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检索条件"主题词=Iterative algorithms"

共 16610 条记录，以下是431-440 订阅

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Capacity-Net-Based RIS Precoding Design Without Channel Estimation for mmWave MIMO System

Capacity-Net-Based RIS Precoding Design Without Channel Esti...

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Chun-Yuan Huang Po-Heng Chou Wan-Jen Huang Ying-Ren Chien Yu Tsao Institute of Communication Engineering (ICE) National Sun Yat-sen University (NSYSU) Kaohsiung Taiwan Research Center for Information Technology Innovation (CITI) Academia Sinica (AS) Taipei Taiwan Department of Electrical Engineering National Ilan University (NIU) I-Lan Taiwan

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

In this paper, we propose Capacity-Net, a novel unsupervised learning approach aimed at maximizing the achievable rate in reflecting intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple input multiple output (MIMO) systems. To combat severe channel fading of the mmWave spectrum, we optimize the phase-shifting factors of the reflective elements in the RIS to enhance the achievable rate. However, most optimization algorithms rely heavily on complete and accurate channel state information (CSI), which is often challenging to acquire since the RIS is mostly composed of passive components. To circumvent this challenge, we leverage unsupervised learning techniques with implicit CSI provided by the received pilot signals. Specifically, perfect CSI is usually required to evaluate the achievable rate as a performance metric of the current optimization result of the unsupervised learning method. Instead of channel estimation, the Capacity-Net is proposed to establish a mapping among the received pilot signals, optimized RIS phase shifts, and the resultant achievable rates. In our simulation, the proposed Capacity-Netbased unsupervised learning without channel estimation obtains performance similar to that of an iterative optimization algorithm and the unsupervised learning that is trained by perfect CSI in terms of achievable rates.

关键词： Precoding Channel estimation Training data Propagation losses MIMO iterative algorithms Vectors Millimeter wave communication Unsupervised learning Optimization

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An Improved iterative Constrained Weighted Least Squares Solution for Moving Target Localization

An Improved Iterative Constrained Weighted Least Squares Sol...

引用

IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Yanping Liao Chanjuan Xing Yongpeng Wang College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350351019

ISBN: (纸本)9798350351026

This paper proposes an improved iterative constrained weighted least squares (ICWLS) localization algorithm based on the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) in the presence of receiver position errors. Instead of estimating the positional parameters, the errors of the parameters are estimated in the proposed method. It not only avoids the nonlinear operations in the twostage weighted least squares (TSWLS) algorithm, but simulation results indicate that the proposed algorithm showcases higher accuracy and better anti-noise performance compared to existing algorithms.

关键词： Location awareness Time-frequency analysis Accuracy Time difference of arrival Simulation Receivers Antennas and propagation iterative algorithms Error correction Convergence

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iterative Channel Estimation for OTFS-based LEO-Sat Communication Using Comb-type ZC Sequences

Iterative Channel Estimation for OTFS-based LEO-Sat Communic...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Haiwei Shi Wang Xing Yiqing Zhou Yu Zhang Shuo Zhou Jinglin Shi State Key Lab of Processors Institute of Computing Technology CAS Beijing China School of Information Science and Technology ShanghaiTech University Shanghai China University of Chinese Academy of Sciences Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In low earth orbit satellite (LEO-Sat) communication systems based on orthogonal time frequency space (OTFS) modulation, existing channel estimation methods usually use high-power pilots, leading to a high peak-to-average power ratio (PAPR). In this paper, we propose a comb-type pilot based frame structure by inserting multiple identical Zadoff-Chu (ZC) sequences along the Doppler dimension. The ZC sequences are arranged at equal intervals in the delay dimension. The power of pilots is consistent with the power of data symbols, which can achieve low PAPR at the transmitter. At the receiver, based on the analyzed power distribution of the correlation function, we derive the power threshold to identify the received pilots. Then, an iterative ZC-sequences based channel estimation algorithm is proposed aided by data detection and interference cancellation. Through iterations, the data interference can be alleviated and estimation accuracy can be improved. Simulation results demonstrate that the proposed scheme can achieve superior channel estimation performance with low PAPR in the time domain.

关键词： Time-frequency analysis Interference cancellation Communication systems Transmitters Channel estimation Symbols Modulation Peak to average power ratio iterative algorithms Time-domain analysis

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Off-Grid Parameter Estimation for OFDM-Based ISAC Systems with Incomplete Data

Off-Grid Parameter Estimation for OFDM-Based ISAC Systems wi...

