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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Kim, Hyunsoo Roh, Taeyup Shim, Byonghyo Seoul National University Institute of New Media and Communications Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798331517786

The ever-increasing data rate demand of 5G and 6G communication systems can be met by employing key technologies such as upper mid-band and millimeter (mmWave)-band communications. In upper mid and mmWave communications, beamforming techniques realized by multiple-input multiple-output (MIMO) systems are used to compensate for severe attenuation due to high diffraction and penetration loss. Unfortunately, the widely used codebook-based beam management techniques require complicated handshaking operations to determine the beam direction which incurs considerable processing latency and energy consumption. To address these issues, we propose the multi-modal beam management (MMBM) technique. The proposed MMBM exploits multi-modal sensing (i.e., LiDAR and RGB camera) along with computer vision (CV) techniques to identify the 3D location of mobile devices. With the 3D position, the base station (BS) can accurately steer directional beams toward the mobile's position. Since the beam direction is determined without feedback delay or codebook quantization, MMBM achieves performance improvements in terms of data rate and latency. From the simulation results, we show that MMBM improves the sum rate by more than 15% compared to the existing codebook-based beamforming scheme in 5G NR. © 2024 IEEE.

关键词： 5G mobile communication systems

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Optimized Deep Networks for Indoor Position and Localization Based on IEEE P802.11az

Optimized Deep Networks for Indoor Position and Localization...

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2024 International Conference on Advanced Technologies for communications, ATC 2024

作者： Tung, Nguyen Nhat Huynh-The, Thien Tran, Quynh-Nhu Thi Ho Chi Minh City University of Technology and Education Department of Computer and Communications Engineering Viet Nam

ISBN: (纸本)9798350353983

With the exponential growth of mobile devices and wireless networks, the IEEE 802.11az standard has emerged as a leading technology, offering distinct advantages in localization and positioning tasks compared to other wireless solutions. This standard boasts superior accuracy, enhanced scalability, robust security features, and reduced energy consumption. This research investigates localization and positioning through experimentation with simulated datasets based on the IEEE 802.11az standard. We propose a deep convolutional neural network (CNN) architecture for positioning and localization tasks. Specifically, we simulate a simplified three-dimensional (3D) map and generate a synthetic dataset of channel impulse response (CIR) fingerprints by leveraging the IEEE P802.11az standard. Our optimized network architecture then predicts the location of a station (STA) based on a subset of the fingerprints, enabling the precise prediction of the exact position within this virtual indoor environment. Simulation results demonstrate that our proposed architecture outperforms the baseline model, achieving up to 93.04% accuracy and 0.92 root mean square error (RMSE), compared with 86.68% accuracy and 1.63 RMSE of the baseline. © 2024 IEEE.

关键词： Mean square error

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Sensing-Based Channel Estimation in 6G mmWave Ultra-Dense Networks 15

Sensing-Based Channel Estimation in 6G mmWave Ultra-Dense Ne...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Moon, Jihoon Shim, Byonghyo Institute of New Media and Communications Seoul National University Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798350364637

As a key enabler for 6th generation (6G) communications, millimeter-wave (mmWave) ultra-dense network (UDN) has been studied. To utilize beamforming technique in mmWave UDN, accurate channel information is needed. By exploiting the small coverage of a BS in UDN, we can obtain accurate channel information via sensing-based channel estimation. Using sensing images and deep learning technique, the proposed sensing-based channel estimation technique can accurately extract the positions of mobile devices as well as the positions and orientations of wireless entities influencing signal propagation. From simulations, we observe that the proposed scheme achieves more than 52% channel estimation accuracy gain over conventional schemes. © 2024 IEEE.

