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arXiv 2024年

作者： Tian, Maojiang Chen, Minyang Du, Wei Tang, Yang Jin, Yaochu Yen, Gary G. The Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China The Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai200237 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The School of Engineering Westlake University Hangzhou310030 China The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74048 United States

Cooperative co-evolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of non-additively separable functions, it suffers from high computational complexity. To address this challenge, this article proposes a composite separability grouping (CSG) method, seamlessly integrating DG and GSG into a problem decomposition framework to utilize the strengths of both approaches. CSG introduces a step-by-step decomposition framework that accurately decomposes various problem types using fewer computational resources. By sequentially identifying additively, multiplicatively and generally separable variables, CSG progressively groups non-separable variables by recursively considering the interactions between each non-separable variable and the formed non-separable groups. Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a non-separable variable grouping method. These two methods are designed to effectively detect multiplicatively separable variables and efficiently group non-separable variables, respectively. Extensive experimental results demonstrate that CSG achieves more accurate variable grouping with lower computational complexity compared to GSG and state-of-the-art DG series designs. © 2024, CC BY.

关键词： Efficiency

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Resource-Aware Split Federated Learning for Fall Detection in the Metaverse

Resource-Aware Split Federated Learning for Fall Detection i...

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Mohamad Wazzeh Ahmad Hammoud Mohsen Guizani Azzam Mourad Hadi Otrok Chamseddine Talhi Zbigniew Dziong Chang-Dong Wang Department of Software and IT Engineering Ecole de Technologie Supérieure Montreal Canada Department of CSM Artificial Intelligence and Cyber Systems Research Center Lebanese American University Beirut Lebanon Department of ML Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE Department of Electrical Engineering Ecole de Technologie Supérieure Montreal Canada Department of Computer Science KU 6G Research Center Khalifa University Abu Dhabi UAE Department of Computer Science Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

As the Metaverse develops, it is becoming more crucial to prioritize the safety of users, especially regarding the potential risks, such as users experiencing dizziness or making incorrect movements that may lead to falls. With more virtual environments becoming increasingly available and immersive, detecting and preventing falls within the Metaverse is required. Given the constrained resources of wearable sensors, precise fall prediction models are critical to efficiently analyzing data gathered by these devices. Traditional fall detection systems require centralizing data collection, which raises privacy concerns over the collected data. Resource-aware Split Federated Learning (RSFL) enables collaboration among multiple devices within the Metaverse to train a fall detection model, all while preserving individual data privacy. The approach also leverages parallelism in Federated Learning (FL) and Split Learning (SL) by decomposing training tasks between clients and servers. Moreover, we devise an efficient client selection mechanism to ensure timely training and model convergence performance. We implemented our architecture and assessed its performance using a sensory dataset. The evaluation results with the baseline demonstrate our architecture's superiority in terms of convergence time. Our approach mitigates data heterogeneity and privacy concerns, creating secure and efficient fall detection systems for the Metaverse.

关键词： Training Wireless communication Data privacy Wireless sensor networks Metaverse Federated learning computer architecture Data models Fall detection Convergence

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Multi-hop Multi-RIS Wireless Communication systems: Multi-reflection Path Scheduling and Beamforming

arXiv

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arXiv 2024年

作者： Ma, Xiaoyan Zhang, Haixia Chen, Xianhao Fang, Yuguang Yuan, Dongfeng Shandong Key Laboratory of Wireless Communication Technologies Jinan250061 China School of Information Science and Engineering Shandong University Qingdao266237 China The School of Control Science and Engineering Shandong University Jinan250061 China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The Department of Computer Science City University of Hong Kong Hong Kong

Reconfigurable intelligent surface (RIS) provides a promising way to proactively augment propagation environments for better transmission performance in wireless communications. Existing multi-RIS works mainly focus on link-level optimization with predetermined transmission paths, which cannot be directly extended to system-level management, since they neither consider the interference caused by undesired scattering of RISs, nor the performance balancing between different transmission paths. To address this, we study an innovative multi-hop multi-RIS communication system, where a base station (BS) transmits information to a set of distributed users over multi-RIS configuration space in a multi-hop manner. The signals for each user are subsequently reflected by the selected RISs via multi-reflection line-of-sight (LoS) links. To ensure that all users have fair access to the system to avoid excessive number of RISs serving one user, we aim to find the optimal beam reflecting path for each user, while judiciously determining the path scheduling strategies with the corresponding beamforming design to ensure the fairness. Due to the presence of interference caused by undesired scattering of RISs, it is highly challenging to solve the formulated multi-RIS multi-path beamforming optimization problem. To solve it, we first derive the optimal RISs’ phase shifts and the corresponding reflecting path selection for each user based on its practical deployment location. With the optimized multi-reflection paths, we obtain a feasible user grouping pattern for effective interference mitigation by constructing the maximum independent sets (MISs). Finally, we propose a joint heuristic algorithm to iteratively update the beamforming vectors and the group scheduling policies to maximize the minimum equivalent data rate of all users. Numerical results demonstrate that the proposed transmission framework achieves superior throughput performance than benchmark schemes. Useful insights on

