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Iterative Closest Point via MultiKernel Correntropy for Point Cloud Fine Registration

IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 766-770页

作者： Yi, Hao Hu, Limei Chen, Feng Ren, Xiaoping Duan, Shukai Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China

The Iterative Closest Point (ICP) method, primarily used for transformation estimation, is a crucial technique in 3D signal processing, especially for point cloud fine registration. However, traditional ICP is prone to local optima and sensitive to noise, especially when there is no good initialization. Based on the observation that registration errors typically exhibit a multimodal distribution under large rotational offsets and noisy environments, the MultiKernel Correntropy (MKC), which can estimate the registration error distribution, is introduced to provide global information for ICP. Moreover, since MKC consists of multiple Gaussian kernels, it can effectively resist most of the noise. A MultiKernel Correntropy based Iterative Closest Point (MKCICP) is proposed. Extensive experiments on both simulated and real-world datasets show that MKCICP achieves better performance compared to other related methods in challenging scenarios involving large rotational angles, low partial overlap, and high noise levels.

关键词： Point cloud compression Noise Iterative methods Optimization Kernel Vectors Signal processing algorithms Mathematical models Loss measurement linear programming MultiKernel Correntropy (MKC) Iterative Closest Point (ICP) transformation estimation signal processing global optimum point cloud registration

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2-D Unconditional Maximum Likelihood DOA Estimation Based on Majorization-Minimization

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 3550-3554页

作者： Li, Pan Li, Jianfeng Zhang, Xiaofei Wu, Qihui Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum S Minist Ind & Informat Technol Nanjing 211106 Peoples R China

In this paper, we propose a majorization-minimization (MM) based refinement strategy tailored for two-dimensional (2-D) unconditional maximum likelihood (UML) direction-of-arrival (DOA) estimation of a single source. We introduce two surrogate functions (linear and quadratic) for 2-D UML DOA estimation with the aim of successively reducing the objective function's value. The proposed MM method guarantees convergence to the objective function's stationary point. Furthermore, we employ the backtracking squared iterative method (SQUAREM) to accelerate the convergence speed of the proposed MM method. Numerical experiments further validate the efficiency of our proposed MM method.

关键词： Direction-of-arrival estimation Unified modeling language Vectors Convergence Maximum likelihood estimation linear programming Minimization Array signal processing direction-of-arrival (DOA) estimation majorization-minimization (MM) unconditional maximum likelihood (UML) optimization

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Synthesis of Sum-and-Difference Patterns for Radome-Enclosed linear Arrays With Common Weights Using Convex Optimization

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2025年第3期24卷 607-611页

作者： Li, Wei-Zong Jiao, Yong-Chang Zhang, Yi-Xuan Zhang, Li Xidian Univ Shaanxi Key Lab Large Scale Electromagnet Comp Xian 710071 Peoples R China

With the advancement of monopulse radar technology, synthesis of the sum-and-difference patterns (SDPs) for the radome-enclosed arrays has emerged as a pressing area of research. Due to the influence of asymmetric radome, active element patterns of the radome-enclosed linear array (RELA) are completely different. To accurately consider the array-element mutual couplings and array-and-radome interactions, a novel simultaneous synthesis technique for low-sidelobe-level (SLL) SDPs of the RELA with common weights is proposed. First, a nonconvex SDP synthesis problem with the nonconvex constraint and the nonconvex objective function is established. Then, by fixing the reference phases for the sum pattern (SP) radiation far-field and the difference pattern (DP) slope at the target direction, the original nonconvex synthesis problem is transformed into a convex minimum problem, which can be efficiently solved by the convex optimization methods. Finally, a 40-element RELA is implemented, demonstrating that the proposed synthesis technique can be used to simultaneously synthesize SDPs of the RELA with asymmetric active element patterns. The SPs with -25 dB SLL and the DPs with -20 dB SLL are simultaneously synthesized, and the obtained results verify effectiveness of the proposed synthesis technique.