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IEEE Conference on Wireless Communications and Networking

作者： Muyao Ling Xinxin Feng Haifeng Zheng Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In the 6G environment, addressing the challenges of data loss and off-grid issues during target parameter estimation poses a significant challenge for the Integrated Sensing and Communication (ISAC) system. In the ISAC framework, a commonly used method for parameter estimation is compressive sensing. However, compressive sensing may encounter off-grid issues in continuous parameter estimation. In contrast, the atomic norm proves effective in addressing off-grid problems, making it more suitable for continuous parameter estimation. We explore the application of the atomic norm in ISAC and further derive an ISAC model based on OFDM (Orthogonal Frequency Division Multiplexing) utilizing the atomic norm under conditions of incomplete data. To ensure improved convergence speed and accuracy of our algorithm, we employ the Alternating Direction Method of Multipliers (ADMM) for iterative implementation. Experimental results demonstrate that our proposed AN algorithm accurately estimates target parameters in the presence of data loss, exhibiting higher precision and robustness compared to traditional methods.

关键词： Parameter estimation Accuracy OFDM Noise reduction Estimation Robustness iterative algorithms

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ESO-Based Data Driven Set-Point Learning Control for Nonlinear batch Processes with PD-Type Feedback Control Structure Subject to Nonrepetitive Uncertainties*

ESO-Based Data Driven Set-Point Learning Control for Nonline...

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Data Driven Control and Learning Systems (DDCLS)

作者： Naseem Ahmad Shoulin Hao Tao Liu Yihui Gong Jiyan Zhang Haixia Wang Yong Zhu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China Institute of Advanced Control Technology Dalian University of Technology Dalian P. R. China School of Biomedical Engineering Dalian University of Technology Dalian P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes configured with an inherent PD-type control loop, so as to actively suppress the adverse effect of nonrepetitive external disturbances and nonidentical initial resetting conditions, by only utilizing the available process input/output data. The controlled process is firstly reformulated as an iterative dynamic linearization data model (IDLDM) with an extra generalized disturbance, where the unknown pseudo partial derivative and the generalized disturbance are, respectively, estimated by a projection-like algorithm and iterative ESO along the batch direction. By virtue of the estimated PPD and generalized disturbance, a set-point learning updating law with adaptive learning gain is then developed to optimize the tracking performance from batch-to-batch, by tuning the set-point command of the closed-loop PD-type control. Robust convergence analysis for the established closed-loop learning system is analyzed rigorously. The effectiveness and superiority of the proposed scheme are verified by an illustrative example.

关键词： Learning systems Uncertainty Heuristic algorithms Batch production systems Process control Observers iterative algorithms

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Multi-Stage Packet Loss Concealment Using ADMM-GLA Phase Reconstruction

Multi-Stage Packet Loss Concealment Using ADMM-GLA Phase Rec...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Chirag Nileshbhai Patadiya Siddhesh Chandrashekhar Gangan Bhavin Shah Siva Prasad Gundur Samsung Research Institute Bangalore India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Packet loss is a commonly occurring problem in VoIP based real-time calls. In order to conceal lost packets in real-time due to latency constraints there is a need for frame based packet loss concealment (PLC) methods. However many existing frame based reconstruction methods cause unnatural artefacts in listening due to poor phase reconstruction. To address these problems, we propose multi-stage packet loss concealment, which utilizes iterative Alternating Direction Method of Multipliers (ADMM) based Griffin-LIM Algorithm (GLA) for phase reconstruction as an intermediate step in three stage packet concealment method. The experimental findings show that 1) Our multi-stage method can reconstruct the phase spectrogram and magnitude spectrogram of the lost packets with good accuracy and perceptual quality, and 2) Our approach reconstruction method accomplish superior speech quality than conventional PLC methods

关键词： Frequency-domain analysis Packet loss Reconstruction algorithms Speech enhancement Predictive models Real-time systems iterative algorithms

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Exploiting N:M Sparsity in Quantized-Folded ResNets: Signed Multicoat Supermasks and iterative Pruning-Quantization

Exploiting N:M Sparsity in Quantized-Folded ResNets: Signed ...

引用

International Symposium on Computing and Networking (CANDAR)

作者： Akihiro Shioda Ángel López García-Arias Hikari Otsuka Yuki Ichikawa Yasuyuki Okoshi Kazushi Kawamura Thiem Van Chu Daichi Fujiki Masato Motomura Institute of Science Tokyo Japan NTT Corporation Japan

