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24th ACM International Conference on Hybrid systems Computation and Control, HSCC 2021, held as part of the 14th Cyber Physical systems and Internet-of-Things Week, CPS-IoT Week 2021

作者： Lavaei, Abolfazl Nejati, Ameneh Soudjani, Sadegh Zamani, Majid Institute for Dynamic Systems and Control Eth Zurich Switzerland Department of Electrical Engineering Technical University of Munich Germany School of Computing Newcastle University United Kingdom Department of Computer Science University of Colorado Boulder United States

ISBN: (纸本)9781450383394

In this work, we propose a data-driven technique for a formal estimation of infinitesimal generators of continuous-time stochastic systems with unknown dynamics. In the proposed framework, we first approximate the infinitesimal generator of the solution process via a set of data collected from solution processes of unknown systems. We then put some proper assumptions on dynamics of systems and quantify the closeness between the infinitesimal generator and its approximation while providing a priori guaranteed confidence bound. We show that both the time discretization and the number of data play significant roles in providing a reasonable closeness precision. Motivations and Related Works. Infinitesimal generator of a stochastic process (i.e., a partial differential operator that encodes a great deal of information about the process) plays a principal role in many analysis frameworks including: (i) stability analysis of continuous-time stochastic systems via Lyapunov functions [1], (ii) establishing similarity relations between two continuous-time stochastic systems via stochastic simulation functions [2], (iii) constructing finite abstractions of continuous-time stochastic control systems [3], and (iv) safety analysis of continuous-time stochastic systems via barrier certificates [4], to name a few. In the relevant literature, only [5] studies the estimation of infinitesimal generators, whose approach is based on the assumption of knowing the precise model and discretizing both time and state. To the best of our knowledge, our work is the first to propose a method for estimating the infinitesimal generator of stochastic systems with unknown dynamics while providing formal guarantees. The complete version of this work can be found in [6]. © 2021 Owner/Author.

关键词： Stochastic systems

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Triple IRS-Aided Communications: Row-Column Sparsity Enhanced Bayesian Tensor Learning for Channel Estimation

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IEEE Transactions on Communications 2025年

作者： Hu, Limei Shao, Xiaodan Qiu, Tingzhi Chen, Feng Cheng, Lei Wu, Qingqing Southwest University College of Artificial Intelligence College of Electronic and Information Engineering Brain-Inspired Computing and Intelligent Control of Chongqing Key Laboratory National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Zhejiang University College of Information Science and Electronic Engineering Hangzhou310058 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

Channel acquisition presents a major challenge in deploying intelligent reflecting surfaces (IRS) aided communication systems, due to massive reflective elements that create a complex multi-path channel and increase channel dimensions. For an IRS-aided communication system, complete channel includes three parts: From the users to IRS, from the IRS to BS, and from the BS back to the IRS. Thus, a generalized multi-IRS cascaded communication system with three cascaded IRSs is considered. Unfortunately, existing channel estimation methods focus on single or double IRS cascades, which is not applicable to the case of triple cascaded IRS channel estimation directly. In this paper, we study the uplink channel estimation for triple cascaded IRSs aided single-user single-input single-output (SISO) systems. Specifically, the triple IRS cascaded channel is typically sparse. It permits us to characterize the channel estimation as a problem of sparse matrix recovery. Then, the sparse learning is explored to achieve robust channel estimation with limited training overhead. Particularly, the sparse channel matrices of the cascaded triple IRS channels have a common row-column block sparsity structure. However, a unique challenge lies in characterizing and enhancing such a common row-column sparsity. To tackle this issue, we apply a random matrix prior to promote the common row-column-wise sparsity of the channel matrix, and then an efficient Bayesian tensor inference algorithm is proposed to estimate the IRS channel. Finally, simulation results confirm that the proposed scheme outperforms traditional counterparts in terms of accuracy. © 1972-2012 IEEE.

