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Integrated Sensing,Computing and communications Technologies in IoV and V2X

China communications 2023年第3期20卷 I0002-I0004页

作者： Shanzhi Chen FRichard Yu Weisong Shi Changle Li China Information and Communication Technology Group Co. Ltd.(CICT) State Key Laboratory of Wireless Mobile Communications SMIC IEEE VTS Mobile World newsletter IEEE Canadian Academy of Engineering(CAE) Engineering Institute of Canada(EIC) IET Department of Computer and Information Sciences at the University of Delaware(UD) State Key Laboratory of Integrated Services Networks of Xidian University

With the development of new generation of information and communication technology,the Internet of Vehicles(IoV)/Vehicle-to-Everything(V2X),which realizes the connection between vehicle and X(i.e.,vehicles,pedestrians,infrastructures,clouds,etc.),is playing an increasingly important role in improving traffic operation efficiency and driving safety as well as enhancing the intelligence level of social traffic services.

关键词： driving traffic operation

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3D Cloud-RAN Functional Split to Provide 6G Connectivity on Mars

3D Cloud-RAN Functional Split to Provide 6G Connectivity on ...

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2022 IEEE Aerospace Conference, AERO 2022

作者： Bonafini, Stefano Sacchi, Claudio Granelli, Fabrizio Bassoli, Riccardo Fitzek, Frank H.P. Kondepu, Koteswararao Trento38123 Italy Technische Universität Dresden Deutsche Telekom Chair of Communication Networks Faculty of Electrical and Computer Engineering Dresden01062 Germany Indian Institute of Technology Dharward Department of Computer Science and Engineering Karnataka 580011 India

ISBN: (数字)9781665437608

ISBN: (纸本)9781665437608

The Mars Helicopter Scout left a milestone in the history of the mankind by successfully completing the first flight on Mars. This achievement opens up to the possibility of having many unmanned aerial vehicles (UAVs) acquiring data from the Martian surface along with firmly anchored machines on ground, such as moving rovers or static landers. Moreover, it is no surprise that space agencies are also paving the way for the first human landing on the Red planet. In this context, it becomes needed to support future missions by providing connectivity to the whole Martian surface in order to allow in-situ wide band data exchange between nodes-user equipments (UEs)-composing the overall network. However, it seems tough to move common mobile terrestrial infrastructures on Mars and install them on-ground or implement them on limited-resource machines, as well as to guarantee everywhere and anytime on-demand connectivity. Thus, this paper will investigate the alternative solution of developing an autonomous Martian space ecosystem, through which globally inter-connect the UEs. UAVs and, eventually, high altitude pseudo satellites (HAPS), will act as radio unit (RU) and, partially, as distributed unit (DU) of a cloud radio access network (CRAN), while small satellites platforms, such as CubeSats (CS), placed in orbit will embark the majority of the computational load by performing the remaining DU and centralized unit (CU) functions. The proposed 3D network configuration, which looks towards a 'Beyond 5G' infrastructure, or even 6G, will start by supposing very low Mars orbit (VLMO) in which CubeSats will be deployed. The design will then proceed through the installation process of CRAN on drones and CubeSats, while respecting strict latency and bandwidth requirements imposed by the Common Public Radio Interface standard (CPRI) at the fronthaul (FH) to allow low PHY-layer splitting options. A link-budget evaluation will then be proposed for the drone-to-Cubesat link. End-to-E

关键词： Drones

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Catalyzing Random Access at Physical Layer for Internet of Things:An Intelligence Enabled User Signature Code Acquisition Approach

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China communications 2021年第10期18卷 181-192页

作者： Xiaojie Fang Xinyu Yin Xuejun Sha Jinghui Qiu Hongli Zhang School of Electronic and Information Engineering Harbin Institute of TechnologyHarbin 1500001China Science and Technology on Communication Networks Laboratory Shijiazhuang 050050China School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

Exploiting random access for the underlying connectivity provisioning has great potential to incorporate massive machine-type communication(MTC)devices in an Internet of Things(Io T)***,massive access attempts from versatile MTC devices may bring congestion to the IIo T network,thereby hindering service increasing of IIo T *** this paper,an intelligence enabled physical(PHY-)layer user signature code acquisition(USCA)algorithm is proposed to overcome the random access congestion problem with reduced signaling and control *** the proposed scheme,the detector aims at approximating the optimal observation on both active user detection and user data reception by iteratively learning and predicting the convergence of the user signature codes that are in *** crossentropy based low-complexity iterative updating rule is present to guarantee that the proposed USCA algorithm is computational feasible.A closed-form bit error rate(BER)performance analysis is carried out to show the efficiency of the proposed intelligence USCA *** results confirm that the proposed USCA algorithm provides an inherent tradeoff between performance and complexity and allows the detector achieves an approximate optimal performance with a reasonable computational complexity.

关键词： Internet of Things(IoT) artificial intelligence physical layer cross-entropy random access

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Edge Computing in Micro Data Centers for Firefighting in Residential Areas of Future Smart Cities

Edge Computing in Micro Data Centers for Firefighting in Res...