关键词： Beamforming

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H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture

arXiv

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arXiv 2022年

作者： Zhang, Chengming Geng, Tong Guo, Anqi Tian, Jiannan Herbordt, Martin Li, Ang Tao, Dingwen School of Electrical Engineering and Computer Science Washington State University PullmanWA United States Mathematics and Computer Science Division Pacific Northwest National Laboratory RichlandWA United States Department of Electrical and Computer Engineering Boston University BostonMA United States Department of Electrical and Computer Engineering University of Rochester RochesterNY United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs. Compared with other Machine Learning (ML) modalities, the acceleration of Graph Neural Networks (GNNs) is more challenging due to the irregularity and heterogeneity derived from graph typologies. Existing efforts, however, have focused mainly on handling graphs’ irregularity and have not studied their heterogeneity. To this end we propose H-GCN, a PL (Programmable Logic) and AIE (AI Engine) based hybrid accelerator that leverages the emerging heterogeneity of Xilinx Versal Adaptive Compute Acceleration Platforms (ACAPs) to achieve high-performance GNN inference. In particular, H-GCN partitions each graph into three subgraphs based on its inherent heterogeneity, and processes them using PL and AIE, respectively. To further improve performance, we explore the sparsity support of AIE and develop an efficient density-aware method to automatically map tiles of sparse matrix-matrix multiplication (SpMM) onto the systolic tensor array. Compared with state-of-the-art GCN accelerators, H-GCN achieves, on average, speedups of 1.1∼2.3×. © 2022, CC BY.

关键词： Graph neural networks

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Leveraging Deep Reinforcement Learning for Geolocation-based MIMO Transmission in FD-RAN

Leveraging Deep Reinforcement Learning for Geolocation-based...

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IEEE International Conference on Communications in China (ICCC)

作者： Zongxi Liu Kai Yu Tianqi Zhang Jingbo Liu Jiacheng Chen Haibo Zhou Xuemin Sherman Shen School of Electronic Science and Engineering Nanjing University Nanjing China Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

In the fully-decoupled radio access network (FD-RAN), the base stations (BSs) are classified as control BSs, uplink BSs, and downlink BSs for better flexibility of the BS-user equipment (UE) association and elasticity of resource scheduling. However, the high feedback delay brought by the physically decoupling architecture renders conventional feedback-based MIMO transmission ineffective. Considering the strong correlation between user geolocation and the channel propagation environment, especially when a line-of-sight (LOS) path exists, we propose a geolocation-based MIMO transmission scheme that determines the user’s precoder and modulation and coding scheme (MCS) without requiring the conventional feedback procedures. To capture the correlation, we utilize deep reinforcement learning (DRL) to perform the task of mapping from geolocation to transmission parameters. Based on the principles of Type I codebook, we further decompose the task into subtasks to reduce the action space. The extensive simulations on realistic ray-tracing channels demonstrate comparable performance to CSI feedback-based methods in 5G under ideal (zero feedback delay) conditions and better performance against 5G (with feedback delay) and DRL without task decomposition.

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Faithfully Simulating Near-Term Quantum Repeaters

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PRX Quantum 2024年第1期5卷 010351-010351页

作者： Julius Wallnöfer Frederik Hahn Fabian Wiesner Nathan Walk Jens Eisert Dahlem Center for Complex Quantum Systems Freie Universität Berlin Arnimallee 14 14195 Berlin Germany Electrical Engineering and Computer Science Department Technische Universität Berlin 10587 Berlin Germany Helmholtz-Zentrum Berlin für Materialien und Energie Hahn-Meitner-Platz 1 14109 Berlin Germany

Quantum repeaters have long been established to be essential for distributing entanglement over long distances. Consequently, their experimental realization constitutes a core challenge of quantum communication. However, there are numerous open questions about implementation details for realistic near-term experimental setups. In order to assess the performance of realistic repeater protocols, here we present ReQuSim, a comprehensive Monte Carlo–based simulation platform for quantum repeaters that faithfully includes loss and models a wide range of imperfections such as memories with time-dependent noise. Our platform allows us to perform an analysis for quantum repeater setups and strategies that go far beyond known analytical results: This refers to being able to both capture more realistic noise models and analyze more complex repeater strategies. We present a number of findings centered around the combination of strategies for improving performance, such as entanglement purification and the use of multiple repeater stations, and demonstrate that there exist complex relationships between them. We stress that numerical tools such as ours are essential to model complex quantum communication protocols aimed at contributing to the quantum Internet.