关键词： Channel estimation

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An Enhanced Framework for 5G Network Slicing Optimization With Prioritized Resource Allocation Using 2-Edge-Connected Subgraphs

An Enhanced Framework for 5G Network Slicing Optimization Wi...

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International Colloquium on Smart Grid Metrology (SmaGriMet)

作者： Igor Begić Drago Bago Adrian Satja Kurdija Department of Telecommunications JP Elektroprivreda HZ HB d.d. Mostar Mostar Bosnia and Herzegovina Management Board JP Elektroprivreda HZ HB d.d. Mostar Mostar Bosnia and Herzegovina Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (数字)9798350357028

ISBN: (纸本)9798350357035

Emerging 5G network slicing technologies require robust optimization frameworks to ensure efficient resource allocation and path protection. This paper presents an improved slicing optimization model that enhances resource distribution while maintaining computational efficiency. The model builds upon our previously developed framework, which utilizes 2-edge-connected subgraphs for path protection. The proposed enhancement introduces prioritized resource allocation by integrating a weighting factor that optimally balances slice importance and resource distribution, without increasing algorithmic complexity. The model is evaluated against our previous 2-edge-connected subgraph framework. Simulation results demonstrate that the improved model achieves better resource efficiency and fairness, while maintaining network resilience and ensuring optimal performance in dynamic network environments. The proposed framework provides valuable insights for network operators in optimizing 5G slicing strategies under constrained resource conditions.

关键词： 5G mobile communication Network slicing Heuristic algorithms Optimization models Computational modeling Dynamic scheduling Computational efficiency Resource management Protection Optimization

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FeReX: A Reconfigurable Design of Multi-bit Ferroelectric Compute-in-Memory for Nearest Neighbor Search

arXiv

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arXiv 2024年

作者： Xu, Zhicheng Liu, Che-Kai Li, Chao Mao, Ruibin Yang, Jianyi Kämpfe, Thomas Imani, Mohsen Li, Can Zhuo, Cheng Yin, Xunzhao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Zhejiang University Hangzhou China School of Electrical and Computer Engineering Georgia Institute of Technology United States Center Nanoelectric Technologies Fraunhofer IPMS Dresden Germany Department of Computer Science University of California Irvine United States Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

Rapid advancements in artificial intelligence have given rise to transformative models, profoundly impacting our lives. These models demand massive volumes of data to operate effectively, exacerbating the data-transfer bottleneck inherent in the conventional von-Neumann architecture. Compute-in-memory (CIM), a novel computing paradigm, tackles these issues by seamlessly embedding in-memory search functions, thereby obviating the need for data transfers. However, existing non-volatile memory (NVM)-based accelerators are application specific. During the similarity based associative search operation, they only support a single, specific distance metric, such as Hamming, Manhattan, or Euclidean distance in measuring the query against the stored data, calling for reconfigurable in-memory solutions adaptable to various applications. To overcome such a limitation, in this paper, we present FeReX, a reconfigurable associative memory (AM) that accommodates various distance metrics including Hamming, Manhattan, and Euclidean distances. Leveraging multibit ferroelectric field-effect transistors (FeFETs) as the proxy and a hardware-software co-design approach, we introduce a constrained satisfaction problem (CSP)-based method to automate AM search input voltage and stored voltage configurations for different distance based search functions. Device-circuit co-simulations first validate the effectiveness of the proposed FeReX methodology for reconfigurable search distance functions. Then, we benchmark FeReX in the context of k-nearest neighbor (KNN) and hyperdimensional computing (HDC), which highlights the robustness of FeReX and demonstrates up to 250× speedup and 104 energy savings compared with GPU. Copyright © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search

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MIM and MIIM-Based Optical Rectennas for Infrared Energy Harvesting at 10.6 μm

MIM and MIIM-Based Optical Rectennas for Infrared Energy Har...

