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Robust Resource Allocation for STAR-RIS Assisted SWIPT Systems

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arXiv 2024年

作者： Zhu, Guangyu Mu, Xidong Guo, Li Huang, Ao Xu, Shibiao The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The Engineering Research Center Ministry of Education for Chain Network Convergence Technology China The National Engineering Research Center for Mobile Internet Security Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is proposed. More particularly, an STAR-RIS is deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), where two practical STAR-RIS operating protocols, namely energy splitting (ES) and time switching (TS), are employed. Under the imperfect channel state information (CSI) condition, a multi-objective optimization problem (MOOP) framework, that simultaneously maximizes the minimum data rate and minimum harvested power, is employed to investigate the fundamental rate-energy trade-off between IUs and EUs. To obtain the optimal robust resource allocation strategy, the MOOP is first transformed into a single-objective optimization problem (SOOP) via the ǫ-constraint method, which is then reformulated by approximating semi-infinite inequality constraints with the S-procedure. For ES, an alternating optimization (AO)-based algorithm is proposed to jointly design AP active beamforming and STAR-RIS passive beamforming, where a penalty method is leveraged in STAR-RIS beamforming design. Furthermore, the developed algorithm is extended to optimize the time allocation policy and beamforming vectors in a two-layer iterative manner for TS. Numerical results reveal that: 1) deploying STAR-RISs achieves a significant performance gain over conventional RISs, especially in terms of harvested power for EUs;2) the ES protocol obtains a better user fairness performance when focusing only on IUs or EUs, while the TS protocol yields a better balance between IUs and EUs;3) the imperfect CSI affects IUs more significantly than EUs, whereas TS can confer a more robust design to attenuate these effects. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

Large Generative Model Assisted 3D Semantic Communication

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arXiv 2024年

作者： Jiang, Feibo Peng, Yubo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua You, Xiaohu Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China The Department of Computer Science Brunel University London United Kingdom The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Frontiers Science Center for Mobile Information Communication and Security National Mobile Communications Research Laboratory Southeast University Nanjing China The Purple Mountain Laboratories Nanjing China

Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction;2) Latent semantic redundancy;and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. Firstly, we introduce a 3D Semantic Extractor (3DSE), which employs generative AI models, including Segment Anything Model (SAM) and Neural Radiance Field (NeRF), to extract key semantics from a 3D scenario based on user requirements. The extracted 3D semantics are represented as multi-perspective images of the goal-oriented 3D object. Then, we present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images, in which we use a semantic encoder with two output heads to perform semantic encoding and mask redundant semantics in the latent semantic space, respectively. Next, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State Information (CSI) of physical channels. Finally, simulation results demonstrate the advantages of the proposed GAM-3DSC system in effectively transmitting the goal-oriented 3D scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

BaKGraSTeC: A Background Knowledge Graph Based Method for Short Text Classification

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BaKGraSTeC: A Background Knowledge Graph Based Method for Sh...

IEEE International Conference on Big Knowledge (ICBK)

作者： Xuhui Jiang Yinghan Shen Yuanzhuo Wang Xiaolong Jin Xueqi Cheng CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Short text classification is an important task in the area of natural language processing. Recent studies attempt to employ external knowledge to improve classification performance, but they ignore the correlation between external knowledge and have poor interpretability. This paper proposes a novel Background Knowledge Graph based method for Short Text Classification called BaKGraSTeC for short, which can not only employ external knowledge from a knowledge graph to enrich text information, but also utilize its structural information through a graph neural network to promote the understanding of texts. Specifically, we construct a background knowledge graph based on training data, then we propose a novel architecture that integrates background knowledge graph into a graph neural network to model and capture implicit interactions between its concepts and classes. Besides, we propose an attention mechanism considering both similarity and co-occurrence between concepts and classes to identify the informative concepts in texts. Our experimental results demonstrate the effectiveness with good interpretability of BaKGraSTeC through using external knowledge and their structural information for short text classification.

