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IEEE Conference on Wireless Communications and Networking

作者： Yihong Zheng Shumin Wang Xinghe Chu Wei Zheng Zhiqun Hu Zhaoming Lu Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the sixth generation (6G) Internet of Things (IoT), integrated sensing and communication (ISAC) emerges as a key technology, which is expected to significantly enhance the perception capabilities and spectrum efficiency of base stations (BSs). It holds potential for applications in unmanned aerial vehicle (UAV) monitoring, vehicle positioning, and crowd detection. However, developing an integrated waveform that efficiently conserves spectrum resources while maintaining lower complexity remains challenging. This paper designs an ISAC system that utilizes orbital angular momentum (OAM) waves generated by a uniform circular array (UCA), which enhances communication and sensing capabilities by allocating distinct modes. This paper proposes a multimode multiplexing communication scheme based on a single UCA and an OAM-circular reception method (OAM-CRM) for the two-dimensional direction-of-arrival (DOA) estimation of targets, encompassing both azimuth and elevation angles. Additionally, an OAM-different modes (OAMDM) algorithm is designed to optimize communication and sensing performance across various mode sets. Simulation results verify the effectiveness of the OAM ISAC system and demonstrate the superior performance of the proposed algorithms compared to conventional methods.

关键词： Multiplexing Orbital calculations Direction-of-arrival estimation Accuracy Simulation Estimation Integrated sensing and communication approximation algorithms Autonomous aerial vehicles Internet of Things

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Improved Performance of Multi-Angle Quantum Approximate Optimization Algorithm (ma-QAOA) Compared to QAOA On Simulation and Experimental Hardware Platforms

Improved Performance of Multi-Angle Quantum Approximate Opti...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Vandit Srivastava Rohith P Sanyam Singhal Debanjan Bhowmik Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Dept. of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Department of Physics Indian Institute of Technology Bombay Mumbai Maharashtra India Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai Maharashtra India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Multi-angle quantum approximate optimization algorithm (ma-QAOA) has been proposed as an alternative to regular QAOA for solving optimization problems using noisy intermediate scale quantum (NISQ) hardware. Here, we first implement QAOA and ma-QAOA algorithms to solve MaxCut on weighted and unweighted versions of the Erdős–Rényi (ER) and random cubic (RC) graphs of 6 and 10 nodes. Quantum circuit parameters are updated iteratively through classical feedback with the quantum circuit for forward pass modelled on Qiskit simulator without noise models. Then, the quantum circuit for the final run with updated parameters is implemented on Qiskit simulator without noise models, Qiskit simulator with experimentally benchmarked noise models (fake back-end), and also on actual experimental quantum hardware (IBM’s 127-qubit quantum processor ibm brisbane). We show that for most graph instances, ma-QAOA performs better than QAOA (in terms of approximation ratio (AR)) and needs less stages in the quantum circuit for all three aforementioned methods. Finally, we extend our results up to 14-node RC graphs while restricting ourselves to Qiskit simulation without noise (both for parameter update and final run) and show that for most instances, ma-QAOA again needs much less stages compared to QAOA to obtain reasonable AR.

关键词： Communication systems Noise Benchmark testing approximation algorithms Hardware Noise measurement Quantum circuit Integrated circuit modeling Optimization

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H-VLO: Hybrid LiDAR-Camera Fusion For Self-Supervised Odometry

H-VLO: Hybrid LiDAR-Camera Fusion For Self-Supervised Odomet...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Aydemir, Eren Fetic, Naida Unel, Mustafa Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Ford Otosan Prod Dev Istanbul Turkey

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

In this paper, we propose a hybrid visual-LiDAR odometry (H-VLO) framework that fuses predicted visual depth map and completed LiDAR map. Compared to the previous visual-LiDAR odometry methods, our approach leverages 2D feature matching and 3D association by utilizing deep depth map, deep flow map and deep LiDAR depth completion networks. Rather than extraction of the depth values from LiDAR measurements for each visual feature, our method first densifies a LiDAR scan with a deep depth completion network and then fuses it with visual deep depth map estimation in a Bayesian framework. This method reduces pose estimation drift by improving feature-to-feature and point-to-feature matching, as well as scale recovery. The evaluations on the public KITTI odometry benchmark show that our technique achieves better or at least comparable estimates than the state-of-the-art visual-LiDAR and monocular visual odometry approaches.

关键词： Visualization Laser radar Three-dimensional displays Fuses approximation algorithms Feature extraction Cameras

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Nested Stochastic Gradient Descent for Sinkhorn Distance-Regularized Distributionally Robust Optimization

arXiv

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arXiv 2025年

作者： Yang, Yufeng Zhou, Yi Lu, Zhaosong Texas A&M University College StationTX United States University of Minnesota Twin Cities MN United States

Distributionally robust optimization (DRO) is a powerful technique to train robust models against data distribution shift. This paper aims to solve regularized nonconvex DRO problems, where the uncertainty set is modeled by a so-called generalized Sinkhorn distance and the loss function is nonconvex and possibly unbounded. Such a distance allows to model uncertainty of distributions with different probability supports and divergence functions. For this class of regularized DRO problems, we derive a novel dual formulation taking the form of nested stochastic programming, where the dual variable depends on the data sample. To solve the dual problem, we provide theoretical evidence to design a nested stochastic gradient descent (SGD) algorithm, which leverages stochastic approximation to estimate the nested stochastic gradients. We study the convergence rate of nested SGD and establish polynomial iteration and sample complexities that are independent of the data size and parameter dimension, indicating its potential for solving large-scale DRO problems. We conduct numerical experiments to demonstrate the efficiency and robustness of the proposed algorithm. © 2025, CC BY.

