector multiplication (MVM) has attracted significant attention. Compared with traditional electrical on neural networks, ONN exhibits faster computational speed, higher parallel input capability and greater energy efficiency. Nevertheless, crosstalk and signal loss are inevitable characteristics in optical signal transmission, which can degrade the performance of ONN. In this paper, we present analysis models and simulations to assess the effects of crosstalk and loss on optical computing systems. We have observed that the accuracy of ONN degrades when crosstalk and loss are present. In severe cases, ONN is unable to complete the classification task. To mitigate the interference of crosstalk, we have designed a novel double-layer structure to reduce the number of optical devices required for signal transmission and optimize the impact of crosstalk on ONN performance. For the identical classification task, the ONN based on a double-layer structure exhibits better interference immunity than the generic ONN, with a 10% performance improvement. In particular, performance is improved by 50% when only crosstalk is considered. © 2024, The Authors. All rights reserved.

关键词： Crosstalk

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High Contrast Ratio 4-to-2 Plasmonic Encoder Utilizing Cascaded Ring Resonators

High Contrast Ratio 4-to-2 Plasmonic Encoder Utilizing Casca...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Yan Chen Yiyuan Xie Junxiong Chai Yichen Ye College of Electronic and Information Engineering Southwest University Chongqing China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

关键词： Integrated optics Optical switches Graphene Plasmons Optical pumping Encoding Optical modulation Optical ring resonators Optical devices Finite difference methods

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Reliability Analysis of Optical Neural networks with Non-Ideal Signal Transmission

SSRN

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SSRN 2024年

作者： Fu, Pengju Xie, Yiyuan Su, Ye Ye, Yichen Chai, Junxiong Jiang, Xiao Yang, Hongyu College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China Key Laboratory of Networks and Cloud Computing Security of Universities Chongqing China

关键词： Crosstalk

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Impact of Crosstalk and Loss on the Training Process of Integrated Photonic Neural networks

Impact of Crosstalk and Loss on the Training Process of Inte...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Jing Pang Ye Su Yiyuan Xie College of Electronic and Information Engineering Southwest University Chongqing People's Republic of China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing People's Republic of China Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing People's Republic of China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

关键词： Training Optical losses Optical interferometry Accuracy Neural networks Crosstalk Optical computing Optical crosstalk Optical noise Optical devices

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RBDN: Residual Bottleneck Dense Network for Image Super-Resolution

Research Square

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Research Square 2021年

作者： An, Zeyu Zhang, Junyuan Sheng, Ziyu Er, Xuanhe Lv, Junjie College of Electronics and Information Engineering Southwest University Chongqing400715 China College of Software Engineering Chengdu University of Information Technology Chengdu610200 China The Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing400715 China

Recent studies have shown that Super-Resolution Generative Adversarial Network (SRGAN) can significantly improve the quality of single-image super-resolution. However, the existing SRGAN approaches also have drawbacks, such as inadequate of features utilization, huge number of parameters and poor scalability. To further enhance the visual quality, we thoroughly study three key components of SRGAN: network architecture, adversarial loss and perceptual loss, and propose a DenseNet with Residual-in-Residual Bottleneck Block (RRBB) named as Residual Bottleneck Dense Network (RBDN) for single-image super-resolution. In particular, RBDN combines ResNet and DenseNet with different roles, in which ResNet refines feature values by addition and DenseNet memorizes feature values by concatenation. Specifically, the DenseNet adopts the Bottleneck structure to reduce the network parameters and improve the convergence rate. In addition, the proposed RRBB, as the basic network building unit, removes the batch normalization (BN) layer and employs the ELU function to reduce the opposite effects in the absence of BN. In this way, RBDN can enjoy the merits of the sufficient feature value refined by residual groups and the refined feature value memorized by dense connections, thus achieving better performance compared with most current residual blocks. © 2021, CC BY.

关键词： Generative adversarial networks

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