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Efficient CI-Based One-Bit Precoding for Multiuser Downlink Massive MIMO systems with PSK Modulation

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arXiv 2021年

作者： Wu, Zheyu Jiang, Bo Liu, Ya-Feng Shao, Mingjie Dai, Yu-Hong The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Ministry of Education Key Laboratory for NSLSCS School of Mathematical Sciences Nanjing Normal University Nanjing210023 China The School of Information Science and Engineering Shandong University Qingdao China

In this paper, we consider the one-bit precoding problem for the multiuser downlink massive multiple-input multiple-output (MIMO) system with phase shift keying (PSK) modulation. We focus on the celebrated constructive interference (CI)-based problem formulation. We first establish the NP-hardness of the problem (even in the single-user case), which reveals the intrinsic difficulty of globally solving the problem. Then, we propose a novel negative 1 penalty model for the considered problem, which penalizes the one-bit constraint into the objective by a negative 1-norm term, and show the equivalence between (global and local) solutions of the original problem and the penalty problem when the penalty parameter is sufficiently large. We further transform the penalty model into an equivalent min-max problem and propose an efficient alternating proximal/projection gradient descent ascent (APGDA) algorithm for solving it, which performs a proximal gradient decent over one block of variables and a projection gradient ascent over the other block of variables alternately. The APGDA algorithm enjoys a low per-iteration complexity and is guaranteed to converge to a stationary point of the min-max problem and a local minimizer of the penalty problem. To further reduce the computational cost, we also propose a low-complexity implementation of the APGDA algorithm, where the values of the variables will be fixed in later iterations once they satisfy the one-bit constraint. Numerical results show that, compared to the state-of-the-art CI-based algorithms, both of the proposed algorithms generally achieve better bit-error-rate (BER) performance with lower computational cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

Augmenting photoluminescence of monolayer MoS_(2) using high order modes in a metal dimer-on-film nanocavity

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Photonics Research 2021年第4期9卷 501-506页

作者： Shiyin Cao Liping Hou Qifa Wang Chenyang Li Weixing Yu Xuetao Gan Kaihui Liu Malin Premaratne Fajun Xiao Jianlin Zhao MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical UniversityXi’an 710129China Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences Xi’an 710119China State Key Laboratory for Mesoscopic Physics Collaborative Innovation Centre of Quantum MatterSchool of PhysicsPeking UniversityBeijing 100871China Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems EngineeringMonash UniversityClaytonVIC 3800Australia

Plasmonic particle-on-film nanocavities,supporting gap modes with ultra-small volume,provide a great solution to boost light–matter interactions at the *** this work,we report on the photoluminescence(PL)enhancement of monolayer MoS_(2) using high order modes of an Au nanosphere dimer-on-film nanocavity(DoFN).The high order plasmon modes,consisting of two bonding quadrupoles in the dimer and their images in the Au film,are revealed by combining the polarization-resolved scattering spectra with the numerical *** integrating the monolayer MoS_(2) into the DoFN,these high order modes are used to enhance PL intensity through simultaneously boosting the absorption and emission processes,producing a 1350-fold enhancement *** opens an avenue to enhance the light–matter interaction with high order plasmon modes and may find applications in future optoelectronics and nanophotonics devices.

关键词： cavity high integrating

A Weighted Byzantine Fault Tolerance Consensus Driven Trusted Multiple Large Language Models Network

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arXiv 2025年

作者： Luo, Haoxiang Sun, Gang Liu, Yinqiu Zhao, Dongcheng Niyato, Dusit Yu, Hongfang Dustdar, Schahram School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computing and Data Science Nanyang Technological University 639798 Singapore Pengcheng Laboratory Shenzhen518055 China Distributed Systems Group TU Wien Vienna1040 Austria ICREA Universitat Pompeu Fabra Barcelona08002 Spain

Large Language Models (LLMs) have achieved remarkable success across a wide range of applications. However, individual LLMs often produce inconsistent, biased, or hallucinated outputs due to limitations in their training corpora and model architectures. Recently, collaborative frameworks such as the Multi-LLM Network (MultiLLMN) have been introduced, enabling multiple LLMs to interact and jointly respond to user queries. Nevertheless, MultiLLMN architectures raise critical concerns regarding the reliability and security of the generated content, particularly in open environments where malicious or compromised LLMs may be present. Moreover, reliance on centralized coordination undermines system efficiency and introduces single points of failure. In this paper, we propose a novel Trusted MultiLLMN framework, driven by a Weighted Byzantine Fault Tolerance (WBFT) blockchain consensus mechanism, to ensure the reliability, security, and efficiency of multi-LLM collaboration. In WBFT, voting weights are adaptively assigned to each LLM based on its response quality and trustworthiness, incentivizing reliable behavior, and reducing the impact of malicious nodes. Extensive simulations demonstrate that WBFT significantly improves both consensus security and efficiency compared to classical and modern consensus mechanisms, particularly under wireless network conditions. Furthermore, our evaluations reveal that Trusted MultiLLMN supported by WBFT can deliver higher-quality and more credible responses than both single LLMs and conventional MultiLLMNs, thereby providing a promising path toward building robust, decentralized AI collaboration networks. © 2025, CC0.

