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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Raghavendra Kulkarni Venkata Satya Suresh kumar Kondeti Binu Sudhakaran Pillai Surendran R Department of Network Technology Principal Engineer T-Mobile USA Inc Bellevue W A USA Department of Professional Services Axyom. Core Senior Systems Engineer North Andover MA USA Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This research introduces a novel deep learning for channel estimation for the 6G networks. The channel estimation for the 6G networks is employed using the proposed Improved Convolutional Recurrent (ImConv-RNN) model. In this, the loss function optimization is employed using the proposed ChSO algorithm. The chaotic mapping based on Chebyshev is included in the skill optimization assist the algorithm to enhance the convergence rate. The proposed ImConv-RNN model is evaluated based on RMSE, R2, MAE and MAPE acquired the values of 0.0136, 0.9584, 0.0114, and 5.8969 respectively.

关键词： 6G mobile communication Deep learning Analytical models Sentiment analysis Channel estimation Estimation Chebyshev approximation Feature extraction Optimization Convergence

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Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications

Map2Schedule: An End-to-End Link Scheduling Method for Urban...

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IEEE International Conference on Communications (ICC)

作者： Lihao Zhang Haijian Sun Jin Sun Ramviyas Parasuraman Yinghui Ye Rose Qingyang Hu School of Electrical and Computer Engineering University of Georgia Athens GA USA School of Computing University of Georgia Athens GA USA Shanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications China Electrical and Computer Engineering Department at Utah State University Logan UT USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency.

关键词： Wireless communication Accuracy Processor scheduling Computational modeling System performance Urban areas Vehicular ad hoc networks

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User Cooperation with Power Control for Federated Learning in CFmMIMO networks

User Cooperation with Power Control for Federated Learning i...

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IEEE International Conference on Communications Workshops, ICC

作者： Han Bao Ke Xiong Ruichen Zhang Pingyi Fan Dusit Niyato Khaled Ben Letaief Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

In synchronous federated learning (SFL), only if all users successfully deliver their local parameters to the central server, the central server can efficiently aggregate a global model. In this case, the user with the worst quality link becomes the bottleneck limiting the training performance of SFL. To cope with this issue for SFL in cell-free massive multiple-input multiple-output (CFmMIMO) network, this paper proposes a joint user cooperation and power control (JUCPC) method. Particularly, the users with good quality links are employed to assist the ones with worse quality links to transfer their parameters to the central server. Then, the power control is employed to coordinate the interference among users to further improve the transmission performance for SFL. To find the joint optimal user cooperation pairing and the power control coefficients, we formulate an optimization problem to minimize the maximum transmission time among users. As the problem is non-convex, we decouple it into two sub-problems. For the first sub-problem, we propose a fairness-based algorithm to select the helping users and also pair them with the ones who required to be assisted. For the second sub-problem, a deep deterministic policy gradient (DDPG)-based algorithm is proposed to find the optimized power control coefficients. Experiments show that the proposed JUCPC method can efficiently reduce the transmission time of the worst user for SFL. Moreover, compared to the traditional optimization method based on convex optimization solver (CVX), our proposed DDPG-based one requires much shorter execution time while achieves approximating result to the CVX-based algorithm.

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Precision control model for chaotic laser generation in optical communication and laser measurement systems

Precision control model for chaotic laser generation in opti...

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Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2024

作者： Kurskoy, Yuriy Hnatenko, Oleksandr Sherstyuk, Daniil Zarytskyi, Valeriy Chernovolyk, Galyna Nussupbekova, Gulzhan Pogrebniak, Liudmyla Kisala, Piotr Klimek, Jacek Muslimov, Kuanysh Kharkiv National University of Radio Electronics 14 Nauku Ave. Kharkiv Ukraine Vinnytsia National Technical University Software Development Department Ukraine Turan University Computer and Software Engineering Department Almaty Kazakhstan Kruty Heroes Military Institute of Telecommunications and Information Technologies Telecommunication System and Network Department Kyiv Ukraine Lublin University of Technology Poland K.I. Satbayev Kazakh National Research Technical University Kazakhstan

ISBN: (数字)9781510685796

ISBN: (纸本)9781510685789

The article is dedicated to advancing technologies in chaotic optical communication and investigating chaotic laser generation in measurement systems and instruments. It conducts an analysis of the informational potential of chaotic signals in the optical range and explores the feasibility of implementing chaotic dynamics in lasers. It is demonstrated that among the current challenges are precision and stable generation of chaotic laser modes, as well as precision and stable synchronization of chaotic laser generators. The definition of chaotic modes is proposed as a set of characteristic types of chaotic oscillations that are stable, distinguishable, and reproducible within an oscillatory system, along with a method for estimating the number of modes and their designation. A method for modulating pump energy using a Chua generator is suggested. Additionally, a model for precision control of chaotic laser generation is proposed, allowing for the generation of chaotic laser modes with the ability to measure and control laser emission parameters and chaotic dynamics based on monitoring small changes in control parameters and influencing factors. © 2024 SPIE.