关键词： Knowledge engineering Neural networks Task analysis Semantics Natural language processing Syntactics Machine learning

Threat-Based Declassification and Endorsement for Mobile computing

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Chinese Journal of Electronics 2019年第5期28卷 1041-1052页

作者： YIN Lihua GUO Yunchuan ZHANG Huibing HUANG Wenting FANG Binxing Cyberspace Institute of Advanced Technology Guangzhou University The State Key Laboratory of Information Security Institute of Information Engineering CAS Guangxi Key Lab of Trusted Software Guilin University of Electronic Technology The National Computer Network Emergency Response Technical Team/Coordination Center of China

Declassification and endorsement can efficiently improve the usability of mobile ***, both declassify and endorse operations in practice are often ad-hoc and nondeterministic, thus, being insecure. From a new perspective of threat assessments,we propose the Threat-based typed security π-calculus(πTBTS) to model declassification and endorsement in mobile computing. Intuitively, when relaxing confidentiality policies and/or integrity policies, we respectively assess threats brought by performing these two relaxes. If these threats are acceptable, the declassification and/or endorsement operations are permitted; Otherwise, they are denied. The proposed assessments have explicit security conditions, results and less open parameters,so our approach solves the problem of the ad-hoc and nondeterministic semantics and builds a bridge between threat assessments and declassification/endorsement.

关键词： Declassification Endorsement Threat assessment Mobile computing π-Calculus

Critical (P5, W4)-Free Graphs

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arXiv 2025年

作者： Xia, Wen Jooken, Jorik Goedgebeur, Jan Beaton, Iain Cameron, Ben Huang, Shenwei College of Computer Science Nankai University Tianjin300071 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China Department of Computer Science KU Leuven Campus Kulak-Kortrijk Kortrijk8500 Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium Department of Mathematics and Statistics Acadia University WolfvilleNS Canada School of Mathematical and Computational Sciences University of Prince Edward Island CharlottetownPE Canada

A graph G is k-vertex-critical if χ(G) = k but χ(G − v) 1, H2)-free if it contains no induced subgraph isomorphic to H1 nor H2. A W4 is the graph consisting of a C4 plus an additional vertex adjacent to all the vertices of the C4. We show that there are finitely many k-vertex-critical (P5, W4)-free graphs for all k ≥ 1 and we characterize all 5-vertex-critical (P5, W4)-free graphs. Our results imply the existence of a polynomial-time certifying algorithm to decide the k-colorability of (P5, W4)-free graphs for each k ≥ 1 where the certificate is either a k-coloring or a (k + 1)-vertex-critical induced subgraph. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph algorithms

Estimating wetland CH4 emissions on the Qinghai-Tibetan Plateau based on a processed model coupled with inundation dynamics between 1960 and 2100

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Science of the Total Environment 2024年 955卷 177169页

作者： Zhang, Jiang Chen, Huai Peng, Changhui Huang, Yuanyuan Wang, Yike Liu, Xinwei Wang, Meng Wang, Le Yu, Dongxue Zhu, Qiuan Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Chengdu Institute of Biology Chinese Academy of Sciences Chengdu610041 China Institute of Environment Sciences Department of Biology Sciences University of Quebec at Montreal Case Postale 8888 Succursale Centre-Ville MontrealQCH3C 3P8 Canada School of Geography Science Hunan Normal University Changsha410081 China Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains Ministry of Education School of Geographical Sciences Northeast Normal University Changchun130024 China Key Laboratory of Soil and Water Processes in Watershed College of Geography and Remote Sensing Hohai University Nanjing210098 China National Earth System Science Data Center National Science & Technology Infrastructure of China Beijing100101 China

Quantifying the impacts of climate change and wetland area dynamics on methane (CH4) emissions in the Qinghai-Tibetan Plateau (QTP) is crucial for enhancing our understanding of regional and global CH4 budgets. However, a major source of uncertainty in estimating wetland methane emissions stems from the limited delineation of wetland area dynamics. In this study, we integrated dynamic wetland inundation schemes into a process-based model to quantify the contributions of wetland area variations to natural wetland CH4 emissions on the QTP, both historically and in the future. The results revealed that climate change significantly influenced the spatiotemporal distribution of wetland CH4 emissions, with wetland inundation dynamics contributing between 21.84 % and 27.18 % of the increase in CH4 emissions from 1960 to 2020. Under future climate change scenarios, wetland CH4 emissions are projected to exhibit a more pronounced increasing trend, with wetland inundation dynamics projected to contribute 18.94 % to 21.80 % of the increase in CH4 emissions in the QTP under seven Shared Socioeconomic Pathways scenarios. © 2024 Elsevier B.V.