关键词： Fault tolerance

Demonstrating a Long-Coherence Dual-Rail Erasure Qubit Using Tunable Transmons

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Physical Review X 2024年第1期14卷 011051-011051页

作者： H. Levine A. Haim J. S. C. Hung N. Alidoust M. Kalaee L. DeLorenzo E. A. Wollack P. Arrangoiz-Arriola A. Khalajhedayati R. Sanil H. Moradinejad Y. Vaknin A. Kubica D. Hover S. Aghaeimeibodi J. A. Alcid C. Baek J. Barnett K. Bawdekar P. Bienias H. A. Carson C. Chen L. Chen H. Chinkezian E. M. Chisholm A. Clifford R. Cosmic N. Crisosto A. M. Dalzell E. Davis J. M. D’Ewart S. Diez N. D’Souza P. T. Dumitrescu E. Elkhouly M. T. Fang Y. Fang S. Flammia M. J. Fling G. Garcia M. K. Gharzai A. V. Gorshkov M. J. Gray S. Grimberg A. L. Grimsmo C. T. Hann Y. He S. Heidel S. Howell M. Hunt J. Iverson I. Jarrige L. Jiang W. M. Jones R. Karabalin P. J. Karalekas A. J. Keller D. Lasi M. Lee V. Ly G. MacCabe N. Mahuli G. Marcaud M. H. Matheny S. McArdle G. McCabe G. Merton C. Miles A. Milsted A. Mishra L. Moncelsi M. Naghiloo K. Noh E. Oblepias G. Ortuno J. C. Owens J. Pagdilao A. Panduro J.-P. Paquette R. N. Patel G. Peairs D. J. Perello E. C. Peterson S. Ponte H. Putterman G. Refael P. Reinhold R. Resnick O. A. Reyna R. Rodriguez J. Rose A. H. Rubin M. Runyan C. A. Ryan A. Sahmoud T. Scaffidi B. Shah S. Siavoshi P. Sivarajah T. Skogland C.-J. Su L. J. Swenson J. Sylvia S. M. Teo A. Tomada G. Torlai M. Wistrom K. Zhang I. Zuk A. A. Clerk F. G. S. L. Brandão A. Retzker O. Painter AWS Center for Quantum Computing Pasadena California 91125 USA Institute of Applied Physics The Hebrew University of Jerusalem Jerusalem 91904 Givat Ram Israel Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem 91904 Givat Ram Israel Centre for Engineered Quantum Systems School of Physics The University of Sydney Sydney New South Wales 2006 Australia Pritzker School of Molecular Engineering University of Chicago Chicago Illinois 60637 USA Institute for Quantum Information and Matter California Institute of Technology Pasadena California 91125 USA Department of Physics California Institute of Technology Pasadena California 91125 USA Thomas J. Watson Sr. Laboratory of Applied Physics and Kavli Nanoscience Institute California Institute of Technology Pasadena California 91125 USA

Quantum error correction with erasure qubits promises significant advantages over standard error correction due to favorable thresholds for erasure errors. To realize this advantage in practice requires a qubit for which nearly all errors are such erasure errors, and the ability to check for erasure errors without dephasing the qubit. We demonstrate that a “dual-rail qubit” consisting of a pair of resonantly coupled transmons can form a highly coherent erasure qubit, where transmon T1 errors are converted into erasure errors and residual dephasing is strongly suppressed, leading to millisecond-scale coherence within the qubit subspace. We show that single-qubit gates are limited primarily by erasure errors, with erasure probability perasure=2.19(2)×10−3 per gate while the residual errors are ∼40 times lower. We further demonstrate midcircuit detection of erasure errors while introducing <0.1% dephasing error per check. Finally, we show that the suppression of transmon noise allows this dual-rail qubit to preserve high coherence over a broad tunable operating range, offering an improved capacity to avoid frequency collisions. This work establishes transmon-based dual-rail qubits as an attractive building block for hardware-efficient quantum error correction.

