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NeuPAN: Direct Point Robot Navigation With End-to-End Model-Based Learning

IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 2804-2824页

作者： Han, Ruihua Wang, Shuai Wang, Shuaijun Zhang, Zeqing Chen, Jianjun Lin, Shijie Li, Chengyang Xu, Chengzhong Eldar, Yonina C. Hao, Qi Pan, Jia Univ Hong Kong Dept Comp Sci Hong Kong 000000 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 100045 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Robot & Autonomous Syst Thrust Guangzhou 511455 Peoples R China Univ Macau IOTSC Macau 999078 Peoples R China Weizmann Inst Sci IL-7621613 Rehovot Israel Southern Univ Sci & Technol Sifakis Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China

Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural proximal alternating-minimization network (NeuPAN): a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: first, it directly maps raw point cloud data to a latent distance feature space for collision-free motion generation, avoiding error propagation from the perception to control pipeline;second, it is interpretable from an end-to-end model-based learning perspective. The crux of NeuPAN is solving an end-to-end mathematical model with numerous point-level constraints using a plug-and-play proximal alternating-minimization network, incorporating neurons in the loop. This allows NeuPAN to generate real-time, physically interpretable motions. It seamlessly integrates data and knowledge engines, and its network parameters can be fine-tuned via backpropagation. We evaluate NeuPAN on a ground mobile robot, a wheel-legged robot, and an autonomous vehicle, in extensive simulated and real-world environments. Results demonstrate that NeuPAN outperforms existing baselines in terms of accuracy, efficiency, robustness, and generalization capabilities across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unknown and unstructured environments with arbitrarily shaped objects, transforming impassable paths into passable ones.

关键词： Robots Navigation Collision avoidance Mobile robots Mathematical models Accuracy Autonomous vehicles approximation algorithms Robot sensing systems Real-time systems Direct point robot navigation model-based learning optimization-based collision avoidance (OBCA)

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An Efficient Network Based on Conjugate Gradient Optimization and Approximate Observation Model for SAR Image Reconstruction

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 2464-2476页

作者： Zhou, Song Chen, Jing Wang, Zao Wang, Yuhao Wen, Pin Nanchang Univ Sch Informat & Engn Nanchang 330027 Peoples R China

Deep learning has been successfully applied to solve the synthetic aperture radar (SAR) imaging problem, which shows superior imaging performance to compressive sensing (CS)-based methods under sparse sampling conditions. However, due to the computation of the large-scale matrix, the optimal searching in an iterative manner will involve tremendous computational complexity with slow convergence, which will prevent deep learning algorithms from being efficiently applied for SAR imaging. To address this problem, an efficient network is proposed for SAR imaging under sparse sampling conditions, which can be designed by an improved conjugate gradient (CG) optimization strategy. First, the large-scale matrix in the CG algorithm can be approximately decomposed by introducing a matched filtering (MF)-based operator, which will facilitate the gradient computation with high efficiency during the optimization process. Second, the strategy utilizes the advantages of CG optimization to precisely eliminate the error component with conjugate searching in each iteration to achieve fast convergence. By incorporating the improved CG into the convolutional neural network (CNN), the network can be developed to automatically learn the prior information and parameters from the training data, based on which the efficiency of the designed network can be increased dramatically to achieve high imaging performance for SAR applications, especially in the cases of wide-scene and high-resolution imaging. Experimental results show that the proposed network exhibits excellent imaging performance and high computational efficiency in SAR imaging of point targets, surface targets, and different types of real scenes.

关键词： Radar polarimetry Computational modeling Imaging Image reconstruction approximation algorithms Optimization Synthetic aperture radar Training Convolutional neural networks Matrix decomposition Approximate observation model conjugate gradient (CG) algorithm convolutional neural network (CNN) regularization synthetic aperture radar (SAR) imaging

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Neural Networks for Phase Shift Optimization of Reconfigurable Intelligent Surfaces Under Imperfect Channel State Information

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IEEE ACCESS 2025年 13卷 53694-53705页