关键词： Quantum communication Quantum entanglement Quantum repeaters

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When is the estimated propensity score better? High-dimensional analysis and bias correction

arXiv

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arXiv 2023年

作者： Su, Fangzhou Mou, Wenlong Ding, Peng Wainwright, Martin J. Department of Electrical Engineering and Computer Sciences United States Department of Statistics UC Berkeley United States Department of Mathematics Lab for Information and Decision Systems and Statistics and Data Science Center Massachusetts Institute of Technology United States

Anecdotally, using an estimated propensity score is superior to the true propensity score in estimating the average treatment effect based on observational data. However, this claim comes with several qualifications: it holds only if propensity score model is correctly specified and the number of covariates d is small relative to the sample size n. We revisit this phenomenon by studying the inverse propensity score weighting (IPW) estimator based on a logistic model with a diverging number of covariates. We first show that the IPW estimator based on the estimated propensity score is consistent and asymptotically normal with smaller variance than the oracle IPW estimator (using the true propensity score) if and only if n & d2. We then propose a debiased IPW estimator that achieves the same guarantees in the regime n & d3/2. Our proofs rely on a novel non-asymptotic decomposition of the IPW error along with careful control of the higher order terms. Copyright © 2023, The Authors. All rights reserved.

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Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services

arXiv

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arXiv 2022年

作者： Du, Hongyang Wang, Jiacheng Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In 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 University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Emerging with the support of computing and communications technologies, Metaverse is expected to bring users unprecedented service experiences. However, the increase in the number of Metaverse users places a heavy demand on network resources, especially for Metaverse services that are based on graphical extended reality and require rendering a plethora of virtual objects. To make efficient use of network resources and improve the Quality-of-Experience (QoE), we design an attention-aware network resource allocation scheme to achieve customized Metaverse services. The aim is to allocate more network resources to virtual objects in which users are more interested. We first discuss several key techniques related to Metaverse services, including QoE analysis, eye-tracking, and remote rendering. We then review existing datasets and propose the user-object-attention level (UOAL) dataset that contains the ground truth attention of 30 users to 96 objects in 1, 000 images. A tutorial on how to use UOAL is presented. With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i.e., attention prediction and QoE maximization. Specially, we provide an overview of the designs of two types of attention prediction methods, i.e., interest-aware and time-aware prediction. By using the predicted user-object-attention values, network resources such as the rendering capacity of edge devices can be allocated optimally to maximize the QoE. Finally, we propose promising research directions related to Metaverse services. © 2022, CC BY.

关键词： Eye tracking

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UVM Verification IP for AXI

UVM Verification IP for AXI

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2021 IEEE East-West Design and Test Symposium, EWDTS 2021

作者： Melikyan, Vazgen Harutyunyan, Stepan Kirakosyan, Artak Kaplanyan, Taron National Polytechnic University of Armenia Chair of Microelectronic Circuits and Systems Synopsys Armenia Educational Department Yerevan Armenia University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (纸本)9781665445030

Over time, the complexity of ICs design increasing which making these designs more error-prone. Verification of Integrated Circuits using Verilog lacks the flexibility and reusability of the environment. System Verilog UVM methodology gives building blocks and OOP concepts to work with. That allows to create much more flexible test environment with reusable components. This paper presents a verification architecture of configurable Verification IP for AXI interface. This paper presents an architecture of verification environment for AMBA AXI interface and can be used to test any AXI device. A functional coverage model has been developed to determine if the verification process covers all possible scenarios or not. Each testcase reports coverage which is later used to analyze the effectiveness of the testcase. Full coverage has been achieved using both random and directed test cases. The coding is done using System Verilog and the simulation is done using VCS. © 2021 IEEE.

关键词： Reusability

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Deep Reinforcement Learning for Sleep Control in 5G and Beyond Radio Access Networks: An Overview

Deep Reinforcement Learning for Sleep Control in 5G and Beyo...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Nessrine Trabelsi Rihab Maaloul Lamia Chaari Fourati Wael Jaafar Laboratory of Signals systeMs aRtificial intelligence and neTworkS – Digital Research Center of Sfax Sfax Tunisia Higher Institute of Computer Science of Mahdia (ISIMa) University of Monastir Tunisia Higher Institute of Computer Science and Multimedia in Sfax (ISIMS) University of Sfax Tunisia Department of Software and IT Engineering École de technologies supérieure (ÉTS) Montreal Canada

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The advent of 5G and beyond networks is envisioned to support lower latency, higher data rates, and wider connectivity than previous cellular network generations. However, given the denser deployment of base stations (BSs) to accommodate such improvements, this results inevitably in a significant and unsustainable increase in the network’s energy consumption. Sleep Control (SC), which allows switching off some BS hardware components during light-traffic time, is considered a viable solution for greener and more energy-efficient Radio Access Networks (RAN). However, the optimization of SC is a highly challenging large-scale network combinatorial problem that depends on dynamic wireless channel conditions and varying traffic demands with stringent Quality-of-Service (QoS) requirements. Driven by the benefits and efficiency of Deep Reinforcement Learning (DRL), which has been successfully applied to multiple wireless network optimization problems, this paper investigates DRL approaches addressing sleep control in 5G and beyond RAN. To this end, we propose a taxonomy to classify the related literature. Then, we provide an overview of the different components of the Markov Decision Process (MDP) modeling the sequential decision-making of sleep control and the applied DRL algorithms. Finally, we highlight the main challenges in existing works and suggest novel strategies to address them.

关键词： 5G mobile communication Wireless networks Taxonomy Decision making Quality of service Switches Deep reinforcement learning

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