关键词： Greenhouse gas emissions

Decoding error probability of random parity-check matrix ensemble over the erasure channel

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arXiv 2021年

作者： Chan, Chin Hei Fu, Fang-Wei Xiong, Maosheng Mathematics Department Hong Kong University of Science and Technology Clear Water Bay Hong Kong Chern Institute of Mathematics and LPMC The Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China

Using tools developed in a work by Shen and the second author, in this paper we carry out an in-depth study on the average decoding error probability of the random parity-check matrix ensemble over the erasure channel under three decoding principles, namely unambiguous decoding, maximum likelihood decoding and list decoding. We obtain explicit formulas for the average decoding error probabilities of the random parity-check matrix ensemble under these three decoding principles and compute the error exponents. Moreover, for unambiguous decoding, we compute the variance of the decoding error probability of the random parity-check matrix ensemble and the error exponent of the variance, which implies a strong concentration result, that is, roughly speaking, the ratio of the decoding error probability of a random linear code in the ensemble and the average decoding error probability of the ensemble converges to 1 with high probability when the code length goes to *** Codes 94A40, 94B70 Copyright © 2021, The Authors. All rights reserved.

关键词： Decoding

Detection and evaluation of abnormal user behavior based on quantum generation adversarial network

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arXiv 2022年

作者： Pan, Minghua Wang, Bin Tao, Xiaoling Zheng, Shenggen Situ, Haozhen Li, Lvzhou Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China Peng Cheng Laboratory Shenzhen518055 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

Quantum computing holds tremendous potential for processing high-dimensional data, capitalizing on the unique capabilities of superposition and parallelism within quantum states. As we navigate the noisy intermediate-scale quantum (NISQ) era, the exploration of quantum computing applications has emerged as a compelling frontier. One area of particular interest within the realm of cyberspace security is Behavior Detection and Evaluation (BDE). Notably, the detection and evaluation of internal abnormal behaviors pose significant challenges, given their infrequent occurrence or even their concealed nature amidst vast volumes of normal data. In this paper, we introduce a novel quantum behavior detection and evaluation algorithm (QBDE) tailored for internal user analysis. The QBDE algorithm comprises a Quantum Generative Adversarial network (QGAN) in conjunction with a classical neural network for detection and evaluation tasks. The QGAN is built upon a hybrid architecture, encompassing a Quantum Generator (GQ) and a Classical Discriminator (DC). GQ, designed as a parameterized quantum circuit (PQC), collaborates with DC, a classical neural network, to collectively enhance the analysis process. To address the challenge of imbalanced positive and negative samples, GQ is employed to generate negative samples. Both GQ and DC are optimized through gradient descent techniques. Through extensive simulation tests and quantitative analyses, we substantiate the effectiveness of the QBDE algorithm in detecting and evaluating internal user abnormal behaviors. Our work not only introduces a novel approach to abnormal behavior detection and evaluation but also pioneers a new application scenario for quantum algorithms. This paradigm shift underscores the promising prospects of quantum computing in tackling complex cybersecurity challenges. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

Uncertainty calibration for ensemble-based debiasing methods 21

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Uncertainty calibration for ensemble-based debiasing methods

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Ruibin Xiong Yimeng Chen Liang Pang Xueqi Cheng Zhiming Ma Yanyan Lan CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences and Baidu Inc. University of Chinese Academy of Sciences and Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences and Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Institute for AI Industry Research Tsinghua University

ISBN: (纸本)9781713845393

Ensemble-based debiasing methods have been shown effective in mitigating the reliance of classifiers on specific dataset bias, by exploiting the output of a bias-only model to adjust the learning target. In this paper, we focus on the bias-only model in these ensemble-based methods, which plays an important role but has not gained much attention in the existing literature. Theoretically, we prove that the debiasing performance can be damaged by inaccurate uncertainty estimations of the bias-only model. Empirically, we show that existing bias-only models fall short in producing accurate uncertainty estimations. Motivated by these findings, we propose to conduct calibration on the bias-only model, thus achieving a three-stage ensemble-based debiasing framework, including bias modeling, model calibrating, and debiasing. Experimental results on NLI and fact verification tasks show that our proposed three-stage debiasing framework consistently outperforms the traditional two-stage one in out-of-distribution accuracy.

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