关键词： approximation algorithms

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Extrinsics and Linearized Component-Wise Conditionally Unbiased MMSE Estimation in Approximate Message Passing

Extrinsics and Linearized Component-Wise Conditionally Unbia...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Zilu Zhao Dirk Slock Communication Systems Department EURECOM France

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

The Bethe Free Energy (BFE) has been found to be closely connected to various message passing algorithms. Studies have indicated that the BFE shares stationary points with message passing algorithms like Belief Propagation (BP) and Expectation Propagation (EP). Generalized Approximate Message Passing (GAMP) algorithms have demonstrated significant efficacy in signal recovery. Nevertheless, they may encounter convergence issues. EP algorithms start from a factored approximate posterior in an exponential family. They update a factor by fitting an exponential family pdf to a approximate posterior which is obtained by replacing one approximate factor by the original (prior) factor. The remaining factors form the approximate extrinsic. Hence extrinsics are obtained by marginalizing the product of all pdf factors except for the prior. A marginal posterior is then obtained by combining the extrinsic with the prior. Low complexity algorithms like GAMP in turn obtain the extrinsic from the posterior. In this paper, we explore the BFE within the context of Generalized Linear Models (GLMs). Applying a BFE based EP approach leads to the re(G)VAMP algorithm which provides asymptotically exact marginal posteriors based on asymptotically Gaussian extrinsics. It also provides equivalent Gaussian priors and hence an equivalent overall Gaussian linear model, which allows the application of large random matrix theory. We show how on the other hand how Large System Limit (LSL) based approximations in BP lead to GAMP. When derived from the BFE of the GLM, GAMP algorithms combine two asymptotic LSL simplifications which are asymptotic Gaussianity of extrinsics and large random matrix theory based asymptotic variance computations. The LSL simplifications allow to relate extrinsic messages to posterior pdfs by first-order Taylor series expansion based perturbations. We also apply LSL approximations to the variances of the various Gaussians involved, which in fact leads to a rederivation of

关键词： Hands Message passing Perturbation methods Computational modeling Fitting Estimation approximation algorithms Taylor series Convergence Context modeling

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Distributed Policy Evaluation with Local Updates over Time-Varying Communication Network

Distributed Policy Evaluation with Local Updates over Time-V...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Matthew Crespo Chinwendu Enyioha Electrical and Computer Engineering University of Central Florida Orlando United States of America

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

This paper studies a consensus-based policy evaluation algorithm in a cooperative team of heterogeneous learners. To improve each agent’s approximation of their value function, they each update their weight parameters, then perform consensus updates with neighboring agents over a dynamic communication network. We present the analysis of an efficient fully distributed algorithm for cooperatively evaluating and optimizing their value functions over a time-varying communication network in the average-reward setting. Our main result shows that, using this algorithm, the agents can approximate the true value function, with the approximation error dependent on the number of communication rounds and number of time steps it takes for the communication network to be connected. To validate the theoretical results, we present accompanying numerical experiments that show the theoretical error bounds are not only tight, but get tighter as the time steps required to guarantee network connectivity reduces.

关键词： Heuristic algorithms approximation algorithms approximation error Communication networks Distributed algorithms Time-varying systems

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An Ensemble Learning Method Based on Neighborhood Granularity Discrimination Index and Its Application in Software Defect Prediction

An Ensemble Learning Method Based on Neighborhood Granularit...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Yuqi Sha Feng Jiang Qiang Hu Yifan He College of Information Science and Technology Qingdao University of Science and Technology Qingdao P. R. China Institute of Cosmetic Regulatory Science Beijing Technology and Business University Beijing P.R. China

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Software defect prediction (SDP) is a primary field of study in software engineering, aiming to optimize test resource allocation by highlighting the defect-prone software modules. Over the last few years, ensemble learning method has been extensively adopted in SDP. However, how to strengthen the diversity of base learners is an issue in ensemble learning. In this paper, we consider the problem of diversity in the eye of feature space perturbation. First, we propose the notion of neighborhood granularity discrimination index (NGDI), by combining the neighborhood knowledge granularity with the neighborhood discrimination index within the framework of neighborhood rough sets. NGDI can not only measure the uncertainty of feature subsets' discriminant capability, but also characterize the granularity of neighborhood knowledge induced by feature subsets. Second, we propose an ensemble learning algorithm, EL-NGDI, established on the NGDI. ELNGDI disturbs the feature space using multiple NGDI-based neighborhood approximate reducts. Third, we use ELNGDI to predict software defects. ELNGDI and the Synthetic Minority Oversampling Technique (SMOTE) are combined in order to handle the class imbalance issue in SDP, and propose a mechanism called SMOTE-ELNGDI. Experimental results on 20 datasets demonstrate that ELNGDI effectively improves the performance of SDP compared with existing ensemble learning methods.

