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Sphere Decoding for Binary Polar Codes with the Modified Multiplicative Repetition Construction

清华大学学报自然科学版（英文版） 2025年第3期30卷 1229-1236页

作者： Haiqiang Chen Yuanbo Liu Shuping Dang Qingnian Li Youming Sun Xiangcheng Li School of Computer Electronics and InformationGuangxi UniversityNanning 530004China School of Electrical Electronic and Mechanical EngineeringUniversity of BristolBristol BS8 1TRUK Information and Engineering College Nanning UniversityNanning 530004China

Compared to the successive cancellation(SC)-based decoding algorithms,the sphere decoding(SD)algorithm can achieve better performance with reduced computational complexity,especially for short polar *** this paper,we propose a new method to construct the binary polar codes with the modified multiplicative repetition(MR)-based *** from the original construction,we first design a 2x2q-ary kernel to guarantee the single-level polarization ***,by replacing the new-designed binary companion matrix,a novel strategy is further developed to enhance the polarization in the bit level,resulting in a better distance ***,the SD-based Monte-Carlo(SDMC)method is used to construct MR-based binary polar codes,while the resulting codes without the butterfly pattern are decoded by the SD *** results show that the proposed method with the SD algorithm can achieve a maximum performance gain of 0.27 dB compared to the original method with slightly lower complexity.

关键词： polar codes sphere decoding generator matrix multiplicative repetition Monte-Carlo method

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A Mini-Review of Machine Learning in Big Data Analytics:Applications,Challenges,and Prospects

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Big Data Mining and Analytics 2022年第2期5卷 81-97页

作者： Isaac Kofi Nti Juanita Ahia Quarcoo Justice Aning Godfred Kusi Fosu the Department of Computer Science and Informatics University of Energy and Natural ResourcesSunyani BS2103Ghana the Department of Electrical&Electronic Engineering Sunyani Technical UniversitySunyani BS2103Ghana the Department of Computer Science Sunyani Technical UniversitySunyani BS2103Ghana

The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of *** capability to process these gigantic amounts of data in real-time with Big Data Analytics(BDA)tools and Machine Learning(ML)algorithms carries many ***,the high number of free BDA tools,platforms,and data mining tools makes it challenging to select the appropriate one for the right *** paper presents a comprehensive mini-literature review of ML in BDA,using a keyword search;a total of 1512 published articles was *** articles were screened to 140 based on the study proposed novel *** study outcome shows that deep neural networks(15%),support vector machines(15%),artificial neural networks(14%),decision trees(12%),and ensemble learning techniques(11%)are widely applied in *** related applications fields,challenges,and most importantly the openings for future research,are detailed.

关键词： Big Data Analytics(BDA) Machine Learning(ML) Big Data(BD) Hadoop MapReduce

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Adaptive Splitting Algorithm for Neural Network Modelling in AMP 21

Adaptive Splitting Algorithm for Neural Network Modelling in...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Kaifei Ma, Yuxiang Wu, Yulei Chen, Tao Ma, Huijie Henan University School of Computer and Information Engineering Kaifeng475004 China University of Bristol School of Electrical Electronic and Mechanical Engineering BristolBS8 1UB United Kingdom

ISBN: (纸本)9798350329223

Artificial Intelligence algorithms usually consume a lot of energy and computing resources during training, so data is usually sent to high-performance devices to complete model training. However, such a computing mode cannot make good use of computing resources in the network and is not conducive to the privacy protection of user information. In recent years, some studies have used end devices to participate in model training, among which federated learning is a typical representative. Due to the heterogeneous computing resources of the end devices, how to split the neural network model to achieve an effective balance between training time and energy consumption is an important issue. In this paper, we propose a novel model splitting method that includes the steps of collecting data, correcting the data, and determining the splitting layers to complete the splitting learning. We consider four factors (training time, energy consumption, CPU utilisation, and memory utilisation) to jointly decide the splitting layer and propose a scheme for adaptively revising the model. We use the VGG16 model for evaluation. The results show that the efficiency of our method is improved by 64.5% compared to the recently proposed ARES scheme. At the same time, our proposed method does not affect the convergence and accuracy of the model. © 2023 IEEE.