关键词： Quantum computation Quantum error correction Quantum information with solid state qubits

TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

Single-shot blind deconvolution with coded aperture

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arXiv 2022年

作者： Muneta, Hideyuki Horisaki, Ryoichi Nishizaki, Yohei Naruse, Makoto Tanida, Jun Department of Information and Physical Sciences Graduate School of Information Science and Technology Osaka University 1-5 Yamadaoka Osaka Suita565-0871 Japan Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Systems and Control Laboratory Environmental Technology Research Division Osaka Research Institute of Industrial Science and Technology 1-6-50 Morinomiya Joto-ku Osaka536-8553 Japan

In this paper, we present a method for single-shot blind deconvolution incorporating a coded aperture (CA). In this method, we utilize the CA, inserted on the pupil plane, as support constraints in blind deconvolution. Not only an object but also a point spread function of turbulence are estimated from a single captured image by a reconstruction algorithm with the CA support. The proposed method is demonstrated by a simulation and an experiment in which point sources are recovered under severe turbulence. © 2022, CC BY.

关键词： Convolution

A model-based assessment of social isolation practices for COVID-19 outbreak response in residential care facilities

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arXiv 2023年

作者： Zachreson, Cameron Tobin, Ruarai Walker, Camelia Conway, Eamon Shearer, Freya M. McVernon, Jodie Geard, Nicholas School of Computing and Information Systems The University of Melbourne ParkvilleVIC Australia Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health The University of Melbourne ParkvilleVIC Australia School of Mathematics and Statistics The University of Melbourne ParkvilleVIC Australia The Walter and Eliza Hall Institute Melbourne Australia Victorian Infectious Disease Reference Laboratory Epidemiology Unit The Royal Melbourne Hospital Peter Doherty Institute for Infection and Immunity MelbourneVIC Australia Department of Infectious Diseases The University of Melbourne Peter Doherty Institute for Infection and Immunity MelbourneVIC Australia

Background: Residential aged-care facilities (RACFs, also called long-term care facilities, aged care homes, or nursing homes) have elevated risks of respiratory infection outbreaks and associated disease burden. During the COVID-19 pandemic, social isolation policies were commonly used in these facilities to prevent and mitigate outbreaks. We refer specifically to general isolation policies that were intended to reduce contact between residents, without regard to confirmed infection status. Such policies are controversial because of their association with adverse mental and physical health indicators and there is a lack of modelling that assesses their effectiveness. Methods: In consultation with the Australian Government Department of Health and Aged Care, we developed an agent-based model of COVID-19 transmission in a structured population, intended to represent the salient characteristics of a residential care environment. Using our model, we generated stochastic ensembles of simulated outbreaks and compared summary statistics of outbreaks simulated under different mitigation conditions. Our study focuses on the marginal impact of general isolation (reducing social contact between residents), regardless of confirmed infection. For a realistic assessment, our model included other generic interventions consistent with the Australian Government's recommendations released during the COVID-19 pandemic: isolation of confirmed resident cases, furlough (mandatory paid leave) of staff members with confirmed infection, and deployment of personal protective equipment (PPE) after outbreak declaration. Results: In the absence of any asymptomatic screening, general isolation of residents to their rooms reduced median cumulative cases by approximately 27%. However, when conducted concurrently with asymptomatic screening and isolation of confirmed cases, general isolation reduced the median number of cumulative infections by only 12% in our simulations. Conclusions: Under reali

关键词： COVID-19

Entanglement swapping under quantum information masking

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arXiv 2021年

作者： Ji, Zhaoxu Fan, Peiru Rahman, Atta Ur Zhang, Huanguo Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

Quantum information contained in single-particle states can be masked by mapping them to entangled states. In this paper, we consider entanglement swapping under the masking of quantum information. Our work can pave the way for developing the applications of quantum information masking schemes in entanglement-swapping-based quantum cryptography. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum optics

Noise optimization for artificial neural networks

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arXiv 2021年

作者： Xiao, Li Zhang, Zeliang Peng, Yijie Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100090 China Department of Management Science and Information Systems Guanghua School of Management Peking University Beijing100871 China School of computer science and technology Huazhong University of Science and Technology China

Adding noises to artificial neural network(ANN) has been shown to be able to improve robustness in previous work. In this work, we propose a new technique to compute the pathwise stochastic gradient estimate with respect to the standard deviation of the Gaussian noise added to each neuron of the ANN. By our proposed technique, the gradient estimate with respect to noise levels is a byproduct of the backpropagation algorithm for estimating gradient with respect to synaptic weights in ANN. Thus, the noise level for each neuron can be optimized simultaneously in the processing of training the synaptic weights at nearly no extra computational cost. In numerical experiments, our proposed method can achieve significant performance improvement on robustness of several popular ANN structures under both black box and white box attacks tested in various computer vision datasets. code is available at https://***/LX-doctorAI/ noiseoptimize © 2021, CC BY.

关键词： Neural networks