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QoE-Guaranteed Optimization in MEC-Enabled Metaverse: An Active Inference Deep Reinforcement Learning Approach

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Du, Jianbo Gong, Jie Chu, Xiaoli Xiong, Zehui Chen, Xianfu Dong, Mianxiong Yu, F. Richard Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an710121 China The University of Sheffield Mappin Street Department of Electronic and Electrical Engineering SheffieldS1 3JD United Kingdom Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore Singapore Shenzhen CyberAray Network Technology Company Ltd Shenzhen518000 China Muroran Institute of Technology Department of Sciences and Informatics Muroran050-8585 Japan Carleton University Systems and Computer Engineering OttawaON Canada

In this paper, we consider a MEC-enabled metaverse scenario which consists of a remote metaverse server and an edge server that cooperates to provide services to mobile users. The edge server is deployed at the base station (BS), serves a dual role: augmenting computational capabilities for user equipment (UE) and pre-caching a portion of the metaverse service contents before each time slot. Moreover, the foreground information and the requested contents generated by the UEs can also be cached to the BS. We formulate a problem to maximize the cache hit number by jointly optimizing contents pre-caching and resource allocation at the BS while considering UEs preference and reducing the UEs total energy consumption, essential for the efficient delivery of services in dynamic MEC environments. To solve this problem, we reformulate it as a partially observable markov decision process and propose an active inference enabled deep reinforcement learning algorithm, which combines active inference with deep reinforcement learning to select the optimal strategy by minimizing the expected free energy. Simulations show that the proposed algorithm can effectively improve the total quality of experience and the cache hit number of UEs, while minimizing the UEs total energy consumption compared with other baseline algorithms © 2015 IEEE.

关键词： Active learning

Statistical Mechanics of Dynamical System Identification

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arXiv 2024年

作者： Klishin, Andrei A. Bakarji, Joseph Kutz, J. Nathan Manohar, Krithika AI Institute in Dynamic Systems University of Washington SeattleWA98195 United States Department of Mechanical Engineering University of Washington SeattleWA98195 United States Department of Mechanical Engineering Artificial Intelligence Hub American University of Beirut Beirut1107 2020 Lebanon Departments of Applied Mathematics and Electrical and Computer Engineering University of Washington SeattleWA98195 United States

Recovering dynamical equations from observed noisy data is the central challenge of system identification. We develop a statistical mechanical approach to analyze sparse equation discovery algorithms, which typically balance data fit and parsimony through a trial-and-error selection of hyperparameters. In this framework, statistical mechanics offers tools to analyze the interplay between complexity and fitness, in analogy to that done between entropy and energy. To establish this analogy, we define the optimization procedure as a two-level Bayesian inference problem that separates variable selection from coefficient values and enables the computation of the posterior parameter distribution in closed form. A key advantage of employing statistical mechanical concepts, such as free energy and the partition function, is in the quantification of uncertainty, especially in in the low-data limit;frequently encountered in real-world applications. As the data volume increases, our approach mirrors the thermodynamic limit, leading to distinct sparsity- and noise-induced phase transitions that delineate correct from incorrect identification. This perspective of sparse equation discovery, is versatile and can be adapted to various other equation discovery algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Statistical mechanics

Performance Analysis of Free-Space Information Sharing in Full-Duplex Semantic Communications

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arXiv 2022年

作者： Du, Hongyang Wang, Jiacheng Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Soong, Boon Hee The School of Computer Science and Engineering Nanyang Technological University Singapore 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 Computer Engineering Sungkyunkwan University Korea Republic of The Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