关键词： Uncertainty Software algorithms Diversity reception Rough sets Prediction algorithms approximation algorithms Software Ensemble learning Indexes Software engineering

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Generalized Approach for Fractional-Order Low-Pass Butterworth Filter Realization with an Inverse Follow the Leader Filter (IFLF) Topology

Generalized Approach for Fractional-Order Low-Pass Butterwor...

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IEEE Southeastcon

作者： Rowan Aldridge David Kubánek Jaroslav Koton Todd J. Freeborn Dept. Elec. Computer Eng. The University of Alabama Tuscaloosa USA Dept. Telecommunications Brno University of Technology Brno Czech Republic

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

This research presents a generalized approach to determine the coefficients to realize any $(N+\alpha)$ fractional-order low-pass Butterworth response using transfer function forms based on the Inverse Follow the Leader Filter (IFLF) topology. This approach automates the process to create and assemble the required transfer function (based on user specified values) and then apply a numerical optimization to estimate the necessary coefficients to achieve the target filter magnitude response. MATLAB simulations of $(2+\alpha)$ and $(10+\alpha)$ order filters using the generalized approach validate the process. Further, an experimental 2.5-order low-pass filter is experimentally realized (using an approximated fractional-order capacitor) to validate the characteristics of a low-pass filter designed using this process.

关键词： Capacitors Low-pass filters Transfer functions Filtering algorithms approximation algorithms Topology Numerical models MATLAB Integrated circuit modeling Optimization

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Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization

Split Federated Learning Over Heterogeneous Edge Devices: Al...

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IEEE Conference on Wireless Communications and Networking

作者： Yunrui Sun Gang Hu Yinglei Teng Dunbo Cai Beijing Key Laboratory of Space-ground Interconnection and Convergence Beijing University of Posts and Telecommunications (BUPT) Beijing China China Mobile (Suzhou) Software Technology Company Limited Suzhou China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data, while reducing computational load and preserving privacy simultaneously. However, current SL algorithms face limitations in training efficiency and suffer from prolonged latency, particularly in sequential settings, where the slowest device can bottleneck the entire process due to heterogeneous resources and frequent data exchanges between clients and servers. To address these challenges, we propose the Heterogeneous Split Federated Learning (HSFL) framework, which allows resource-constrained clients to train their personalized client-side models in parallel, utilizing different cut layers. Aiming to mitigate the impact of heterogeneous environments and accelerate the training process, we formulate a latency minimization problem that optimizes computational and transmission resources jointly. Additionally, we design a resource allocation algorithm that combines the Sample Average approximation (SAA), Genetic Algorithm (GA), Lagrangian relaxation and Branch and Bound (B&B) methods to efficiently solve this problem. Simulation results demonstrate that HSFL outperforms other frameworks in terms of both convergence rate and model accuracy on heterogeneous devices with non-iid data, while the optimization algorithm is better than other baseline methods in reducing latency.

关键词： Training Federated learning Computational modeling Simulation approximation algorithms Data models Servers Optimization Genetic algorithms Convergence

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UAV-Aided Delay-Oriented IoT Data Collection Strategies Using a Pattern Search Framework

UAV-Aided Delay-Oriented IoT Data Collection Strategies Usin...

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IEEE Conference on Wireless Communications and Networking

作者： Kyeongsoo Kim Wooseok Cha Jihwan P. Choi Dept. of Aerospace Engineering KAIST Korea

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The role of unmanned aerial vehicle (UAV) technologies in the Internet of Things (IoT) data collection is attracting tremendous attention due to its operational flexibility. To guarantee reliability and network efficiency of the IoTecosystem, delay is a crucial performance metric in a IoTdata collection scenario. To reap the benefits of UAVs, this paper investigates delay-oriented IoT data collection scenarios utilizing UAVs. To achieve low-delay data transmission, non-orthogonal multiple access (NOMA), which has high spectral efficiency, is employed. Considering the queue system of IoT, we formulate a problem that jointly optimizes UAV deployment and IoT transmit power to minimize the average packet delay of IoT nodes. As a novel strategy to solve the non-convex problem, a pattern search framework that can obtain near-optimal UAV deployment and IoT transmit power is proposed. Numerical results show that the optimal deployment of UAV depends on the traffic volume of IoT nodes to minimize delay. Furthermore, our proposed pattern search framework achieves significant delay reduction up to 85% compared to other benchmark schemes, highlighting its potential in the UAV-aided IoT data collection.

关键词： NOMA Spectral efficiency Data collection Benchmark testing Autonomous aerial vehicles approximation algorithms Search problems Delays Internet of Things Reliability

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