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IEEE Conference on Global Communications (GLOBECOM)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

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Energy-Efficient Design in STAR-RIS Assisted Communication System with Antenna Selection

Energy-Efficient Design in STAR-RIS Assisted Communication S...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyang Zhang Yang Lu Bo Ai Zhangdui Zhong Zhiguo Ding Tony Q. S. Quek School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing China Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Department of Electrical and Electronic Engineering University of Manchester Manchester UK Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

This paper investigates the energy-efficient beamforming design in a simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) assisted wireless communication system, where the antenna selection scheme is adopted. An energy efficiency (EE) maximization problem is formulated by optimizing the transmit beamformers and the phase shift vectors subject to the power budget constraint of the base station (BS), the maximum transmit power constraint per antenna and the users' data rate requirements. An alternating optimization-based algorithm is proposed to tackle the coupled variables, and the quadratic transform is used to deal with the fractional formulations. Simulation results demonstrate that the antenna selection scheme can significantly improve the EE performance by suppressing the energy consumption due to massive antennas. With the assistance of the STAR-RIS, the EE performance is further enhanced.

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Optimal AoI-based Block Propagation and Incentive Mechanism for Blockchain Networks in Web 3.0

arXiv

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arXiv 2024年

作者： Wen, Jinbo Kang, Jiawen Xiong, Zehui Du, Hongyang Yang, Zhaohui Niyato, Dusit Shen, Meng Jiao, Yutao Zhang, Yang The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong The College of Information Science and Electronic Engineering Zhejiang University China The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Cyberspace Science and Technology Beijing Institute of Technology China The College of Communications Engineering Army Engineering University of PLA China

Web 3.0 is regarded as a revolutionary paradigm that enables users to securely manage data without a centralized authority. Blockchains, which enable data to be managed in a decentralized and transparent manner, are key technologies for achieving Web 3.0 goals. However, Web 3.0 based on blockchains is still in its infancy, such as ensuring block freshness and optimizing block propagation for improving blockchain performance. In this paper, we develop a freshness-aware block propagation optimization framework for Web 3.0. We first propose a novel metric called Age of Block Information (AoBI) based on the concept of age of information to quantify block freshness. AoBI measures the time elapsed from the freshest transaction generation to the completion of block consensus. To make block propagation optimization tractable, we classify miners into five different states and propose a block propagation model for public blockchains inspired by epidemic models. Moreover, considering that the miners are bounded rational, we propose an incentive mechanism based on the evolutionary game for block propagation to improve block propagation efficiency. Numerical results demonstrate that compared with other block propagation mechanisms in public blockchains, the proposed scheme has a higher block forwarding probability, which improves block propagation efficiency and decreases the minimum value of average AoBI. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

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FAS-RIS: A Block-Correlation Model Analysis

arXiv

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arXiv 2024年

作者： Lai, Xiazhi Yao, Junteng Zhi, Kangda Wu, Tuo Morales-Jimenez, David Wong, Kai-Kit The School of Computer Science Guangdong University of Education Guangdong Guangzhou China The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Signal Theory Networking and Communications Universidad de Granada Granada18071 Spain The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Yonsei Frontier Laboratory The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

In this correspondence, we analyze the performance of a reconfigurable intelligent surface (RIS)-aided communication system that involves a fluid antenna system (FAS)-enabled receiver. By applying the central limit theorem (CLT), we derive approximate expressions for the system outage probability when the RIS has a large number of elements. Also, we adopt the block-correlation channel model to simplify the outage probability expressions, reducing the computational complexity and shedding light on the impact of the number of ports. Numerical results validate the effectiveness of our analysis, especially in scenarios with a large number of RIS elements. © 2024, CC BY.

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Dynamic Spectrum Aggregation and Access for Rescue Cognitive Networks Using Multi-Agent Actor-Critic Reinforcement Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhang, Wensheng Ding, Wenjiao Wang, Cheng-Xiang Chen, Yunfei Abreu, Giuseppe Liu, Zhi Sun, Jian Han, Zhu Shandong University School of Information Science and Engineering Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Qingdao266237 China Industrial and Commercial Bank Of China Limited Shandong Branch Jinan China Southeast University National Mobile Communications Research Laboratory School of Information Science and Engineering Nanjing211189 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing211111 China Durham University Department of Engineering DurhamDH1 3LE United Kingdom Constructor University Bremen GGMBH School of Computer Science and Engineering Campus Ring 1 Bremen28757 Germany University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Unmanned aerial vehicles (UAVs) based emergency communication is gaining more attention for disaster rescue, where the UAV can work as an aerial base station to provide spectrum access in areas with power outages or network interruptions. However, UAVs can only provide limited spectrum access for a short period of time due to limited battery energy. Aiming to providing spectrum access to more users under spectrum and energy limitations, a new dynamic spectrum aggregation and access scheme is proposed for rescue cognitive networks (RCNs), in which different kinds of users are modeled as primary users (PUs) and secondary users (SUs). In each time slot, SUs with different spectrum sensing capabilities, spectrum aggregation capabilities, and bandwidth demands need to sense and aggregate multiple spectrum slots that are not occupied by PUs for data transmission. Based on selected channel states, the reward is designed as a function of transmission rates and is fed back to SUs. A maximum entropy based multi-agent actor-critic (ME-MAAC) algorithm is proposed to optimize the action selection policy for SUs. Simulation results show that the proposed ME-MAAC algorithm can effectively achieve higher achievable rate, lower mean collision rate and lower power consumption, than state-of-the-art methods based on Deep Q-Network (DQN) algorithm. The proposed RCNs provide a novel spectrum usage paradigm for emergency communication and rescue networks. © 2015 IEEE.