关键词： Radomes Antenna arrays linear antenna arrays Phased arrays Optimization linear programming Convex functions Radar tracking Vectors Radar Active radome-element patterns convex optimization radome-enclosed linear array sum-and-difference pattern synthesis

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Weighted Sum Power Minimization for Cooperative Spectrum Sharing in Cognitive Radio Networks

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IEEE COMMUNICATIONS LETTERS 2025年第1期29卷 160-164页

作者： Yu, Yang Tang, Xiaoqing Hubei Three Gorges Polytech Elect Informat Sch Yichang 443000 Peoples R China Hubei Univ Sch Artificial Intelligence Wuhan 430062 Peoples R China

This letter introduces weighted sum power (WSP), a new performance metric for wireless resource allocation during cooperative spectrum sharing in cognitive radio networks, where the primary and secondary nodes have different priorities and quality of service (QoS) requirements. Compared to using energy efficiency (EE) and weighted sum energy efficiency (WSEE) as performance metrics and optimization objectives of wireless resource allocation towards green communication, the linear character of WSP can reduce the complexity of optimization problems. Meanwhile, the weights assigned to different nodes are beneficial for managing their power budget. Using WSP as the optimization objective, a suboptimal resource allocation scheme is proposed, leveraging linear programming and Newton's method. Simulations verify that the proposed scheme provides near-optimal performance with low computation time. Furthermore, the initial approximate value selection in Newton's method is also optimized to accelerate the proposed scheme.

关键词： Resource management Relays Quality of service Newton method Power demand Minimization linear programming Device-to-device communication Transmitters Standards Cognitive radio network cooperative spectrum sharing green communication WSP

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Wideband Beamforming Design for IRS-Assisted Multi-User THz Communications

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IEEE COMMUNICATIONS LETTERS 2025年第2期29卷 269-273页

作者： Qiu, Chi Wu, Qingqing Chen, Wen Gao, Ying Hao, Wanming Shanghai Jiao Tong Univ Dept Elect Engn Minhang 200240 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Univ Macau Dept Elect & Comp Engn Macau Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 201210 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

Intelligent reflecting surface (IRS)-assisted terahertz (THz) communication is becoming a key technology for next-generation wireless networks. Although IRS has great promise, it is severely hindered by the beam squint problem resulting from the frequency-independent nature of the passive reflecting elements, especially in ultra-wide THz bands. To address this problem, we introduce time delay modules to the IRS and focus on the weighted sum rate maximization problem through the joint optimization of transmit beamforming, IRS phase shifts, and time delays. To solve the formulated optimization problem, an alternating optimization algorithm that decomposes the original problem into three subproblems is proposed. Specifically, we apply semidefinite relaxation and successive convex approximation techniques to solve each subproblem. Simulation results demonstrate the superiority of the proposed scheme over recent literature works. Particularly, it achieves near-optimal performance with slightly increased hardware cost.

关键词： Terahertz communications Array signal processing Optimization Hardware Delay effects Costs Wideband Vectors linear programming Transmission line matrix methods IRS THz multi-user beam squint joint beamforming design

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Coded Cooperative Networks for Semi-Decentralized Federated Learning

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第3期14卷 626-630页

作者： Weng, Shudi Xiao, Ming Ren, Chao Skoglund, Mikael KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-11428 Stockholm Sweden

To enhance straggler resilience in federated learning (FL) systems, a semi-decentralized approach has been recently proposed, enabling collaboration between clients. Unlike the existing semi-decentralized schemes, which adaptively adjust the collaboration weight according to the network topology, this letter proposes a deterministic coded network that leverages wireless diversity for semi-decentralized FL without requiring prior information about the entire network. Furthermore, the theoretical analyses of the outage and the convergence rate of the proposed scheme are provided. Finally, the superiority of our proposed method over benchmark methods is demonstrated through comprehensive simulations.

关键词： Training Stochastic processes Convergence Wireless networks Signal to noise ratio linear programming Encoding Computational modeling Collaboration Codes Semi-decentralized federated learning wireless network diversity network code communication stragglers

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Rate Maximization for Downlink Pinching-Antenna Systems

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第5期14卷 1431-1435页

作者： Xu, Yanqing Ding, Zhiguo Karagiannidis, George K. Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen 518172 Peoples R China Khalifa Univ Dept Comp & Informat Engn Abu Dhabi U Arab Emirates Univ Manchester Dept Elect & Elect Engn Manchester M1 9BB England Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece

In this letter, we consider a new type of flexible-antenna system, termed pinching-antenna, where multiple low-cost pinching antennas, realized by activating small dielectric particles on a dielectric waveguide, are jointly used to serve a single-antenna user. Our goal is to maximize the downlink transmission rate by optimizing the locations of the pinching antennas. However, these locations affect both the path losses and the phase shifts of the user's effective channel gain, making the problem challenging to solve. To address this challenge and solve the problem in a low complexity manner, a relaxed optimization problem is developed that minimizes the impact of path loss while ensuring that the received signals at the user are constructive. This approach leads to a two-stage algorithm: in the first stage, the locations of the pinching antennas are optimized to minimize the large-scale path loss;in the second stage, the antenna locations are refined to maximize the received signal strength. Simulation results show that pinching-antenna systems significantly outperform conventional fixed-location antenna systems, and the proposed algorithm achieves nearly the same performance as the highly complex exhaustive search-based benchmark.

