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A NOMA Power Allocation Strategy Based on Genetic Algorithm 8th

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A NOMA Power Allocation Strategy Based on Genetic Algorithm

8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019

作者： Yin, Lu Chenggong, Wang Kai, Mao Kuanxin, Bao Haowei, Bian Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Nanjing University of Posts and Telecommunications Ministry of Education Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789811394089

The NOMA technology uses the power domain non-orthogonal multiplexing method to enable multiple users to occupy the entire frequency band simultaneously to transmit signals. In order to maximize the total transmission rate of the system, an effective method is to use the genetic algorithm for NOMA power allocation. In this paper, the NOMA downlink system model is constructed, and the objective function and constraints are analyzed. A NOMA power allocation strategy based on genetic algorithm is proposed. The algorithm distributes user power based on the criterion of maximizing total transmission rate, therefore the algorithm has random search capabilities and relatively low search complexity. The simulation results show that when the system transmits power or multiplexed users is fixed the proposed algorithm outperforms the fixed power allocation algorithm in the total transmission rate. The total system transmission rate of the genetic algorithm is similar to the full space search algorithm. As the number of multiplexed users increases, the computational complexity of genetic algorithms is much lower than that of full-space search algorithms. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Computational complexity

Data Anonymous Authentication for BIoMT with Proxy Group Signature

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Journal of Cyber Security 2021年第4期3卷 207-216页

作者： Chaoyang Li Yalan Wang Gang Xu Xiubo Chen Xiangjun Xin Jian Li College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450001China Department of Computer Science University of SurreyGuildfordSurreyGU27XHUK School of Computing Science and Technology North China University of TechnologyBeijing100144China Information Security Centre State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China

Along with the increase of wearable medical device,the privacy leakage problem in the process of transmission between these edge medical *** blockchain-enabled Internet of Medical Things(BIoMT)has been developed to reform traditional centralized medical system in recent *** paper first introduces a data anonymous authentication model to protect user privacy and medical data in ***,a proxy group signature(PGS)scheme has been proposed based on lattice *** scheme can well satisfy the anonymous authentication demand for the proposed model,and provide anti-quantum attack security for BIoMT in the future general quantum computer ***,the security analysis shows this PGS scheme is secure against the dynamical-almost-full anonymous and *** efficiency comparison shows the proposed model and PGS scheme is more efficient and practical.

关键词： Blockchain-enabled Internet of Medical Things anonymous authentication proxy group signature medical data

Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems

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arXiv 2020年

作者： Hu, Qiyu Cai, Yunlong Shi, Qingjiang Xu, Kaidi Yu, Guanding Ding, Zhi College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Software Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering University of California DavisCA95616 United States

Optimization theory assisted algorithms have received great attention for precoding design in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant optimization algorithms are able to provide excellent performance, they generally require considerable computational complexity, which gets in the way of their practical application in real-time systems. In this work, in order to address this issue, we first propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed in matrix form to better solve the problems in communication systems. Then, we implement the proposed deep-unfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. Specifically, the iterative WMMSE algorithm is unfolded into a layer-wise structure, where a number of trainable parameters are introduced to replace the high-complexity operations in the forward propagation. To train the network, a generalized chain rule of the IAIDNN is proposed to depict the recurrence relation of gradients between two adjacent layers in the back propagation. Moreover, we discuss the computational complexity and generalization ability of the proposed scheme. Simulation results show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

Correction to: Feature selection and risk prediction for patients with coronary artery disease using data mining

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Medical & biological engineering & computing 2022年第3期60卷 887页

作者： Nashreen Md Idris Yin Kia Chiam Kasturi Dewi Varathan Wan Azman Wan Ahmad Kok Han Chee Yih Miin Liew Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia. Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia. yinkia@um.edu.my. Department of Information Systems Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia. Department of Medicine University Malaya Medical Centre 50603 Kuala Lumpur Malaysia. Department of Biomedical Engineering Faculty of Engineering Universiti Malaya 50603 Kuala Lumpur Malaysia.

Recommender systems based on generative adversarial networks: a problem-driven perspective

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arXiv 2020年

作者： Gao, Min Zhang, Junwei Yu, Junliang Li, Jundong Wen, Junhao Xiong, Qingyu Department of Electrical and Computer Engineering Department of Computer Science School of Data Science University of Virginia Charlottesville22904 United States School of Information Technology and Electrical Engineering University of Queensland QLD4072 Australia School of Big Data and Software Engineering Chongqing University Chongqing401331 China