关键词： Neural network models

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Combined Bulk and Per-subcarrier Relay Selection Enabled by Cascaded Neural Computing

Combined Bulk and Per-subcarrier Relay Selection Enabled by ...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Li, Wenwu Dang, Shuping Zhang, Zhenrong Ge, Zhihui School of Computer Electronics and Information Guangxi University Nanning530004 China University of Bristol Department of Electrical and Electronic Engineering BristolBS81UB United Kingdom

ISBN: (纸本)9781665494274

Cooperative relaying has been recognized as a key technology of sixth generation (6G) networks. It can enhance the flexibility of network deployment and improve coverage. Multi-carrier relay selection is required to efficiently allocate spatial resources in multi-carrier cooperative networks. To effectively reduce the end-to-end latency in multi-carrier cooperative networks, two basic multi-carrier relay selection strategies have been enabled by machine learning, i.e., bulk relay selection, and per-subcarrier relay selection. Nevertheless, bulk relay selection enabled by machine learning may lead to a low coding gain. Per-sub carrier relay selection enabled by machine learning may lead to a complex artificial neural network (ANN) structure when there are many relays in future wireless networks. To balance the complexity and performance, we design a newly cascaded ANN (CANN) to perform the combined bulk and per-subcarrier relay selection scheme. After training the CANN, we demonstrate through simulations that the proposed method can effectively achieve a compromise between complexity and performance. © 2023 IEEE.

关键词： Neural networks

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IoT-Enabled Smart Irrigation System with ABiLSTM for Optimized Water Management and Crop Yields 3

IoT-Enabled Smart Irrigation System with ABiLSTM for Optimiz...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chaturvedi, Abhay Rao, Praveena Venkata Maruti Prasad, S. Malini, T. Prabagar, S. Bharathi, K. Gla University Department of Electronics and Communication Engineering Mathura India Institute of Aeronautical Engineering Department of Civil Engineering Hyderabad India Aditya Institute of Technology & Management Department of Bs & H Tekkali India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India School of Advanced Computing Alliance University Department of Computer Science and Engineering Bangalore India Erode Sengunthar Engineering College Department of Agricultural Engineering Perundurai India

ISBN: (纸本)9798331508456

An efficient management of water resources in agriculture is now crucial in light of the growing global population and increased demand for water. Innovative solutions are needed to improve water efficiency since conventional irrigation methods are not sustainable anymore. Through the use of precision agriculture and the Internet of Things (IoT), smart irrigation has revolutionised water management, leading to increased crop yields. In order to improve the efficiency of decision-making, this research presents an Internet of Things (IoT) smart irrigation system that uses SA-BiLSTM (Self-Attention Bidirectional Long Short-Term Memory). Using Min-Max and Z-score normalisation approaches for feature scaling, the methodology includes data preparation, feature selection, and model training. Methods based on ranking, such as the Chi-Square Test, Pearson Correlation Coefficient (PCC), and Mutual Information (MI), were used to prioritise feature selection. Classification and prediction testing showed that the proposed SA-BiLSTM model outperformed state-of-the-art models like HGAT and Linear SVM, with an astounding 95.63% accuracy. Improved resource allocation and irrigation effectiveness, leading to increased agricultural output, are highlighted by the results, which highlight the effectiveness of advanced machine learning approaches in precision agriculture. © 2025 IEEE.

关键词： Resource allocation

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Applications of Quantum Cascade Lasers in Spectroscopy and Trace Gas Analysis 4

Applications of Quantum Cascade Lasers in Spectroscopy and T...

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4th International Conference on Advances in electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Anandaram, Harishchander Velpula, Srikanth Ramkumar, M. Ramya, G. Lakshmisridevi, S. Ashok, P. Amrita Vishwa Vidyapeetham Amrita School of Artificial Intelligence Coimbatore India Sr University Department of Electrical and Electronics Engineering Telangana Warangal India Sri Krishna College of Engineering and Technology Electronics and Communication Engineering Tamilnadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Bs Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Maharashtra Pune India