作者： Fondo-Ferreiro, Pablo Saghezchi, Firooz B. Gil-Castineira, Felipe Rodriguez, Jonathan Univ Vigo Informat Technol Grp atlanTTic Vigo 36310 Spain Rhein Westfal TH Aachen Chair Distributed Signal Proc D-52074 Aachen Germany Inst Telecomunicacoes P-3810193 Aveiro Portugal

Reconfigurable intelligent surface (RIS) is a key enabling technology for the sixth generation (6G) of mobile networks. It can focus the signal at an intended location (e.g., a user hotspot) through dynamically adjusting the phase shifts of its passive reflecting elements, thereby enhancing the signal quality and network coverage. However, the optimal configuration of the phase shift profile of RIS is challenging since it requires accurate channel state information (CSI), which is prohibitively expensive to acquire in practice because the number of reflecting elements in RIS is usually large. To address this limitation, in this paper, we train and test a fully-connected neural network (FCN) that estimates the optimal phase shift profile of RIS from noisy CSI measurements. We evaluate the performance of the proposed Machine Learning (ML) model in terms of different key performance indicators (KPIs), including the system bit error rate (BER) and throughput, phase shift estimation mean square error (MSE), and the training time of the neural network itself. Simulation results demonstrate that our proposed technique can significantly improve the performance in RIS-assisted wireless networks, reducing the gap to the optimal network throughput to below 1 %.

关键词： Channel estimation Reconfigurable intelligent surfaces Optimization Neural networks Receivers Estimation Accuracy Bit error rate System performance approximation algorithms 6G reconfigurable intelligent surface (RIS) phase shift optimization imperfect channel state information (CSI) bit error rate (BER) machine learning neural network

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Exact and approximation algorithms for Linear Arrangement Problems

Exact and Approximation Algorithms for Linear Arrangement Pr...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Quilliot, Alain Rebaine, Djamal Univ Blaise Pascal LABEX IMOBS3 LIMOS CNRS UMR 6158 F-63173 Aubiere France UQAC Dept Informat Chicoutimi Saguenay PQ Canada

ISBN: (纸本)9788360810583

We present here new results and algorithms for the Linear Arrangement Problem (LAP). We first propose a new lower bound, which links LAP with the Max Cut Problem, and derive a LIP model as well as a branch/bound algorithm for the general case. Then we focus on the case of interval graphs: we first show that our lower bound is tight for unit interval graphs, and derive an efficient polynomial time approximation algorithm for general interval graphs.

关键词： Algorithm design and analysis approximation algorithms approximation methods Extremities Polynomials Semantics Vectors approximation algorithms Algorithm design and analysis approximation method Extremities Semantics Polynomial Exact semiotics unit interval Lexical permutation sorting

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A Method to Compare Scaling algorithms for Cloud-Based Services

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2025年第1期13卷 34-45页

作者： De Vleeschauwer, Danny Chang, Chia-Yu Soto, Paola De Bock, Yorick Camelo, Miguel De Schepper, Koen Nokia Bell Labs Network Automat Dept B-2108 Antwerp Belgium Univ Antwerp IMEC IDLab B-2000 Antwerp Belgium

Nowadays, many services are offered via the cloud, i.e., they rely on interacting software components that can run on a set of connected Commercial Off-The-Shelf (COTS) servers sitting in data centers. As the demand for any particular service evolves over time, the computational resources associated with the service must be scaled accordingly while keeping the Key Performance Indicators (KPIs) associated with the service under control. Consequently, scaling always involves a delicate trade-off between using the cloud resources and complying with the KPIs. In this paper, we show that a (workload-dependent) Pareto front embodies this trade-off's limits. We identify this Pareto front for various workloads and assess the ability of several scaling algorithms to approach that Pareto front.