关键词： Channel estimation

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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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computer Systems Science & engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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An Improved Deep Structure for Accurately Brain Tumor Recognition

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computer Systems Science & engineering 2023年第8期46卷 1597-1616页

作者： Mohamed Maher Ata Reem N.Yousef Faten Khalid Karim Doaa Sami Khafaga Department of Communications and Electronics Engineering MISR Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Electronics and Communications Delta Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain *** using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiologists would be a noninvasive *** paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning *** proposed model extracts Gray Level Co-occurrence Matrix(GLCM)textural features from MRI brain tumor ***,a deep neural network(DNN)model has been proposed to select the most salient features from the ***,it manipulates the extraction of the additional high levels of salient features from a proposed CNN ***,a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition *** common datasets have been applied and tested,Br35H and FigShare *** first dataset contains binary labels,while the second one splits the brain tumor into four classes;glioma,meningioma,pituitary,and no ***,several performance metrics have been evaluated from both datasets,including,accuracy,sensitivity,specificity,F-score,and training *** results show that the proposed methodology has achieved superior performance compared with the current state of art *** proposed system has achieved about 98.22%accuracy value in the case of the Br35H dataset however,an accuracy of 98.01%has been achieved in the case of the FigShare dataset.

关键词： Brain tumor convolutional neural network gray level co-occurrence matrix noninvasive FigShare dataset Br35H dataset

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Ridge Waveguide Technology and Ridge Gap Waveguide Technology: A Comparison 11

Ridge Waveguide Technology and Ridge Gap Waveguide Technolog...

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11th International Symposium on Telecommunication, IST 2024

作者： Karimi-Nobandegani, Abbas Mahdavi, Parisa Bahri, Reza Hosseini, S. Esmail Shiraz University School of Electrical and Computer Engineering Department of Communications and Electronics Shiraz Iran Tehran Iran

ISBN: (纸本)9798350356250

Ridge Gap waveguide (RGW) has been a famous metallic transmission line in microwave and antenna technology applications since 2009. This structure has many advantages, that have outweighed its disadvantages, especially in high-frequency applications. However, the similarity of this technology to the ridge waveguide (RW) raised the question about the effectiveness of this waveguide in comparison to the RW. In this paper, the insertion loss of RGW and RW is compared. This comparison shows in terms of loss in Ku band there is no evident difference between them. However, RGW shows slightly better power handling capability compared to RW. Gysel power divider (GPD) has been designed, simulated, and measured in Ku band with both structures to have a proper comparison. Both power dividers covered the Ku-band, and even the lower side of K band was partially covered. The measured result of both structures showed good agreement with the simulation, which means both of them have low sensitivity to manufacturing error in the intended bandwidth;Another aspect of this research is that the manufacturing cost of RW is noticeably lower than RGW, which should be considered when one of these technologies is being considered for large-scale production. © 2024 IEEE.

关键词： Ridge waveguides

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Task-oriented V2X communications using Large Multi-modality Model 100

Task-oriented V2X Communications using Large Multi-modality ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Kim, Hyunsoo Jeong, Seokhyun Shim, Byonghyo Seoul National University Institute of New Media and Communications Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798331517786

In the 5G NR and 6G, vehicle-to-everything (V2X) communication has emerged as a crucial technology. In this paper, we present a novel task-oriented communications (ToC) approach that employs large multi-modal models (LMMs) for V2X tasks such as traffic management. The key idea of the proposed multi-modal task-oriented communications (MMToC) is to extract essential information relevant to vehicular tasks from the various sensing data, integrate the information, and infer the output of vehicular tasks using LMMs. Unlike traditional communication methods focusing on bit-level metrics such as bits per second and error rate, MMToC enhances task efficiency and performance by optimizing information transmission methods for specific vehicular tasks. MMToC exchanges only the essential feature vectors needed for vehicular tasks, thereby reducing communication overhead and improving task performance. Numerical results demonstrate that the proposed MMToC technique significantly increases average vehicle speed by more than 40% compared to conventional methods. © 2024 IEEE.