ISBN: (纸本)9798350343670

The primary emphasis of this research paper lies in advancing versatile quantum cascade laser-based spectroscopic methods tailored for highly sensitive gas detection. These techniques hold promise for a range of applications including atmospheric sensing, biomedical science, and high-resolution fundamental molecular spectroscopy. Quantum Cascade Lasers (QCLs) have revolutionized spectroscopic techniques and trace gas analysis due to their unique properties. In spectroscopy, QCLs offer widely tunable, high-powered, and narrow-linewidth radiation across the mid-infrared (MIR) region, enabling precise identification and quantification of molecular species. Utilizing a continuous wave cavity ring-down spectrometer (CW-CRDS) with high resolution, coupled with an external cavity continuous-wave quantum cascade laser (CW-EC-QCL) functioning within the wavenumber interval from 1250 cm1 to 1333 cm1 enables spectroscopic examination. This has found applications in various fields namely, Atmospheric Sensing, Biomedical Science, Security and Defense, Environmental Monitoring, Industrial Process Control, Astrophysics and Astronomy. In trace gas analysis, QCLs offer unmatched sensitivity and selectivity. They can detect minuscule concentrations of gases, down to parts per trillion or even lower, making them indispensable in fields where precise gas measurements are paramount. This includes environmental monitoring, medical diagnostics, industrial safety, and scientific research. The versatility and precision of QCL-based spectroscopic techniques continue to drive innovation and advancements in these diverse fields. © 2024 IEEE.

关键词： Free Space Optics Gain Switching Quantum Cascade Lasers Rate Equations Spectroscopic Techniques Trace Gas Analysis

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Multimodal sensor fusion in the latent representation space

arXiv

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arXiv 2022年

作者： Piechocki, Robert J. Wang, Xiaoyang Bocus, Mohammud J. School of Computer Science Electrical and Electronic Engineering and Engineering Maths University of Bristol BristolBS8 1UB United Kingdom

A new method for multimodal sensor fusion is introduced. The technique relies on a two-stage process. In the first stage, a multimodal generative model is constructed from unlabelled training data. In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion tasks. The method also handles cases where observations are accessed only via subsampling i.e. compressed sensing. We demonstrate the effectiveness and excellent performance on a range of multimodal fusion experiments such as multisensory classification, denoising, and recovery from subsampled *** Codes 68Txx © 2022, CC BY.

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Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Zhang, Chuanting Zhang, Haixia Dang, Shuping Shihada, Basem Alouini, Mohamed-Slim Shandong University School of Software Jinan250101 China Shandong University School of Control Science and Engineering Jinan250061 China Shandong University Shandong Key Laboratory of Wireless Communication Technologies Jinan250061 China University of Bristol Department of Electrical and Electronic Engineering BristolBS8 1UB United Kingdom Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia

Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because of the exemption from raw data transmission and enhanced privacy protection. However FL-based wireless traffic prediction methods still rely on heavy data transmissions between local clients and the server for local model updates. Besides, how to model the spatial dependencies of local clients under the framework of FL remains uncertain. To tackle this, we propose an innovative FL algorithm that employs gradient compression and correlation-driven techniques, effectively minimizing data transmission load while preserving prediction accuracy. Our approach begins with the introduction of gradient sparsification in wireless traffic prediction, allowing for significant data compression during model training. We then implement error feedback and gradient tracking methods to mitigate any performance degradation resulting from this compression. Moreover, we develop three tailored model aggregation strategies anchored in gradient correlation, enabling the capture of spatial dependencies across diverse clients. Experiments have been done with two real-world datasets and the results demonstrate that by capturing the spatio-temporal characteristics and correlation among local clients, the proposed algorithm outperforms the state-of-the-art algorithms and can increase the communication efficiency by up to two orders of magnitude without losing prediction accuracy. © 2015 IEEE.

关键词： Prediction models

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Blockage-Aware Robust Beamforming in RIS-Aided Mobile Millimeter Wave MIMO Systems

arXiv

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arXiv 2024年

作者： Yang, Yan Dang, Shuping Wen, Miaowen Ai, Bo Qingyang, Rose The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The School of Electrical Electronic and Mechanical Engineering University of Bristol BristolBS8 1UB United Kingdom The School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China The Department of Electrical and Computer Engineering Utah State University LoganUT84322 United States

Millimeter wave (mmWave) communications are sensitive to blockage over radio propagation paths. The emerging paradigm of reconfigurable intelligent surface (RIS) has the potential to overcome this issue by its ability to arbitrarily reflect the incident signals toward desired directions. This paper proposes a Neyman-Pearson (NP) criterion-based blockage-aware algorithm to improve communication resilience against blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for downlink mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. To minimize the outage probability, a robust RIS beamformer with variant beamwidth is designed to combat uncertain channel state information (CSI). For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we leverage a subspace constraint to reduce the scope of the projection operation and formulate a new Nesterov momentum acceleration scheme to speed up the convergence process of PGD. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach, and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate. © 2024, CC BY.

关键词： Millimeter waves

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Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices

arXiv

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arXiv 2025年

作者： Weidner, C.A. O'Neil, S.P. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science & Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Robust control

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