ISBN: (数字)9798331528362

ISBN: (纸本)9798331528379

Neural networks provide excellent performance, but operating them under limited resources remains challenging due to their high computational cost. Therefore, pruning techniques to reduce the computational cost of neural networks are gaining attention. In particular, N:M sparsity, a structured pruning method, offers an excellent trade-off between sparsity and image recognition accuracy. More hardware products, such as GPUs and AI accelerators, support N:M sparsity. In this paper, we apply N:M sparsity and quantization to Folded ResNets, where weights are shared for some iterative blocks, to find a good tradeoff between model size and image recognition accuracy. First, we investigate the performance of an algorithm that combines two state-of-the-art methods in N:M pruning and quantization, SRSTE and LSQ, respectively. We also investigate an N:M extension of the Signed Multicoated Supermasks (SMS) algorithm, which is based on the Strong Lottery Ticket Hypothesis (SLTH). Although the SMS-based method outperforms the LSQ-based method in unstructured pruning, our experiments show that the SMS-based method is inferior in N:M sparsity. This accuracy degradation is caused by N:M structured sparsity constraint narrowing the search space of the strong lottery ticket’s subnetworks. To address this degradation, we propose adding noise to the initial weights to improve accuracy. This approach consistently improves the accuracy of the N:M SMS across a variety of sparsity levels, while ensuring that the parameter size increase remains minimal.

关键词： Degradation Accuracy Quantization (signal) Image recognition Neural networks Noise AI accelerators iterative algorithms Hardware Computational efficiency

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Learning Hierarchical Control For Multi-Agent Capacity-Constrained Systems

Learning Hierarchical Control For Multi-Agent Capacity-Const...

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IEEE Conference on Decision and Control

作者： Charlott Vallon Alessandro Pinto Bartolomeo Stellato Francesco Borrelli Department of Mechanical Engineering University of California Berkeley Berkeley CA NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA Department of Operations Research and Financial Engineering Princeton University Princeton NJ

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper introduces a data-driven hierarchical control scheme for a fleet of nonlinear, capacity-constrained autonomous agents in an iterative environment. The proposed control framework consists of a high-level dynamic task assignment and routing layer and low-level motion planning and tracking layer. Each layer uses a data-driven Model Predictive Control (MPC) policy for efficient calculation of new task assignments and actuation. We use collected data to iteratively refine estimates of agent capacity usage, and update MPC policy parameters accordingly. We leverage tools from iterative learning control to integrate learning at both hierarchy levels, and coordinate learning between levels to maintain closed-loop feasibility and performance improvement at each iteration.

关键词： Tracking Dynamics Routing iterative algorithms Trajectory Planning Computational complexity iterative learning control Predictive control Multi-agent systems

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Memory-enhanced Bidirectional Potential-guided RRT with Resampling-based iterative Optimization for Efficient Path Planning*

Memory-enhanced Bidirectional Potential-guided RRT with Resa...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Bowen Lin Junhu Wei State Key Laboratory for Manufacturing Systems Engineering School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In many applications, robots heavily rely on advanced path planning approaches to navigate from start to goal without collisions. The sampling-based Rapidly-exploring Random Tree (RRT) algorithms stand out for their probabilistic completeness and efficiency in exploring the state space. However, conventional RRT and its variants often struggle to produce low-cost paths or require significant solution time to find optimal or near-optimal paths in cluttered environments. To address these challenges, this paper introduces a novel approach. Firstly, building upon the bidirectional RRT framework, the artificial potential function is incorporated to guide the expansion of random trees. Furthermore, we propose memory-enhanced sampling to record and cluster invalid nodes for simplifying the computation of obstacles’ repulsion. Lastly, we introduce a resampling-based iterative optimization strategy that significantly reduces the time and memory consumed to converge to optimal solutions. Simulation results demonstrate the efficiency of the proposed approach, which can achieve optimal or near-optimal paths within a relatively short time.

关键词： Computer aided software engineering Navigation Simulation Probabilistic logic Path planning iterative algorithms Space exploration Collision avoidance Optimization Robots

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Efficient Design for NOMA Enabled Integrated Sensing and Semantic Communication

Efficient Design for NOMA Enabled Integrated Sensing and Sem...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zhouxiang Zhao Yating Tang Yuzhi Yang Yuanyuan Dong Lexi Xu Zhaohui Yang Zhaoyang Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Info. Proc. Commun. & Netw. (IPCAN) Hangzhou China Research Institute China United Network Communications Corporation Beijing China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

This paper investigates semantic energy efficiency in a non-orthogonal multiple access (NOMA) enabled integrated sensing and semantic communication (ISSC) system. The model involves the base station (BS) transmitting information to multiple users while performing target sensing using dedicated beamforming. In the considered model, the BS needs to transmit substantial text data to each user using text semantic communication techniques while sensing the targets with certain constraints. Our goal is to maximize semantic energy efficiency and meet semantic communication and sensing accuracy requirements. We formulate an optimization problem for the beamforming matrix and semantic parameter, employing the Dinkelbach's algorithm for simplification and proposing an iterative solution. Numerical results validate the efficacy of the NOMA-ISSC scheme.

关键词： NOMA Vehicular and wireless technologies Array signal processing Semantics Energy efficiency iterative algorithms Data models

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