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2022 International Conference on Electrical, computer, communications and Mechatronics engineering, ICECCME 2022

作者： Gudepu, Venkateswarlu Pappu, Bhavani Javvadi, Tejasri Bassoli, Riccardo Fitzek, Frank H.P. Valcarenghi, Luca Devi, D.V.N. Kondepu, Koteswararao IIT Dharwad Department of Computer Science and Engineering Dharwad India RGUKT-Srikakulam Department of Computer Science and Engineering Srikakulam India RGUKT-Srikakulam Department of Electronics Communication Engineering Srikakulam India Technische Universitat Dresden Deutsche Telekom of Communication Networks Dresden Germany Telecommunications. Computer Engineering and Photonics Institute Scuola Superiore sant'Anna Pisa Italy RGUKT Nuzvid Department of Computer Science and Engineering Nuzvid India

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

5G standardization is going to reach its end, so research on 6G has started, driven by the scientific and industrial communities. 5G and especially 6G, are going to provide resources to enhance every aspect of human life via communication networks and computing. Among the different verticals, emergency services are one of the most important parts of making smart cities of the future a reality. In this context, firefighting is highly important for security and safety. However, firefighting requires ultra-reliable and low-latency communications since firefighters can be provided with advanced guidance during fire control with the employment of distributed sensors, robots, etc. Also, the use of machine learning algorithms is important for firefighters to analyze and make decisions based on the audiovisual and possibly tactile information they collect. Such a scenario cannot leverage the current cloud computing paradigm, which has significant latency issues. That is why it is important to study and design edge computing paradigms to address the goals of these scenarios and use cases since they can be capable of fulfilling latency and reliability requirements. In this situation, this work looks into how computing at Edge Micro Data Centers (EMDC) can be used to improve how fires are predicted and managed. We propose a novel three-stage architecture. The initial stage focuses on prediction and classification of the fire occurrence based on available sensor data at the EMDC, whereas the second stage deals with the fire occurrence confirmation using a convolutional neural network (CNN) classification model. After the fire occurrence has been confirmed, the final stage notifies the tenants and streams 360-degree monitoring video to the nearby fire station after processing at EMDC. The results showed that the proposed architecture can realize firefighting services with low latency. to the best of the authors' knowledge, this is the first work studying and experimentally evalu

关键词： Network architecture

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Tactile Network Resource Allocation Enabled by Quantum Annealing Based on ILP Modeling 4

Tactile Network Resource Allocation Enabled by Quantum Annea...

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Witt, Arthur Körber, Christopher Kirstädter, Andreas Luu, Thomas Institute of Communication Networks and Computer Engineering University of Stuttgart Stuttgart Germany Ruhr-University Bochum Institute of Theoretical Physics Ii Bochum Germany Fraunhofer Research Institution for Energy Infrastructures and Geothermal Systems Ieg Bochum Germany Jara Hpc Forschungszentrum Jülich Jülich Germany

ISBN: (纸本)9798350343236

Agile networks with fast adaptation and reconfiguration capabilities are required for on-demand provisioning of various network services. We propose a new methodical framework for short-time network optimization based on quantum computing (QC) and integer linear program (ILP) models, which has the potential of realizing a real-time network automation. We define methods to map a nearly real-world ILP model for resource provisioning to a quadratic unconstrained binary optimization (QUBO) problem, which is solvable on quantum annealer (QA). We concentrate on the three-node network to evaluate our approach and its obtainable quality of solution using the state-of-the-art quantum annealer D-Wave Advantage™ 5.2/5.3. By studying the annealing process, we find annealing configuration parameters that obtain feasible solutions close to the reference solution generated by the classical ILP-solver CPLEX. Further, we studied the scaling of the network problem and provide estimations on quantum annealer's hardware requirements to enable a proper QUBO problem embedding of larger networks. We achieved the QUBO embedding of networks with up to 6 nodes on the D-Wave Advantage™. According to our estimates a real-sized network with 12 to 16 nodes require a QA hardware with at least 50000 qubits or more. © 2023 IEEE.

关键词： Resource allocation

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Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 15513-15526页

作者： Liu, Lei Feng, Jie Mu, Xuanyu Pei, Qingqi Lan, Dapeng Xiao, Ming Xidian University Guangzhou Institute of Technology Guangzhou510555 China Ministry of Education Shanghai201804 China Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunication Engineering Shaanxi Xi'an710071 China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi'an710121 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China University of Oslo Department of Informatics Oslo0373 Norway Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm10044 Sweden

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading. © 2000-2011 IEEE.

关键词： Reinforcement learning

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Performance Evaluation of High Gain 2×2 MIMO Microstrip Antenna Operating at 28 GHz 2

Performance Evaluation of High Gain 2×2 MIMO Microstrip Ant...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Ismail, Alyani Binti Rasid, M.F.A. Hamidon, Mohd Nizar B Abdullah, Raja Syamsul. A. Raja Shebani, Nafaa Mohamad Universiti Putra Malaysia Wireless and Photonics Networks Research Centre Department of Computer and Communication Systems Engineering Faculty of Engineering Serdang Malaysia Universiti Putra Malaysia Institute of Nanoscience and Nanotechnology Department of Electrical and Electronic Engineering Faculty of Engineering Serdang Malaysia

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

In this paper, four configurations of 2×2 MIMO microstrip antenna operating at 28 GHz are proposed and simulated by computer Simulation Technology (CST) software. The antenna is designed on PTFE/Teflon substrate with dielectric constant of 2.1, loss tangent of 0.0002, and thickness of 1.58 mm. The performance of all proposed configurations of 2×2 MIMO antennas are evaluated based on the overall gain, envelope correlation coefficient (ECC), and diversity gain (DG). Moreover, the comparison between all cases in this work is done in order to choose the optimal configuration, and to prove the reliability of the design the best obtained case is compared with proposed antennas in published works. © 2022 IEEE.

关键词： Microstrip antennas

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