关键词： Cloud computing Software algorithms Software approximation algorithms Symbols Key performance indicator Vectors Synapses Service level agreements Servers Cloud-based services kubernetes pods scaling

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Joint Beamforming in RIS-Assisted Multi-User Transmission Design: A Model-Driven Deep Reinforcement Learning Framework

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第5期73卷 3184-3198页

作者： Jin, Weijie Zhang, Jing Wen, Chao-Kai Jin, Shi Zheng, Fu-Chun Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan Harbin Inst Technol Shenzhen Sch Elect & Informat Engn Shenzhen 518055 Peoples R China

The deployment of multiple reconfigurable intelligent surfaces (RIS) is a promising strategy to enhance wireless system performance. However, joint beamforming in multi-RIS assisted systems faces significant challenges due to the increased number of optimization variables, non-convex objective functions, and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and the successive convex approximation algorithm, maximizing the weighted sum rate in a double-RIS assisted downlink multi-user multiple-input single-output system. We also present a general framework for model-driven deep learning that addresses the limitations of existing methods, which often lack flexibility to different channels and suffer from a large training burden due to the high-dimensional action space of deep reinforcement learning (DRL). Initially, we configure the step size in the proposed algorithm as trainable, accelerating convergence. Then, a recurrent neural network generates the step size for iterations, allowing dynamic iteration extension in varying environmental conditions. We enhance the neural network's self-adaptability by introducing a model-driven DRL algorithm, integrating expert knowledge into the DRL actor network's design. Simulation results demonstrate up to 30% performance improvement over traditional algorithms, achieved by our model-driven framework. The proposed model-driven DRL shows higher capacity for dynamic extension and rapid adaptation to new environments.

关键词： Array signal processing Heuristic algorithms Optimization Wireless communication Vectors Training Convergence approximation algorithms Recurrent neural networks Complexity theory Reconfigurable intelligent surface joint beamforming model-driven deep reinforcement learning

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Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 1094-1109页

作者： Sun, Gangle Cao, Mengyao Wang, Wenjin Xu, Wei Studer, Christoph Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211100 Peoples R China Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland

Grant-free transmission and cell-free communication are vital in improving coverage and quality-of-service for massive machine-type communication. This paper proposes a novel framework of joint active user detection, channel estimation, and data detection (JACD) for massive grant-free transmission in cell-free wireless communication systems. We formulate JACD as an optimization problem and solve it approximately using forward-backward splitting. To deal with the discrete symbol constraint, we relax the discrete constellation to its convex hull and propose two approaches that promote solutions from the constellation set. To reduce complexity, we replace costly computations with approximate shrinkage operations and approximate posterior mean estimator computations. To improve active user detection (AUD) performance, we introduce a soft-output AUD module that considers both the data estimates and channel conditions. To jointly optimize all algorithm hyper-parameters and to improve JACD performance, we further deploy deep unfolding together with a momentum strategy, resulting in two algorithms called DU-ABC and DU-POEM. Finally, we demonstrate the efficacy of the proposed JACD algorithms via extensive system simulations.

关键词： Signal processing algorithms Channel estimation Wireless communication approximation algorithms Vectors Optimization Machine learning algorithms Training Sparse matrices Decoding Active user detection cell-free communication channel estimation data detection deep unfolding grant-free transmission massive machine-type communication

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Two-Stage Correction for Wavefront Curvature Effects of PFA in Focusing Nonideal Circular SAR Data

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第1期61卷 886-899页

作者： Han, Shengliang Zhu, Daiyin Mao, Xinhua Hefei Univ Sch Adv Mfg Engn Hefei 230601 Peoples R China Nanjing Univ Aeronaut & Astronaut Nanjing 211106 Peoples R China

Although the polar-format algorithm (PFA) provides high computational efficiency in spotlight synthetic aperture radar imaging, it is still constrained to applications with large scene size requirements due to the unconsidered wavefront curvature effects. To solve this problem, a two-stage correction algorithm is proposed in this article to mitigate the image deterioration caused by planar wavefront approximation. Through analyzing the distribution characteristics of the quadratic wavefront curvature phase error (QWCPE) for a nonideal circular flight path with different elevation angles, the 2-D space-variant compensation problem is separated into two 1-D compensation. Based on the aforementioned analysis, the QWCPE is first assumed to be associated only with the distorted coordinates of the target, and the first-stage correction is applied to every range gate before the azimuth fast Fourier transform operation of the PFA. Then, the residual QWCPE after the first-stage correction (RQWCPE-1) is assumed to be associated only with the distorted azimuth coordinates of the target;to correct this error, the image after the first-stage compensation is separated into multiple subblocks along the azimuth direction;for each azimuth subblock, the RQWCPE-1 can be regarded as space invariant and corrected by constructing a filter according to the subblock center. Finally, all the subblocks are mosaicked together to generate the final refocused image. Simulated and real data experiments are performed to verify the performance of the proposed algorithm.