Recommender systems (RS) play a very important role in various aspects of people’s online life. Many companies leverage RS to help users discover new and favored items. Despite their empirical success, these systems still suffer from two main problems: data noise and data sparsity. In recent years, Generative Adversarial Networks (GANs) have received a surge of interests in many fields because of their great potential to learn complex real data distribution, and they also provide new means to mitigate the aforementioned problems of RS. Particularly, owing to adversarial learning, the problem of data noise can be handled by adding adversarial perturbations or forcing discriminators to tell the informative and uninformative data examples apart. As for the mitigation of data sparsity issue, the GAN-based models are able to replicate the real distribution of the user-item interactions and augment the available data. To gain a comprehensive understanding of these GAN-based recommendation models, we provide a retrospective of these studies and organize them from a problem-driven perspective. Specifically, we propose a taxonomy of these models, along with a detailed description of them and their advantages. Finally, we elaborate on several open issues and expand on current trends in the GAN-based RS. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

Transferring knowledge distillation for multilingual social event detection

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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

Federated learning in network traffic classification: Taxonomy of implementation, application, and impact on sixth-generation wireless networks

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engineering Applications of Artificial Intelligence 2025年 158卷

作者： Azizi Ariffin Firdaus Afifi Faiz Zaki Hazim Hanif Nor Badrul Anuar Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21030 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia Centre of Research for Cyber Security and Network (CSNET) Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia Institute of Informatics & Computing in Energy Universiti Tenaga Nasional Putrajaya Kampus 43000 Kajang Selangor Malaysia

Objectives Network traffic classification (NTC) is crucial for network management. However, the surge in Internet traffic poses scalability and privacy issues. Researchers are turning to federated learning (FL) to tackle these issues. Despite the importance of FL, there is lack of literature that comprehensively reviews its implementation and application within NTC and its impact on Sixth-Generation Wireless (6G) networks. Current surveys cover the technical aspects of NTC without fully addressing the integration of FL and its broader implications. This study aims to review the technical implementation of FL in NTC and its application to various network-related areas, including 6G. Methods This study presents a taxonomy for FL implementation in NTC, considering aspects such as learning and aggregation approaches, topology, and client operations. It examines the limitations of these elements and their effects on performance, efficiency, scalability, and their impact on 6G. This study outlines a taxonomy for FL applications, focusing on privacy preservation, scalable classification, and shared security intelligence. Novelty The proposed taxonomy provides insights into research landscape and highlights its limitations. The analysis of the impact of FL-based NTC on 6G provides insight into its integration and implementation challenges. This study discusses open issues and advocates for future research directions in FL for NTC, including 6G. Findings The study identifies areas needing improvement such as privacy, addressing security, single-point-of-failure, hardware limitations, delays and heterogeneity concerns. The findings of this paper show that an optimal implementation approach is essential to cater for heterogeneity and real-time requirements of the network environment.

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Geometry Constrained Weakly Supervised Object Localization

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arXiv 2020年

作者： Lu, Weizeng Jia, Xi Xie, Weicheng Shen, Linlin Zhou, Yicong Duan, Jinming Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China School of Computer Science University of Birmingham United Kingdom Department of Computer and Information Science University of Macau China

We propose a geometry constrained network, termed GC-Net, for weakly supervised object localization (WSOL). GC-Net consists of three modules: a detector, a generator and a classifier. The detector predicts the object location defined by a set of coefficients describing a geometric shape (i.e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator. The classifier takes the resulting masked images as input and performs two complementary classification tasks for the object and background. To make the mask more compact and more complete, we propose a novel multi-task loss function that takes into account area of the geometric shape, the categorical cross-entropy and the negative entropy. In contrast to previous approaches, GC-Net is trained end-to-end and predict object location without any post-processing (e.g. thresholding) that may require additional tuning. Extensive experiments on the CUB-200-2011 and ILSVRC2012 datasets show that GC-Net outperforms state-of-the-art methods by a large margin. Our source code is available at https://***/lwzeng/GC-Net. Copyright © 2020, The Authors. All rights reserved.

关键词： Geometry

Average-case algorithms for testing isomorphism of polynomials, algebras, and multilinear forms

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arXiv 2020年

作者： Grochow, Joshua A. Qiao, Youming Tang, Gang Departments of Computer Science and Mathematics University of Colorado—Boulder BoulderCO80309-0430 United States Centre for Quantum Software and Information School of Computer Science Faculty of Engineering and Information Technology University of Technology SydneyNSW2150 Australia

We study the problems of testing isomorphism of polynomials, algebras, and multilinear forms. Our first main results are average-case algorithms for these problems. For example, we develop an algorithm that takes two cubic forms f, g ∈ Fq[x1, . . ., xn], and decides whether f and g are isomorphic in time qO(n) for most f . This average-case setting has direct practical implications, having been studied in multivariate cryptography since the 1990s. Our second result concerns the complexity of testing equivalence of alternating trilinear forms. This problem is of interest in both mathematics and cryptography. We show that this problem is polynomial-time equivalent to testing equivalence of symmetric trilinear forms, by showing that they are both Tensor Isomorphism-complete (Grochow & Qiao, ITCS, 2021), therefore is equivalent to testing isomorphism of cubic forms over most fields. Copyright © 2020, The Authors. All rights reserved.

关键词： Cryptography