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Enhancing the Throughput of Device-to-Device Communication in Cellular systems Using Non-Orthogonal Transmission

IEEE Access 2024年 12卷 112290-112307页

作者： Chu-Tung Liu Hsuan-Jung Su Hsien-Bo Tseng Jhe-Yi Lin Wei-Hsiang Wang Alan Shenghan Tsai Yasuhiro Takano Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan MediaTek Inc. Hsinchu Taiwan Realtek Semiconductor Corp. Hsinchu Taiwan Department of Electrical and Computer Engineering University of Maryland College Park MD USA *** Inc. Sunnyvale CA USA Department of Information Systems Engineering Chitose Institute of Science and Technology Hokkaido Chitose Japan

Device-to-Device (D2D) communications facilitate direct data exchange between two devices, bypassing the necessity of routing through a central base station. This proximity-based communication offers advantages such as high data rates, low latency, reduced power consumption, and efficient frequency reuse. In the cellular spectrum, D2D operations encompass overlay D2D and underlay D2D. Overlay D2D allocates orthogonal radio resources to D2D and cellular users, resulting in inefficient frequency reuse. On the other hand, underlay D2D shares the same radio resources among D2D and cellular users, necessitating centralized coordination and extensive exchanges of channel and traffic information for effective interference management. To address the limitations of overlay and underlay D2D, we introduce a scheme that allows D2D users to simultaneously and non-orthogonally transmit both D2D and cellular signals. This approach resembles underlay D2D in terms of high frequency reuse but eliminates the need for centralized interference management. In this paper, we investigate various system design aspects based on this scheme, including power allocation, multiple-antenna extension, scheduling constraints, and different interference cancellation techniques. Incorporating a coordinated scheduling method, the proposed scheme enhances the sum rate of a conventional multiple-antenna overlay D2D system by 2.14 times, with only a negligible impact on the cellular communication rate. Furthermore, the proposed scheme improves the sum rate by up to 87% within conventional underlay D2D systems, all without the need for additional centralized coordination.

关键词： Device-to-device communication Resource management Interference cancellation Cellular networks Spectral efficiency Precoding Throughput MIMO communication

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Logic Locking based Trojans: A Friend Turns Foe

arXiv

引用

arXiv 2023年

作者： Liu, Yuntao Jayasena, Aruna Mishra, Prabhat Srivastava, Ankur Institute of Systems Research University of Maryland College Park United States Department of Electrical and Computer Engineering University of Florida United States

Logic locking and hardware Trojans are two fields in hardware security that have been mostly developed independently from each other. In this paper, we identify the relationship between these two fields. We find that a common structure that exists in many logic locking techniques has desirable properties of hardware Trojans (HWT). We then construct a novel type of HWT, called Trojans based on Logic Locking (TroLL), in a way that can evade state-of-the-art ATPG-based HWT detection techniques. In an effort to detect TroLL, we propose customization of existing state-of-the-art ATPG-based HWT detection approaches as well as adapting the SAT-based attacks on logic locking to HWT detection. In our experiments, we use random sampling as reference. It is shown that the customized ATPG-based approaches are the best performing but only offer limited improvement over random sampling. Moreover, their efficacy also diminishes as TroLL’s triggers become longer (i. e. have more bits specified). We thereby highlight the need to find a scalable HWT detection approach for TroLL. © 2023, CC BY.

关键词： Hardware security

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Modulation and Estimation with a Helper

arXiv

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arXiv 2023年

作者： Khina, Anatoly Merhav, Neri The Department of Electrical Engineering–Systems Tel Aviv University Tel Aviv6997801 Israel The Viterby Faculty of Electrical and Computer Engineering Technion–Israel Institute of Technology Haifa3200004 Israel

The problem of transmitting a parameter value over an additive white Gaussian noise (AWGN) channel is considered, where, in addition to the transmitter and the receiver, there is a helper that observes the noise non-causally and provides a description of limited rate Rh to the transmitter and/or the receiver. We derive upper and lower bounds on the optimal achievable α-th moment of the estimation error and show that they coincide for small values of α and for high values of Rh. The upper bound relies on a recently proposed channel-coding scheme that effectively conveys Rh bits essentially error-free and the rest of the rate—over the same AWGN channel without help, with the error-free bits being allocated to the most significant bits of the quantized parameter. We then concentrate on the setting with a total transmit energy constraint, for which we derive achievability results for both channel coding and parameter modulation for several scenarios: when the helper assists only the transmitter or only the receiver and knows the noise, and when the helper assists the transmitter and/or the receiver and knows both the noise and the message. In particular, for the message-informed helper that assists both the receiver and the transmitter, it is shown that the error probability in the channel-coding task decays doubly exponentially. Finally, we translate these results to those for continuous-time power-limited AWGN channels with unconstrained bandwidth. As a byproduct, we show that the capacity with a message-informed helper that is available only at the transmitter can exceed the sum of the capacity without help and the help rate Rh, when the helper knows only the noise but not the message. © 2023, CC BY-SA.