关键词： Optical communication

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Evaluation Quality Service of a Digital Broadcasting Station with Automatic Equipment Backup

Evaluation Quality Service of a Digital Broadcasting Station...

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2021 Ural Symposium on Biomedical engineering, Radioelectronics and Information Technology, USBEREIT 2021

作者： Tissen, Olga V. Sai, Sergey V. Russian Television and Radio Broadcasting Network Khabarovsk TV and Radio Broadcasting Department Khabarovsk Russia Pacific National University Department of Computer Engineering Khabarovsk Russia

ISBN: (纸本)9781728176918

The main task of DVB-T2 broadcasting is provided subscribers with high quality services. Quality of Service (QoS) indicator characteristics for terrestrial digital television broadcasting when the signal is distributed over the air are considered in the paper. An experiment was carried out to organize the required reservation of broadcasting equipment. DVB-T2 transmitter 'Polaris TVC', local content inserter TTV-PLPSW-0401 and universal analyzer Enensys Referee were use in experiment. The T2-MI inserter output stream file has been written when the device reserves a local stream to a national stream. There was also a record of events when the transmitter reserves the main stream as a backup. The timing diagrams are presented based on the types of errors that occurred when the inserter and transmitter were reserving input streams. The numerical values of the SAE, SDE and SIE parameters are determined for the case of the reservation of the local T2-MI stream by the tie-in device (ETSI TS 102773) and the DVB-T2 transmitter of the main stream-the reserve one. The results were evaluated by perceived quality of service (pQoS), as well as by calculating the boundaries of confidence intervals. © 2021 IEEE.

关键词： Quality of service

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MEAT: MEDIAN-ENSEMBLE ADVERSARIAL TRAINING FOR IMPROVING ROBUSTNESS AND GENERALIZATION

arXiv

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arXiv 2024年

作者： Hu, Zhaozhe Yin, Jia-Li Chen, Bin Lin, Luojun Chen, Bo-Hao Liu, Ximeng Fujian Province Key Laboratory of Information Security and Network Systems Fuzhou350108 China College of Computer Science and Big Data Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering Yuan Ze University Taiwan

Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA). However, previous research has shown that self-ensemble defense methods in adversarial training (AT) still suffer from robust overfitting, which severely affects the generalization performance. Empirically, in the late phases of training, the AT becomes more overfitting to the extent that the individuals for weight averaging also suffer from overfitting and produce anomalous weight values, which causes the self-ensemble model to continue to undergo robust overfitting due to the failure in removing the weight anomalies. To solve this problem, we aim to tackle the influence of outliers in the weight space in this work and propose an easy-to-operate and effective Median-Ensemble Adversarial Training (MEAT) method to solve the robust overfitting phenomenon existing in self-ensemble defense from the source by searching for the median of the historical model weights. Experimental results show that MEAT achieves the best robustness against the powerful AutoAttack and can effectively allievate the robust overfitting. We further demonstrate that most defense methods can improve robust generalization and robustness by combining with MEAT. Copyright © 2024, The Authors. All rights reserved.

关键词： network security

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Shaping Advice in Deep Reinforcement Learning

arXiv

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arXiv 2022年

作者： Xiao, Baicen Ramasubramanian, Bhaskar Poovendran, Radha Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Electrical and Computer Engineering Department of Engineering and Design Western Washington University BellinghamWA98225 United States

Reinforcement learning involves agents interacting with an environment to complete tasks. When rewards provided by the environment are sparse, agents may not receive immediate feedback on the quality of actions that they take, thereby affecting learning of policies. In this paper, we propose methods to augment the reward signal from the environment with an additional reward termed shaping advice in both single- and multi-agent reinforcement learning. The shaping advice is specified as a difference of potential functions at consecutive time-steps. Each potential function is a function of observations and actions of the agents. The use of potential functions is underpinned by an insight that the total potential when starting from any state and returning to the same state is always equal to zero. We show through theoretical analyses and experimental validation that the shaping advice does not distract agents from completing tasks specified by the environment reward. Theoretically, we prove that the convergence of policy gradients and value functions when using shaping advice implies the convergence of these quantities in the absence of shaping advice. We design two algorithms-Shaping Advice in Single-agent reinforcement learning (SAS) and Shaping Advice in Multi-agent reinforcement learning (SAM). Shaping advice in SAS and SAM needs to be specified only once at the start of training, and can easily be provided by non-experts. Experimentally, we evaluate SAS and SAM on two tasks in single-agent environments and three tasks in multi-agent environments that have sparse rewards. We observe that using shaping advice results in agents learning policies to complete tasks faster, and obtain higher rewards than algorithms that do not use shaping advice. Code for our experiments is available at https://***/baicenxiao/Shaping-Advice. © 2022, CC BY.