关键词： 5G mobile communication systems

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Identification and Acknowledgment of Programmed Traffic Sign Utilizing Profound Convolutional Neural Organization

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Intelligent Automation & Soft Computing 2023年第2期35卷 1527-1543页

作者： P.Vigneshwaran N.Prasath M.Islabudeen A.Arunand A.K.Sampath Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering School of EngineeringPresidency UniversityBengaluru560064India

Trafﬁc signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and indivi-duals by walking,*** a segment of the clever transportation structure,the acknowledgment of trafﬁc signs is basic for the driving assistance system,trafﬁc sign upkeep,self-administering driving,and various *** are different assessments turns out achieved for trafﬁc sign acknowledgment in the ***,most of the works are only for explicit arrangements of trafﬁc signs,for example,beyond what many would consider a possible ***ﬁc sign recognizable proof is generally seen as trying on account of various complexities,for example,extended establishments of trafﬁc sign *** critical issues exist during the time spent identiﬁcation(ID)and afﬁrmation of trafﬁc *** signs are occasionally blocked not entirely by various vehicles and various articles are accessible in busy time gridlock scenes which make the signed acknowledgment hard and walkers,various vehicles,constructions,and loads up may frustrate the ID structure by plans like that of road *** concealing information from trafﬁc scene pictures is affected by moving light achieved by environment conditions,time(day-night),and ***ﬁc sign revelation and afﬁrmation structure has two guideline sorts out:The essential stage incorpo-rates the trafﬁc sign limitation and the resulting stage portrays the perceived trafﬁc signs into a particular class.

关键词： Trafﬁc sign classiﬁer convolution neural network image vehicle

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Sparse Array Sensor Selection in ISAC with Identifiability Guarantees 58

Sparse Array Sensor Selection in ISAC with Identifiability G...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Rajamäki, Robin Pal, Piya Aalto University Department of Information and Communications Engineering Finland University of California Department of Electrical and Computer Engineering San Diego United States

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354058

This paper investigates array geometry and waveform design for integrated sensing and communications (ISAC) employing sensor selection. We consider ISAC via index modulation, where various subsets of transmit (Tx) sensors are used for both communications and monostatic active sensing. The set of Tx subarrays make up a codebook, whose cardinality we maximize (for communications) subject to guaranteeing a desired target identifiability (for sensing). To characterize the size of this novel optimal codebook, we derive first upper and lower bounds, which are tight in case of the canonical uniform linear array (ULA) and any nonredundant array. We show that the ULA achieves a large codebook-comparable to the size of the conventional unconstrained case-as satisfying the identifiability constraint only requires including two specific sensors in each Tx subarray (codeword). In contrast, nonredundant arrays, which have the largest identifiability for a given number of physical sensors, only have a single admissible codeword, rendering them ineffectual for communications via sensor selection alone. The results serve as a step towards an analytical understanding of the limits of sensor selection in ISAC and the fundamental trade-offs therein. © 2024 IEEE.

关键词： Redundancy

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Advancing Underwater Communication with NBO-MFO Hybrid Routing in UWSNs 10

Advancing Underwater Communication with NBO-MFO Hybrid Routi...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kalaiselvan, S.A. Kumar, M.Lenin Amanullah, M. Mani Chigurupati, T.R. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vignan Institute of Technology and Science Department of Electronics and Communications Engineering Telangana India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350353068

In this research provides a solution for optimizing routing path selection in Underwater Wireless Sensor Networks (UWSNs). The algorithm combines Namib Beetle Optimization (NBO) and Moth-Flame Optimization (MFO), integrating them into the ILEACH methodology for efficient routing path selection. The algorithm operates in an iterative manner, commencing with NBO's feature selection process, evaluating fitness through average classification error and feature selection ratio. Simultaneously, MFO mimics moth behavior, updating positions via a logarithmic spiral function based on flame locations. The mutual learning phase facilitates information exchange between NBO and MFO, enhancing their collective intelligence and refining the routing path selection process. ILEACH's contribution lies in its fitness aggregation and selection methods. It amalgamates various criteria, including energy levels, distances, and node conditions, into a comprehensive fitness metric. Subsequently, ILEACH selectively identifies optimal routing paths aligned with energy-efficient criteria and node statuses, ensuring efficient data transmission and energy conservation within UWSNs. This integrated approach fosters a comprehensive strategy for routing path selection in UWSNs, ensuring sustainability and efficiency in data transmission within challenging underwater environments. © 2024 IEEE.

关键词： Iterative methods

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