In next-generation Internet services, such as Metaverse, the mixed reality (MR) technique plays a vital role. Yet the limited computing capacity of the user-side MR headset-mounted device (HMD) prevents its further application, especially in scenarios that require a lot of computation. One way out of this dilemma is to design an efficient information sharing scheme among users to replace the heavy and repetitive computation. In this paper, we propose a free-space information sharing mechanism based on full-duplex device-to-device (D2D) semantic communications. Specifically, the view images of MR users in the same real-world scenario may be analogous. Therefore, when one user (i.e., a device) completes some computation tasks, the user can send his own calculation results and the semantic features extracted from the user’s own view image to nearby users (i.e., other devices). On this basis, other users can use the received semantic features to obtain the spatial matching of the computational results under their own view images without repeating the computation. Using generalized small-scale fading models, we analyze the key performance indicators of full-duplex D2D communications, including channel capacity and bit error probability, which directly affect the transmission of semantic information. Finally, the numerical analysis experiment proves the effectiveness of our proposed methods. © 2022, CC BY.

关键词： Mixed reality

On the generalization of learning algorithms that do not converge 22

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On the generalization of learning algorithms that do not con...

Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Nisha Chandramoorthy Andreas Loukas Khashayar Gatmiry Stefanie Jegelka Institute for Data Systems and Society Massachusetts Institute of Technology Prescient Design Genentech Roche Electrical Engineering and Computer Science Massachusetts Institute of Technology

ISBN: (纸本)9781713871088

Generalization analyses of deep learning typically assume that the training converges to a fixed point. But, recent results indicate that in practice, the weights of deep neural networks optimized with stochastic gradient descent often oscillate indefinitely. To reduce this discrepancy between theory and practice, this paper focuses on the generalization of neural networks whose training dynamics do not necessarily converge to fixed points. Our main contribution is to propose a notion of statistical algorithmic stability (SAS) that extends classical algorithmic stability to non-convergent algorithms and to study its connection to generalization. This ergodic-theoretic approach leads to new insights when compared to the traditional optimization and learning theory perspectives. We prove that the stability of the time-asymptotic behavior of a learning algorithm relates to its generalization and empirically demonstrate how loss dynamics can provide clues to generalization performance. Our findings provide evidence that networks that "train stably generalize better" even when the training continues indefinitely and the weights do not converge.

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Electronic Energy System and Energy Management Algorithm for FutureHAUS

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Electronic Energy System and Energy Management Algorithm for...

IEEE Conference on Power Electronics and Renewable Energy (CPERE)

作者： Vladimir Mitrovic Igor Cvetkovic Dushan Boroyevich Joseph Wheeler Robert Vance The Bradley Dep. of Electrical and Computer Engineering College of Engineering Center for Power Electronics Systems Virginia Polytechnic Institute and State University Blacksburg VA USA Center for Design Research School of Architecture + Design College of Architecture and Urban Studies Virginia Polytechnic Institute and State University Blacksburg VA USA

Technology advancements that happened in the last two decades have changed the way how people live, interact, and work more than any technology did in the past 100 years or so. Not only little “gadgets” enabled this enormous paradigm shift, the new innovative concepts have been well integrated into the design and manufacturing of computers, appliances, automobiles, airplanes, and ships, and it was about time to start seeing these concepts applied to the homes where building practices, by contrast, experienced a very slow, if not resistant effort to advance. At least they did before Virginia Tech demonstrated the use of advanced manufacturing concepts, prefabricated structures, and a great number of power electronics to redefine the conventional practice of modern home design. Its FutureHAUS won first place at the international competition in Dubai, UAE, validating an enormous societal desire to see this change finally happening. This paper will describe details of the electronic energy system implementation as well as an energy management algorithm developed to successfully achieve a net-positive energy balance in the FutureHAUS.

