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Dynamic Placement of O-CU and O-DU Functionalities in open-RAN Architecture 20

Dynamic Placement of O-CU and O-DU Functionalities in Open-R...

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20th Annual ieee International conference on Sensing, Communication, and networking (SECON)

作者： Hojeij, Hiba Sharara, Mahdi Hoteit, Sahar Veque, Veronique Univ Paris Saclay CNRS CentraleSupelec Lab Signaux & Syst L2S F-91190 Gif Sur Yvette France

ISBN: (纸本)9798350300529

open Radio Access network (O-RAN) has recently emerged as a new trend for mobile network architecture. It is based on four founding principles: disaggregation, intelligence, virtualization, and open interfaces. In particular, RAN disaggregation involves dividing base station virtualized networking functions (VNFs) into three distinct components - the open-Central Unit (O-CU), the open-Distributed Unit (O-DU), and the open-Radio Unit (O-RU) - enabling each component to be implemented independently. Such disaggregation aims to improve system performance and allow rapid and open innovation in many components while ensuring multi-vendor operability. As the disaggregation of network architecture becomes a key enabler of O-RAN, the deployment scenarios of VNFs over O-RAN clouds become critical. In this context, we propose an optimal and dynamic placement scheme of the O-CU and O-DU functionalities either on the edge or in regional O-clouds. The objective is to maximize users' admittance ratio by considering mid-haul delay and server capacity requirements. We develop an Integer Linear programming (ILP) model for VNF placement in O-RAN architecture. Additionally, we introduce a Recurrent Neural network (RNN) heuristic model that can effectively replicate the behavior of the ILP model. We get promising results in terms of improving users' admittance ratio by up to 10% when compared to baselines from state-of-the-art. Moreover, our proposed model minimizes the deployment costs and increases the overall throughput.

关键词： network architecture

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Context-based Interpretable Spatio-Temporal Graph Convolutional network for Human Motion Forecasting

Context-based Interpretable Spatio-Temporal Graph Convolutio...

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ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Medina, Edgar Loh, Leyong Gurung, Namrata Oh, Kyung Hun Heller, Niels QualityMinds GmbH Berlin Germany

ISBN: (纸本)9798350318920;9798350318937

Human motion prediction is still an open problem extremely important for autonomous driving and safety applications. Due to the complex spatiotemporal relation of motion sequences, this remains a challenging problem not only for movement prediction but also to perform a preliminary interpretation of the joint connections. In this work, we present a Context-based Interpretable Spatio-Temporal Graph Convolutional network (CIST-GCN), as an efficient 3D human pose forecasting model based on GCNs that encompasses specific layers, aiding model interpretability and providing information that might be useful when analyzing motion distribution and body behavior. Our architecture extracts meaningful information from pose sequences, aggregates displacements and accelerations into the input model, and finally predicts the output displacements. Extensive experiments on Human 3.6M, AMASS, 3DPW, and ExPI datasets demonstrate that CIST-GCN outperforms previous methods in human motion prediction and robustness. Since the idea of enhancing interpretability for motion prediction has its merits, we showcase experiments towards it and provide preliminary evaluations of such insights here. (1)

关键词： 3D computer vision Algorithms Algorithms and algorithms Applications Autonomous Driving formulations Machine learning architectures

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Millimeter Accuracy Indoor Localization System Using an Attention Convolution Model 25

Millimeter Accuracy Indoor Localization System Using an Atte...

引用

ieee Wireless Communications and networking conference (ieee WCNC)

作者： Ma, Jiteng Qiao, Liang Wang, Zhiyu Dang, Shuping Beach, Mark Univ Bristol Sch Elect Elect & Mech Engn Bristol Avon England

ISBN: (纸本)9798350303582;9798350303599

This paper presents a novel deep learning model for achieving millimeter accuracy for indoor localization. The model comprises a multi-head self-attention model and a convolutional neural network (CNN), allowing for robust feature extraction from the captured wireless signals. To further enhance the localization accuracy, we also introduced a data augmentation method to increase the size and diversity of the dataset by creating synthetic variants. The performance of the proposed model is tested on an open dataset containing measured channel state information (CSI) signals from a massive multiple-input multiple-output (MIMO) system. The validation accuracies for all three cases are more than seven times higher than the state of the art. The model has also been further evaluated in the COST INTERACT CA20120 Machine Learning Challenge. The performance of our model is competitive with the measured position and significantly outperforms other teams. The proposed model and the associated approaches contribute to the development of practical millimeter-level indoor localization systems using deep learning architectures.