关键词： Reinforcement learning

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FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models

arXiv

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arXiv 2024年

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Fireclay

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Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting

arXiv

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arXiv 2024年

作者： Guo, Pengxin Jin, Pengrong Li, Ziyue Bai, Lei Zhang, Yu The Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong The Department of Mathematics Southern University of Science and Technology Shenzhen518055 China The Department of Information Systems University of Cologne Cologne50923 Germany The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Peng Cheng Laboratory Shenzhen518000 China

Accurate spatial-temporal traffic flow forecasting is crucial in aiding traffic managers in implementing control measures and assisting drivers in selecting optimal travel routes. Traditional deep-learning based methods for traffic flow forecasting typically rely on historical data to train their models, which are then used to make predictions on future data. However, the performance of the trained model usually degrades due to the temporal drift between the historical and future data. To make the model trained on historical data better adapt to future data in a fully online manner, this paper conducts the first study of the online test-time adaptation techniques for spatial-temporal traffic flow forecasting problems. To this end, we propose an Adaptive Double Correction by Series Decomposition (ADCSD) method, which first decomposes the output of the trained model into seasonal and trend-cyclical parts and then corrects them by two separate modules during the testing phase using the latest observed data entry by entry. In the proposed ADCSD method, instead of fine-tuning the whole trained model during the testing phase, a lite network is attached after the trained model, and only the lite network is fine-tuned in the testing process each time a data entry is observed. Moreover, to satisfy that different time series variables may have different levels of temporal drift, two adaptive vectors are adopted to provide different weights for different time series variables. Extensive experiments on four real-world traffic flow forecasting datasets demonstrate the effectiveness of the proposed ADCSD method. The code is available at https://***/Pengxin-Guo/ADCSD. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Sensory Modulation of Gait Balance in Human Locomotion: A Neuromusculoskeletal Modeling Study

Sensory Modulation of Gait Balance in Human Locomotion: A Ne...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Vahid Firouzi Omid Mohseni Oskar Von Stryk Andre Seyfarth Maziar A. Sharbafi Laufiabor Locomotion Laboratory Centre for Cognitive Science Technische Universität Darmstadt Darmstadt Germany Department of Computer Science Technische Universität Darmstadt Darmstadt Germany Department of Electrical Engineering and Information Technology Measurement and Sensor Technology Group Technische Universität Darmstadt Darmstadt Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Maintaining balance during gait relies on intricate sensorimotor processes, engaging compensatory muscle actions and reflex modulations to counteract external perturbations and prevent falls. This study aims to identify possible muscle reflex parameters that are modulated to maintain balance in the face of external perturbations through neuromechanical simulations. A reflex-based neuromusculoskeletal model is utilized and subjected to four distinct sagittal-plane perturbation types (two whole body linear momentum (WBLM) and two whole body angular momentum (WBAM) applied to the torso at five different timings. Analysis of the correlation between reflex parameters and their impact on perturbation recovery indicates seven key reflex parameters as strong candidates for modulating gait balance. For WBLM perturbations, our findings indicated the contribution of both stance and swing phase strategies by modulating Soleus positive force feedback and Iliopsoas activity. In WBAM perturbations, Vasti positive force feedback emerged as a crucial reflex contributing to knee joint stability in backward torque perturbation. The study suggests leg adjustments as a primary strategy for recovering from forward torque perturbations, achieved by modulating hamstring positive force feedback and increasing Iliopsoas muscle activation during the swing phase. These findings provide new insights into the underlying mechanisms of reflex modulation and its significance in balancing and recovering from perturbations during locomotion.

关键词： Torso Legged locomotion Torque Perturbation methods Force feedback Modulation Muscles Robot sensing systems Stability analysis Timing

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Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data

arXiv

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arXiv 2022年

作者： Huo, Zepeng Qian, Xiaoning Huang, Shuai Wang, Zhangyang Mortazavi, Bobak J. Computer Science & Engineering Department Texas A&M University College StationTX United States Electrical & Computer Engineering Department Texas A&M University College StationTX United States Industrial & Systems Engineering Department University of Washington SeattleWA United States Electrical & Computer Engineering Department University of Texas AustinTX United States

Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive. Training models with this imbalance rate (class density discrepancy) may lead to suboptimal prediction. Traditionally this problem is addressed through ad-hoc methods such as resampling or reweighting but performance in many cases is still limited. We propose a framework for training models for this imbalance issue: 1) we first decouple the feature extraction and classification process, adjusting training batches separately for each component to mitigate bias caused by class density discrepancy;2) we train the network with both a density-aware loss and a learnable cost matrix for misclassifications. We demonstrate our model's improved performance in real-world medical datasets (TOPCAT and MIMIC-III) to show improved AUC-ROC, AUC-PRC, Brier Skill Score compared with the baselines in the domain. © 2022, CC BY.

关键词： Medical computing

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