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Repurposing Mu-Mimo Downlink For Joint Wireless Communications And Imaging Via Virtual Users

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Repurposing Mu-Mimo Downlink For Joint Wireless Communicatio...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kris Li David Ramirez Kumar Vijay Mishra Ashutosh Sabharwal Department of Electrical and Computer Engineering Rice University Houston TX USA DOCOMO Innnovations Inc. Portland OR USA United States DEVCOM Army Research Laboratory Adelphi MD USA

In this paper, we propose a method to repurpose the multi-user MIMO downlink transmission for joint wireless communication and imaging. The key idea is to introduce the concept of virtual users in the communication coverage area and use the existing MUMIMO beamforming methods to jointly beamform towards real and virtual users. The virtual users are placed to complement the locations of actual users, with the objective to illuminate the scene as uniformly as possible. We study a single-parameter tradeoff, introduced by a power split parameter between real and virtual users. We demonstrate via simulated examples that the virtual user concept is effective in providing a scalable imaging and communications performance tradeoff for cases where the real users are clustered in small geographical areas.

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

RIS-assisted Multi-cell Over-the-air Computation

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IEEE Transactions on Wireless Communications 2025年

作者： Xiao, Yue Tegos, Sotiris A. Huang, Shaocheng Diamantoulakis, Panagiotis D. Tyrovolas, Dimitrios Ma, Zheng Karagiannidis, George K. Fan, Pingzhi Southwest Jiaotong University Provincial Key Laboratory of Information Coding and Transmission Chengdu610031 China Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Southwest Institute of Electronics and Telecommunications Sichuan Chengdu610041 China

The advent of sixth-generation (6G) wireless communication systems represents a transformative leap in global connectivity, moving from traditional internet of things (IoT) frameworks to an advanced artificial intelligence of things (AIoT) paradigm. This evolution presents significant challenges, primarily due to exponential growth in data volume, complexity, and latency requirements. To address these challenges, over-the-air (OTA) computation has emerged as a breakthrough approach by integrating computational processes directly into the communication framework, overcoming the inefficiencies of traditional separate designs. In this study, we explore the integration of OTA computation with reconfigurable intelligent surfaces (RISs) within multi-cell multiple-input multiple-output (MIMO) networks. RIS technology enhances signal propagation, mitigates interference, and optimizes wireless coverage, thereby complementing the OTA computation paradigm’s ability to facilitate real-time data aggregation and processing. Specifically, we propose a novel joint optimization framework aimed at minimizing the mean squared error (MSE) in multi-cell environments. This framework addresses the complexity of beamforming design through an innovative power-iteration-based majorization-minimization approach and a successive alignment technique. In addition, we perform asymptotic analysis to elucidate the performance benefits of large-scale MIMO and RIS configurations. We also consider the fairness of MSE computation throughout the multi-cell system. Finally, numerical simulations validate the effectiveness of the proposed methods and provide additional insights based on the asymptotic analysis. © 2002-2012 IEEE.

关键词： Asymptotic analysis

Universal Multi-view Black-box Attack against Object Detectors via Layout Optimization

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Donghua Yao, Wen Jiang, Tingsong Li, Chao Zhejiang University College of Computer Science and Technology Hangzhou China Chinese Academy of Military Science Intelligent Game and Decision Laboratory Defense Innovation Institute Beijing China Zhongyuan University of Technology School of Automation and Electrical Engineering Zhengzhou China

Object detectors have demonstrated vulnerability to adversarial examples crafted by small perturbations that can deceive the object detector. Existing adversarial attacks mainly focus on white-box attacks and are merely valid at a specific viewpoint, while the universal multi-view black-box attack is less explored, limiting their generalization in practice. In this paper, we propose a novel universal multi-view black-box attack against object detectors, which optimizes a universal adversarial UV texture constructed by multiple image stickers for a 3D object via the designed layout optimization algorithm. Specifically, we treat the placement of image stickers on the UV texture as a circle-based layout optimization problem, whose objective is to find the optimal circle layout filled with image stickers so that it can deceive the object detector under the multi-view scenario. To ensure reasonable placement of image stickers, two constraints are elaborately devised. To optimize the layout, we adopt the random search algorithm enhanced by the devised important-aware selection strategy to find the most appropriate image sticker for each circle from the image sticker pools. Extensive experiments conducted on four common object detectors suggested that the detection performance decreases by a large magnitude of 74.29% on average in multi-view scenarios. Additionally, a novel evaluation tool based on the photo-realistic simulator is designed to assess the texture-based attack fairly. © 1991-2012 IEEE.