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Mediterrannean Microwave Symposium (MMS)

作者： Ali Yahyaoui Ahmad Elsharabasy Jawad Yousaf Hatem Rmili Taoufik Aguili University of Tunis El Manar (UTM) National Engineering School of Tunis (ENIT) Communications Systems Laboratory (SysCom) Tunis Tunisia Electrical and Computer Engineering Department McMaster University Hamilton ON Canada Department of Electrical Computer and Biomedical Engineering Abu Dhabi University United Arab Emirates Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

This work presents a comparative study between metal-insulator-metal (MIM) and metal-insulator-insulator-metal (MIIM) based optical log spiral rectennas for infrared IR energy harvesting at 10.6 μm. We have considered the spiral antenna terminals as the electrodes of the rectifying diode and we have integrated the insulators between the two arms (electrodes) to enhance the harvested energy with the proposed nano-antennas. The study presents a comparison between the performance (E-field, I/V, responsivity, and resistivity) of the two proposed rectennas.

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cVTS: A Constrained Voronoi Tree Search Method for High Dimensional Analog Circuit Synthesis

cVTS: A Constrained Voronoi Tree Search Method for High Dime...

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Design Automation Conference

作者： Aidong Zhao Xianan Wang Zixiao Lin Zhaori Bi Xudong Li Changhao Yan Fan Yang Li Shang Dian Zhou Xuan Zeng Microelectronics Department State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China Department of Electrical Engineering University of Texas Dallas USA Department of Electrical and Computer Engineering University of California San Diego USA School of Data Science Fudan University Shanghai China School of Computer Science Fudan University Shanghai China

A constrained Voronoi tree-based domain decomposition method for high-dimensional Bayesian optimization is proposed to solve large scale analog circuit synthesis problems, which can be formulated as high-dimensional heterogeneous black-box optimization. Hierarchical Voronoi tree progressively breaks down the design space into partitions with implicit performance boundaries such that promising regions are efficiently explored. Fast exploitation is ensured in Voronoi nest via local Bayesian optimization with a few observations. A slice-enhanced Gibbs sampling method is proposed to sample acquisition function cMES in irregular polyhedrons with design constraints. Compared with state-of-the-art methods, cVTS achieves significant speed up without loss of accuracy.

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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IEEE Conference on Wireless Communications and Networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

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Challenges in Cyber Physical Social systems and Internet of Things

Challenges in Cyber Physical Social Systems and Internet of ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Revathi J. Raja M Mohanraj K. Malathi Balasubbareddy Mallala R.G. Vidhya Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Business Systems Sri Eshwar college of engineering Coimbatore India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of ECE HKBK College of Engineering Bangalore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This research analyses the complex dynamics of Cyber-Physical-Social systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (IoT), and social media. By exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems.

关键词： Data privacy Social networking (online) Shape Media Real-time systems Internet of Things Protection Predictive analytics Monitoring Resilience

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Exploring Equilibrium Strategies in Network Games with Generative AI

arXiv

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arXiv 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu The College of Communications Engineering Army Engineering University PLA Nanjing210000 China The School of Computer Science and Engineering NTU Singapore The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 2024, CC BY.

关键词： Game theory

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On the Capacity of Vehicular Communications

On the Capacity of Vehicular Communications

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National Conference with International Participation (TELECOM)

作者： Paschalis C. Sofotasios Seong Ki Yoo Nidhi Simmons Simon L. Cotton Mikko Valkama Department of Electrical Engineering and Computer Science Center for Cyber Physical Systems Khalifa University Abu Dhabi UAE Department of Electrical Engineering Tampere University Tampere Finland Centre for Future Transport and Cities Faculty of Engineering Environment and Computing Coventry University UK Institute of Electronics Communications and Information Technology Queen’s University Belfast Belfast UK

The κ-μ / inverse gamma and η-μ / inverse gamma distributions constitute two of the most distinct composite fading models. The present work offers novel results on the achievable ergodic capacity over these composite multipath/shadowing conditions. The derived expressions are corroborated with respective simulation results and are shown to be useful in practical wireless scenarios in the context of device to device communications, such as off-body, and vehicle-to-vehicle communications.

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