关键词： Antennas Pins Receiving antennas Transmitting antennas Downlink Optimization linear programming Adaptive arrays System analysis and design Simulation Pinching antenna flexible-antenna system downlink rate maximization line-of-sight communication

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A Reinforcement Learning-Enhanced Dung Beetle Optimization Approach for Agile Earth Observation Satellite Scheduling

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Huang, Weiquan Wang, He Wu, Junyu Hou, Haoyu Li, Jiatao Li, Zixuan Song, Yanjie Harbin Engn Univ Coll Intelligent Sci & Engn Harbin 150001 Peoples R China Harbin Inst Technol Coll Mech & Elect Engn Harbin 150001 Peoples R China Dalian Maritime Univ Natl Engn Res Ctr Maritime Nav Syst Dalian 116026 Peoples R China

Due to the increasing demand for remote sensing imaging products, the agile Earth observation satellite scheduling problem (AEOSSP) has garnered significant attention. In response, this letter proposes a reinforcement learning-based dung beetle optimization (RLDBO) algorithm to address the AEOSSP. The proposed method dynamically adjusts the proportions of four types of dung beetles (ball-rolling beetle, brood ball beetle, small dung beetle, and thief beetle), enabling adaptive optimization of the scheduling scheme to better handle the complexities and uncertainties of the search space. The Q-learning mechanism guides the adjustment of these proportions, effectively balancing global exploration and local exploitation at different stages of the search process. Experimental results demonstrate that the RLDBO algorithm effectively solves the AEOSSP across multiple instances, and it outperforms other algorithms in various aspects, including optimization performance, convergence speed, and scheduling effectiveness. The experimental validation confirms that RLDBO significantly enhances the efficiency and effectiveness of agile Earth observation satellite (AEOS) scheduling.

关键词： Satellites Earth Scheduling Optimization Processor scheduling Planetary orbits Heuristic algorithms Metaheuristics linear programming Uncertainty Agile Earth observation satellite (AEOS) dung beetle optimization Q-learning remote sensing scheduling

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Designing Quantum Gradient Descent Algorithm for MIMO NOMA Rate Maximization With STAR-RIS

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第4期14卷 959-963页

作者： Paul, Anal Singh, Keshav Li, Chih-Peng Mumtaz, Shahid Natl Sun Yat sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan Nottingham Trent Univ Dept Engn Nottingham NG1 4FQ England

We introduce, for the first time in wireless communication networks, a quantum gradient descent (QGD) algorithm to maximize sum data rates in non-orthogonal multiple access (NOMA)-based simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multiple-input and multiple-output systems. The QGD algorithm utilizes the principles of quantum parallelism and superposition to efficiently solve the high-dimensional optimization challenges inherent in configuring transmit and receive beamformers and STAR-RIS elements. Extensive simulations demonstrate that the QGD algorithm significantly outperforms classical optimization methods, achieving up to 49.50% and 44.88% higher data rates compared to classical gradient descent algorithms for configurations with 256 STAR-RIS elements. Furthermore, the NOMA model shows substantial improvements in sum data rate performance, with gains of 179.65% and 145.61% over space division multiple access schemes under similar frameworks.

关键词： Optimization NOMA Reflection Reconfigurable intelligent surfaces Downlink Vectors Signal to noise ratio Array signal processing Parallel processing linear programming Quantum computing search space-time complexity gradient descent algorithms quantum gradient descent

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Efficient CNN Prediction With Smoothness Factor for Reversible Data Hiding

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1341-1345页

作者： Lin, Minchun Xiang, Shijun Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

In reversible data hiding (RDH) community, researchers often train the CNN-based predictors with the Mean Square Error (MSE) loss function to evaluate the differences between original and predicted images. This will make the prediction network parameters optimized for all pixels without difference. Considering that the prediction errors in smooth areas are prioritized from the prediction error set for reversible data hiding, in this letter we propose to apply a smoothness factor into the MSE loss function. The smoothness factor used to evaluate the pixel smoothness of an image in steganography is adopted as the loss weight in the new loss function, corresponding to large values in the smooth areas and small values in the texture areas. Experimental results have shown that the CNN-based predictors trained with the proposed loss function can predict pixels more accurately in the smooth areas than using the original loss function. As a bonus, better embedding performance can be achieved by comparing with recent typical CNN-based RDH methods.

关键词： Distortion Filter banks Convolutional neural networks Training linear programming Kernel Data mining Wavelet coefficients Steganography Optimization Reversible data hiding CNN prediction loss function smoothness factor

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