关键词： Azimuth Imaging Synthetic aperture radar Filtering algorithms approximation algorithms Trajectory Apertures Polar-format algorithm (PFA) subblock two-stage correction algorithm (TSCA) wavefront curvature effects

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Quantum algorithms for the Physical Layer: Potential Applications to Physical Layer Security

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IEEE ACCESS 2025年 13卷 13988-14009页

作者： Matsumine, Toshiki Ochiai, Hideki Shikata, Junji Yokohama Natl Univ Inst Adv Sci Yokohama 2408501 Japan Osaka Univ Grad Sch Engn Osaka 5650871 Japan Yokohama Natl Univ Grad Sch Environm & Informat Sci Yokohama 2408501 Japan

The field of quantum technologies has garnered considerable interest and witnessed noteworthy progress in recent years. It is also anticipated that these technologies will continue to flourish and exert a considerable influence on society, i.e., a plethora of real-world problems that cannot be solved by classical algorithms are believed to benefit from the implementation of quantum algorithms. Meanwhile, as an alternative to cryptographic methods, physical layer security (PLS) has been extensively studied as a means to realize secure wireless communication that is resistant to attacks by both classical and quantum computers, i.e., quantum-safe. While the prevailing approach to PLS has been based on classical algorithms, this could potentially be accelerated by the application of quantum algorithms. This paper examines the potential applications of various quantum algorithms, including quantum annealing, hybrid quantum-classical algorithms, and Grover-based algorithms, to the the PLS problems. In particular, we begin with a concise overview of their applications to physical layer techniques and then proceed to discuss their use in addressing the challenges of secret message transmission and secret key generation from wireless channels.

关键词： Logic gates Quantum computing Quantum algorithm Optimization Annealing Qubit Quantum annealing Cryptography Computers approximation algorithms Hybrid quantum-classical algorithms multi-input multi-output (MIMO) physical layer security (PLS) quantum algorithms quantum annealing (QA) quantum approximate optimization algorithm (QAOA) reconfigurable intelligent surface (RIS) secret key generation (SKG)

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Exploring a Favorable Tradeoff for Finding Every Efficient Path in Large-Scale Networks

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第4期55卷 1606-1619页

作者： Qin, Jian Yu, Wenwu Mo, Yuanqiu Liu, Hongzhe Zhu, Xia Wei, Wenjia Yao, Zhen Southeast Univ Sch Math Jiangsu Prov Key Lab Networked Collect Intelligenc Nanjing 210096 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Huawei Technol Co Ltd Shenzhen 518129 Peoples R China

Multiobjective shortest path problem (MSPP) is one of the most critical issues in network optimization, aimed at identifying all efficient paths across conflicting objectives. Nowadays, existing methods face substantial bottlenecks in addressing the diverse preferences of decision makers and high spatiotemporal overhead caused by the calculation process, particularly in cases with large-scale networks. To overcome these obstacles, a generalized MSPP in large-scale networks is investigated with the aim of solving it with diverse preferences of decision makers satisfied and low spatiotemporal overhead. Toward this end, with a novel concept, the generalized dominance relation is introduced, and the generalized multiobjective shortest path algorithm via the generalized dynamic programming approach is developed. Moreover, the H-reducible technique is further employed to accelerate the convergence of the proposed algorithm. Additionally, several rigorous proofs are provided for the conclusions that all efficient paths could be found within a tolerable time by the developed algorithm and the algorithm could be implemented in a distributed manner under mild assumptions. Finally, numerous routing experiments are conducted on large-scale communication networks for demonstrating the effectiveness and competitiveness of our approach.

关键词： Optimization Dynamic programming Symbols Routing Directed graphs approximation algorithms Heuristic algorithms Electronic mail Additives Spatiotemporal phenomena Distributed algorithm dynamic programming large-scale networks multiobjective optimization path planning

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