关键词： Object detection

FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field∗

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arXiv 2024年

作者： Stathoulopoulos, Nikolaos Lindqvist, Björn Koval, Anton Agha-Mohammadi, Ali-Akbar Nikolakopoulos, George Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Sweden Jet Propulsion Laboratory California Institute of Technology PasadenaCA911 09 United States

In this article, a novel approach for merging 3D point cloud maps in the context of egocentric multi-robot exploration is presented. Unlike traditional methods, the proposed approach leverages state-of-the-art place recognition and learned descriptors to efficiently detect overlap between maps, eliminating the need for the time-consuming global feature extraction and feature matching process. The estimated overlapping regions are used to calculate a homogeneous rigid transform, which serves as an initial condition for the GICP point cloud registration algorithm to refine the alignment between the maps. The advantages of this approach include faster processing time, improved accuracy, and increased robustness in challenging environments. Furthermore, the effectiveness of the proposed framework is successfully demonstrated through multiple field missions of robot exploration in a variety of different underground environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

Efficient Feature Mapping Using a Collaborative Team of AUVs

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arXiv 2024年

作者： Biggs, Benjamin Stilwell, Daniel J. Yetkin, Harun McMahon, James US Naval Research Laboratory WashingtonDC7130 United States Bradley Department of Electrical and Computer Engineering Virginia Tech Blacks-burg VA United States Bartin University Turkey

We present the results of experiments performed using a team of small autonomous underwater vehicles (AUVs) to determine the location of an isobath. The primary contributions of this work are (1) the development of a novel objective function for level set estimation that utilizes a rigorous assessment of uncertainty, and (2) a description of the practical challenges and corresponding solutions needed to implement our approach in the field using a team of AUVs. We combine path planning techniques and an approach to decentralization from prior work that yields theoretical performance guarantees. Experimentation with a team of AUVs provides empirical evidence that the desirable performance guarantees can be preserved in practice even in the presence of limitations that commonly arise in underwater robotics, including slow and intermittent acoustic communications and limited computational resources. © 2024, CC BY-NC-SA.

关键词： Autonomous underwater vehicles

Measuring 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1394-1395页

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Cho, Min Gee Qian, Yiwen Gu, Junsi Rozeveld, Steve Ercius, Peter Fessler, Jeffrey A. Xu, Ting Scott, Mary Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL United States Advanced Photon Source Argonne National Laboratory Lemont IL United States Department of Materials Science and Engineering University of California at Berkeley Berkeley CA United States National Center for Electron Microscopy Lawrence Berkeley National Laboratory Berkeley CA United States Dow Chemical Co. Midland MI United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Materials Science Division Lawrence Berkeley National Laboratory Berkeley CA United States Applied Physics Program University of Michigan Ann Arbor MI United States

High-Capacity Metasurface at Limits of Polarization and Wavelength Multiplexing

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arXiv 2024年

作者： Bao, Yanjun Shi, Hongsheng Wei, Rui Wang, Boyou Zhou, Zhou Qiu, Cheng-Wei Li, Baojun Guangdong Provincial Key Laboratory of Nanophotonic Manipulation Institute of Nanophotonics College of Physics & Optoelectronic Engineering Jinan University Guangzhou511443 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117583 Singapore

Polarization and wavelength multiplexing are the two most widely employed techniques to improve the capacity in the metasurfaces. Existing works have pushed each technique to its individual limits. For example, the polarization multiplexing channels working at a single wavelength have been significantly increased by using noise engineering. However, it is still challenging to achieve the multiplexing limits of wavelength and polarization simultaneously. Besides, such multiplexing methods suffer from computational inefficiencies, hindering their application in tasks like image recognition that require extensive training computation. In this work, we introduce a gradient-based optimization algorithm using deep neural network (DNN) to achieve the limits of both polarization and wavelength multiplexing with high computational efficiency. We experimentally demonstrate this capability, achieving a record-breaking capacity of 15 holographic images across five wavelengths and the maximum of three independent polarization channels, as well as 18 holographic images across three wavelengths and six corelated polarization channels. Moreover, leveraging the high computational efficiency of our DNN-based method, which is well-suited for processing large datasets, we implement large-scale image recognition tasks across 36 classes encoded in a record of nine multiplexed channels (three wavelengths × three polarizations), achieving 96% classification accuracy in calculations and 91.5% in experiments. This work sets a new benchmark for high-capacity multiplexing with metasurfaces and demonstrates the power of gradient-based inverse design for realizing multi-functional optical elements. © 2024, CC BY.

关键词： Deep neural networks

The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration

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arXiv 2024年

作者： Greer, Ross Antoniussen, Bjørk Andersen, Mathias V. Møgelmose, Andreas Trivedi, Mohan M. The Laboratory for Intelligent and Safe Automobiles Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92092 United States The Faculty of Electronic Systems Aalborg University Denmark

Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data. We demonstrate the effectiveness of entropy querying to select informative samples, aiming to reduce annotation costs and improve model performance. We experiment using the BEVFusion model for 3D object detection on the nuScenes dataset, comparing active learning to random sampling and demonstrating that entropy querying outperforms in most cases. The method is particularly effective in reducing the performance gap between majority and minority classes. Class-specific analysis reveals efficient allocation of annotated resources for limited data budgets, emphasizing the importance of selecting diverse and informative data for model training. Our findings suggest that entropy querying is a promising strategy for selecting data that enhances model learning in resource-constrained environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Entropy