关键词： Indoor localization channel state information (CSI) deep learning self-attention mechanism convolutional neural network (CNN)

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Integrating network Security into Software Defined networking (SDN) architectures 2

Integrating Network Security into Software Defined Networkin...

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2nd ieee International conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Polimetla, Kiran University Of North Dakota School of Electrical Engineering and Computer Science Grand ForksND United States Adobe Inc. Dept. of Information Technology AustinTX United States

ISBN: (纸本)9798350317558

The decision for introduced green and cozy networking solutions has grown as the era continues to evolve. With centralized network control and programmability functions, software applications defined as networking (SDN) have emerged as a promising method to address this call. But, as SDN architectures become vaster, the need for strong community protection turns into even extra important. Integrating community protection into SDN structure includes enforcing safety mechanisms straight away into the manipulate aircraft, considering actual-time-grained manipulation over community site visitors. This may be done through protection guidelines and policies that can be dynamically completed and updated via the centralized controller. With the aid of leveraging the programmable nature of SDN, network protection may be, without troubles, tailored to satisfy the changing hazard panorama. The open API in SDN also permits integration with gift-day protection answers, firewalls, and intrusion detection systems, similarly, to improving the community's overall safety. © 2024 ieee.

关键词： Architecture Dynamically networking Protection Technology Visitors

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Sustainable placement of VNF chains in Intent-based networking 16

Sustainable placement of VNF chains in Intent-based Networki...

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16th ieee/ACM International conference on Utility and Cloud Computing (UCC) / 10th ieee/ACM International conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Di Riccio, Tommaso Massa, Jacopo Forti, Stefano Brogi, Antonio Univ Pisa Dept Comp Sci Pisa Italy

ISBN: (纸本)9798400702341

Intent-based networking (IBN) aims at automatically driving network configurations and operations to fulfil high-level intents specified by infrastructure providers and application operators. Deploying Virtual network Function (VNF) chains onto the available network resources is a way to model and fulfil intents. Focussing on the Cloud-Edge continuum, this article describes and compares a mathematical programming and a declarative heuristic methodology to solve the problem of placing VNF chains to fulfil multiple intents in IBN settings. Taking the infrastructure operators' viewpoint, our approach aims at balancing the trade-off between environmental sustainability and profit. We release an open-source prototype of the proposed methodology and assess its scalability and functioning via simulation over lifelike data.

关键词： Mathematical programming

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ACCELERATING BRAIN RESEARCH USING EXPLAINABLE ARTIFICIAL INTELLIGENCE

ACCELERATING BRAIN RESEARCH USING EXPLAINABLE ARTIFICIAL INT...

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ieee International conference on Multimedia and Expo (ieee ICME)

作者： Chou, Jing-Lun Huang, Ya-Lin Hsieh, Chia-Ying Huang, Jian-Xue Wei, Chun-Shu Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (数字)9781665472180

ISBN: (纸本)9781665472180

In this demo, we present ExBrainable, an open-source application dedicated to modeling, evaluating and visualizing explainable CNN-based models on EEG data for brain/neuroscience research. We have implemented the functions including EEG data loading, model training, evaluation and parameter visualization. The application is also built with a model base including representative convolutional neural network architectures for users to implement without any programming. With its easy-to-use graphic user interface (GUI), it is completely available for investigators of different disciplines with limited resource and limited programming skill. Starting with preprocessed EEG data, users can quickly train the desired model, evaluate the performance, and finally visualize features learned by the model with no pain.

关键词： Brain-computer interface (BCI) electroencephalography (EEG) feature visualization convolutional neural network (CNN)

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GPU-RANC: A CUDA Accelerated Simulation Framework for Neuromorphic architectures

GPU-RANC: A CUDA Accelerated Simulation Framework for Neurom...

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Neuro Inspired Computational Elements conference (NICE)

作者： Hassan, Sahil Inouye, Michael Gonzalez, Miguel C. Aliyev, Ilkin Mack, Joshua Hafiz, Maisha Akoglu, Ali Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA

ISBN: (纸本)9798350390599;9798350390582

open-source simulation tools play a crucial role for neuromorphic application engineers and hardware architects to investigate performance bottlenecks and explore design optimizations before committing to silicon. Reconfigurable Architecture for Neuromorphic Computing (RANC) is one such tool that offers ability to execute pre-trained Spiking Neural network (SNN) models within a unified ecosystem through both software-based simulation and FPGA-based emulation. RANC has been utilized by the community with its flexible and highly parameterized design to study implementation bottlenecks, tune architectural parameters or modify neuron behavior based on application insights and study the trade space on hardware performance and network accuracy. In designing architectures for use in neuromorphic computing, there are an incredibly large number of configuration parameters such as number and precision of weights per neuron, neuron and axon counts per core, network topology, and neuron behavior. To accelerate such studies and provide users with a streamlined productive design space exploration, in this paper we introduce the GPU-based implementation of RANC. We summarize our parallelization approach and quantify the speedup gains achieved with GPU-based tick-accurate simulations across various use cases. We demonstrate up to 780 times speedup compared to serial version of the RANC simulator based on a 512 neuromorphic core MNIST inference application. We believe that the RANC ecosystem now provides a much more feasible avenue in the research of exploring different optimizations for accelerating SNNs and performing richer studies by enabling rapid convergence to optimized neuromorphic architectures.

关键词： Neuromorphic computing CUDA Graphics Processing Unit (GPU) Spiking Neural network (SNN)

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Declarative Provisioning of Virtual network Function Chains in Intent-based networks 9

Declarative Provisioning of Virtual Network Function Chains ...

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9th ieee International conference on network Softwarization (ieee NetSoft) - Boosting Future networks through Advanced Softwarization

作者： Massa, Jacopo Forti, Stefano Paganelli, Federica Dazzi, Patrizio Brogi, Antonio Univ Pisa Dept Comp Sci Pisa Italy

ISBN: (纸本)9798350399806

Intent-based networking (IBN) aims at simplifying network configuration and management by using high-level objectives that express the desired state of the network rather than the details of how to implement it. In this article, we propose a declarative methodology and an associated open-source Prolog prototype (i) to model IBN intents related to the provisioning of Virtual network Function (VNF) chains, and (ii) to process those intents to assemble and place a VNF chain that fulfils them. Our prototype is assessed over a lifelike motivating scenario.

关键词： intent-based networking declarative programming virtual network functions cloud-edge continuum

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Comparing Graph Neural networks Aggregation Strategies for Dynamic programming-Based Reasoning Tasks 4

Comparing Graph Neural Networks Aggregation Strategies for D...

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4th International Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Shen, Tongle School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9798331528676

This paper investigates the reasoning capabilities of various Graph Neural networks (GNN) on dynamic programming-based tasks, focusing on the impact of different aggregation strategies. The study evaluates five GNN architectures, Message Passing Neural networks, Graph Convolutional networks, Graph Attention networks, Graph Sample and Aggregation, and Graph Isomorphism networks—across three DP problems: Longest Increasing Subsequence (LIS), Coin Change, and 0/1 Knapsack. These tasks challenge the GNNs to simulate discrete functions and implicitly learn correct logical reasoning steps. Experiments are conducted to assess the reasoning accuracy and generalization ability of GNNs. The results demonstrate that GraphSAGE consistently outperforms other models in the LIS problem for relational understanding. However, all models struggle with the NP-Hard integer programming problem Coin Change, highlighting the need for more expressive aggregation mechanisms. This study provides theoretical insights into the effect of different GNN designs for algorithmic reasoning tasks. It suggests directions for future research, including exploring more algorithmic challenges and the influence of advanced network structure. ©2024 ieee.

关键词： Neural network models

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Progressive Quantum Key Distribution network Recovery after Massive Failures

Progressive Quantum Key Distribution Network Recovery after ...

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ieee conference on Global Communications (ieee GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Li, Mengyao Zhang, Qiaolun Gatto, Alberto Bregni, Stefano Yang, Zongshuai Tornatore, Massimo Politecn Milan Dept Elect Informat & Bioengn Milan Italy

ISBN: (纸本)9798350310900

Progressive network recovery is the problem arising when a network is subject to massive failures and the network operator has to identify the optimal sequence of repair actions to maximize carried traffic. As initial deployments of quantum-key-distribution (QKD) over optical networks start appearing in several locations worldwide, in this paper, for the first time, we model and solve the Progressive QKD network recovery (PQNR) problem in QKD networks to accelerate the QKD network recovery after massive failures. Specifically, we formulate an Integer Linear programming (ILP) model to model the achievable key rate for different QKD network architectures (w/o trusted relay and optical bypass). Due to the scalability issue of ILP, we also propose a scalable heuristic to solve the PQNR problem for large topologies. Our numerical results show that joint utilization of optical bypass and trusted node technologies leads to significant improvement in performance and that our heuristic solution has an acceptable optimality gap compared with ILP while reducing time.

关键词： Quantum key distribution network progressive recovery quantum key pool trusted relay optical bypass

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