关键词： Reinforcement learning

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UAV-RIS-Aided Energy-Efficient and QoS-Aware Emergency Communications Based on DRL

UAV-RIS-Aided Energy-Efficient and QoS-Aware Emergency Commu...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jiawei Hu Ying Ju Haoyu Wang Lei Liu Qingqi Pei Y. G. Shee Xiaojie Zhu Celimuge Wu Guangzhou Institute of Technology Xidian University Guangzhou China Department of Electrical Engineering and Computer Science University of California Irvine CA USA Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts Telecommunications Xi’an China Department of Electrical and Electronic Engineering Universiti Tunku Abdul Rahman Kajang Malaysia Department of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Ensuring reliable communication can be incredibly challenging in emergencies due to the breakdown of conventional infrastructure. However, a promising solution is on the horizon: the integration of reconfigurable intelligent surfaces (RIS) onto unmanned aerial vehicles (UAV), known as UAV-RIS. This innovative approach holds the potential to offer agile and adaptable communication services during crises, overcoming the limitations of traditional systems. This paper establishes an innovative UAV-RIS system with an active RIS to enhance the uplink communication between ground devices (GDs) and the air base station (ABS). We present an advanced communication strategy utilizing deep reinforcement learning (DRL) for UAV-RIS-supported uplink communication in dynamic emergencies. This scheme is designed to optimize the energy efficiency of the UAV-RIS communication system while adhering to quality of service (QoS) constraints for all GDs. It achieves this by jointly optimizing the trajectory of the UAV-RIS and the phase of the active RIS, ensuring efficient and reliable communication in challenging environments. To optimize the performance of the system, we propose a hierarchical Proximal Policy Optimization (H-PPO) algorithm and the upper and lower layers of H-PPO optimize the trajectory and phase control, respectively. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the performance of dynamic emergency communication networks.

关键词： Vehicular and wireless technologies Simulation Quality of service Phase control Reconfigurable intelligent surfaces Autonomous aerial vehicles Energy efficiency Trajectory Uplink Vehicle dynamics

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Multi-class classification based on relative distribution of class 2

Multi-class classification based on relative distribution of...

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2nd International Conference on computer and Information Sciences, ICCIS 2020

作者： Shim, Seong-O College of Computer Science and Engineering University of Jeddah Department of Computer and Network Engineering Jeddah Saudi Arabia

ISBN: (纸本)9781728154671

Binarization of multi-class classification problem into two class problem is widely adopted in machine learning because of its simplicity and efficiency. It consists of dividing multiple classes into pairs of all possible combinations and learning the base classifiers on each pair of classes. Then, their outputs are combined to classify an instance. To improve the classification accuracy, several different combination schemes were studied previously. We proposed a new combination scheme based on relative distribution of each class. Instead of merely computing the distances of an instance to the nearest neighbors of each class, relative distances were measured considering the relative distribution of each class. Experimental results showed the proposed method outperforms previous methods both in terms of accuracy and kappa measures. © 2020 IEEE.

关键词： Classifiers

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SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System

SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and networks (SECON)

作者： Yinnan Zhou Hao Zhou Luowei Li Siming Chen Zhi Liu Xiang-Yang Li LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

WiFi-based technology is appealing for indoor localization due to the widely deployed infrastructures. Recently, path separation solutions have been proposed to address the multipath effects and achieve decimeter-level localization accuracy in line-of-sight (LoS) scenarios. However, these solutions experience serious performance degradation in non-line-of-sight (NLoS) scenarios, and couldn't be used for mobile device tracking where continuous LoS/NLoS switching happens. In this paper, we propose SaTrack, a LoS/NLoS state-aware mobile device tracking system. SaTrack identifies LoS/NLoS states based on the diversity of the strongest estimated paths when using different reference antennas. With the observation of spatial aggregation and temporal continuity for the Tx-Rx direct path, SaTrack chooses the direct path through two-step clustering, i.e., clustering in the spatial domain to form candidates and clustering again in the temporal domain to select the winner. Extensive experiments are conducted to evaluate the effectiveness of SaTrack. In a typical indoor environment with abundant multipath, SaTrack achieves 0.64m and 1.27m for the median and 90th percentile tracking errors, outperforming the state-of-the-art (SOTA) solutions.

关键词： Location awareness Performance evaluation Accuracy Line-of-sight propagation Switches Mobile handsets Sensors Indoor environment Wireless fidelity Antennas

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