关键词： Resistance computers Renewable energy sources Home appliances Decision making Inverters Manufacturing

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

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

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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Using Affect as a Communication Modality to Improve Human-Robot Communication in Robot-Assisted Search and Rescue Scenarios

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arXiv 2022年

作者： Akgun, Sami Alperen Ghafurian, Moojan Crowley, Mark Dautenhahn, Kerstin The Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering and Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Emotions can provide a natural communication modality to complement the existing multi-modal capabilities of social robots, such as text and speech, in many domains. We conducted three online studies with 112, 223 and 151 participants to investigate the benefits of using emotions as a communication modality for Search And Rescue (SAR) robots. In the first experiment, we investigated the feasibility of conveying information related to SAR situations through robots' emotions, resulting in mappings from SAR situations to emotions. The second study used Affect Control Theory as an alternative method for deriving such mappings. This method is more flexible, e.g. allows for such mappings to be adjusted for different emotion sets and different robots. In the third experiment, we created affective expressions for an appearance-constrained outdoor field research robot using LEDs as an expressive channel. Using these affective expressions in a variety of simulated SAR situations, we evaluated the effect of these expressions on participants' (adopting the role of rescue workers) situational awareness. Our results and proposed methodologies provide (a) insights on how emotions could help conveying messages in the context of SAR, and (b) evidence on the effectiveness of adding emotions as a communication modality in a (simulated) SAR communication context. © 2022, CC BY.

关键词： Control theory

Electric Vehicles Security and Privacy: Challenges, Solutions, and Future Needs

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arXiv 2023年

作者： Brighente, Alessandro Conti, Mauro Donadel, Denis Poovendran, Raadha Turrin, Federico Zhou, Jianying The Department of Mathematics HIT Research Center University of Padova Padova35131 Italy The Department of Electrical and Computer Engineering University of Washington Seattle98195 United States School of Information Systems Technology and Design Singapore University of Technology and Design 487372 Singapore

Electric Vehicles (EVs) share common technologies with classical fossil-fueled cars, but they also employ novel technologies and components (e.g., Charging System and Battery Management System) that create an unexplored attack surface for malicious users. Although multiple contributions in the literature explored cybersecurity aspects of particular components of the EV ecosystem (e.g., charging infrastructure), there is still no contribution to the holistic cybersecurity of EVs and their related technologies from a cyber-physical system perspective. In this paper, we provide the first in-depth study of the security and privacy threats associated with the EVs ecosystem. We analyze the threats associated with both the EV and the different charging solutions. Focusing on the Cyber-Physical systems (CPS) paradigm, we provide a detailed analysis of all the processes that an attacker might exploit to affect the security and privacy of both drivers and the infrastructure. To address the highlighted threats, we present possible solutions that might be implemented. We also provide an overview of possible future directions to guarantee the security and privacy of the EVs ecosystem. Based on our analysis, we stress the need for EV-specific cybersecurity solutions. © 2023, CC BY-NC-ND.

关键词： Electric vehicles

Data-Induced Interactions of Sparse Sensors

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arXiv 2023年

作者： Klishin, Andrei A. Kutz, J. Nathan Manohar, Krithika AI Institute in Dynamic Systems University of Washington SeattleWA98195 United States Department of Mechanical Engineering University of Washington SeattleWA98195 United States Departments of Applied Mathematics and Electrical and Computer Engineering University of Washington SeattleWA98195 United States

Large-dimensional empirical data in science and engineering frequently has low-rank structure and can be represented as a combination of just a few eigenmodes. Because of this structure, we can use just a few spatially localized sensor measurements to reconstruct the full state of a complex system. The quality of this reconstruction, especially in the presence of sensor noise, depends significantly on the spatial configuration of the sensors. Multiple algorithms based on gappy interpolation and QR factorization have been proposed to optimize sensor placement. Here, instead of an algorithm that outputs a singular "optimal" sensor configuration, we take a thermodynamic view to compute the full landscape of sensor interactions induced by the training data. The landscape takes the form of the Ising model in statistical physics, and accounts for both the data variance captured at each sensor location and the crosstalk between sensors. Mapping out these data-induced sensor interactions allows combining them with external selection criteria and anticipating sensor replacement impacts. Copyright © 2023, The Authors. All rights reserved.

关键词： Ising model