关键词： Transmitters

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Probabilistic Constraint for Safety-Critical Reinforcement Learning

arXiv

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arXiv 2023年

作者： Chen, Weiqin Subramanian, Dharmashankar Paternain, Santiago Department of Electrical Computer and Systems Engineering Renssealaer Polytechnic Institute IBM T.J. Watson Research Center United States

In this paper, we consider the problem of learning safe policies for probabilistic-constrained reinforcement learning (RL). Specifically, a safe policy or controller is one that, with high probability, maintains the trajectory of the agent in a given safe set. We establish a connection between this probabilistic-constrained setting and the cumulative-constrained formulation that is frequently explored in the existing literature. We provide theoretical bounds elucidating that the probabilistic-constrained setting offers a better trade-off in terms of optimality and safety (constraint satisfaction). The challenge encountered when dealing with the probabilistic constraints, as explored in this work, arises from the absence of explicit expressions for their gradients. Our prior work provides such an explicit gradient expression for probabilistic constraints which we term Safe Policy Gradient-REINFORCE (SPG-REINFORCE). In this work, we provide an improved gradient SPG-Actor-Critic that leads to a lower variance than SPG-REINFORCE, which is substantiated by our theoretical results. A noteworthy aspect of both SPGs is their inherent algorithm independence, rendering them versatile for application across a range of policy-based algorithms. Furthermore, we propose a Safe Primal-Dual algorithm that can leverage both SPGs to learn safe policies. It is subsequently followed by theoretical analyses that encompass the convergence of the algorithm, as well as the near-optimality and feasibility on average. In addition, we test the proposed approaches by a series of empirical experiments. These experiments aim to examine and analyze the inherent trade-offs between the optimality and safety, and serve to substantiate the efficacy of two SPGs, as well as our theoretical contributions. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Design and Modeling of a Two-stage PV Inverter for Single Phase and Three Phase Applications

Design and Modeling of a Two-stage PV Inverter for Single Ph...

引用

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Yidi Shen Reza Asrar Ghaderloo Chanaka Singhabahu Rakesh Resalayyan Alireza Khaligh Department of Electrical and Computer Engineering Maryland Power Electronics Laboratory MD USA The Institute for Systems Research University of Maryland MD USA

This paper presents the modeling and design of a 1kW two-stage photovoltaic (PV) inverter compatible with both single phase and three phase grid. The topology consists of a cascade of dual-active-bridge (DAB) based dc-dc stage and a two-level voltage source inverter (VSI) stage. Two-level VSI based dc-ac stage is designed and the modulation schemes are compared considering operation for both single phase and three phase output connection. A 3 leg interleaved hybrid pulse width modulation (3 leg-IHPWM) is proposed for single phase operation to reduce power loss and current total harmonic distortion (THD) with full utilization of the third switching leg. Sizing of dc link capacitance is analyzed for both single phase power decoupling and three phase load unbalance. Circuit design and component selection are conducted including dc link voltage selection, output filter sizing, DAB dc-dc stage transformer sizing and modulation optimization for zero-voltage-switching (ZVS) ability and control flexibility. The proposed design is verified through hardware prototype and experimental testing.

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Consensus Control of Leader-Following Multi-Agent systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

引用

2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Incremental-Dissipativity-Based Synchronization of Interconnected systems with MIMO Nonlinear Operators

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IFAC-PapersOnLine 2023年第2期56卷 1853-1858页

作者： Tao Liu David J. Hill Jun Zhao Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong SAR Shenzhen Institute of Research and Innovation University of Hong Kong Shenzhen China Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia College of Information Science and Engineering Northeastern University Shenyang China

This paper considers a type of dynamical networks with incrementally dissipative multiple inputs and multiple outputs nonlinear operators that are interconnected both internal and external interconnections. A novel quadratic form of incremental dissipativity is used to investigate synchronization of such a network from an input-output approach point of view. It is shown that the synchronization problem is equivalent to a relative stability issue of a large-scale interconnected nonlinear system with dissipative subsystems. Therefore, traditional dissipativity theory is used for the synchronization analysis.

关键词： Incremental dissipativity synchronization multiple-input multiple-output operator interconnected systems stability of nonlinear systems

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Multi-Layer Network Formation through HAPS Base Station and Transmissive RIS-Equipped UAV

arXiv

引用

arXiv 2024年

作者： Khennoufa, Faical Abdellatif, Khelil Yanikomeroglu, Halim Ozturk, Metin Elganimi, Taissir Kara, Ferdi Rabie, Khaled LGEERE Lab Department of Electrical Engineering El-Oued University El-Oued Algeria NTN Lab Department of Systems and Computer Engineering Carleton University Ottawa Canada Electrical and Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey Department of Electrical and Electronic Engineering University of Tripoli Libya Division of Communications Systems KTH Royal Institute of Technology Sweden Department of Computer Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Dhahran Saudi Arabia

In order to bolster future wireless networks, there has been a great deal of interest in non-terrestrial networks, especially aerial platforms including high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). These platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA). In this regard, this paper proposes a multi-layer network architecture consisting of HAPS and UAV, where the former acts as a HAPS super macro base station (HAPS-SMBS), while the latter serves as a relay node for the ground Internet of Things (IoT) devices. The UAV is equipped with active transmissive RIS, which is a novel technology with promising benefits. We also utilize multiple-input single-output (MISO) technology, i.e., multiple antennas at the HAPS-SMBS and a single antenna at the IoT devices. Additionally, we consider NOMA as the multiple access technology as well as the existence of hardware impairments as a practical limitation. We compare the proposed system model with various scenarios, all involving the HAPS-SMBS and RIS-equipped UAV relay combination, but with different types of RIS, antenna configurations, and access technologies. Sum rate and energy efficiency are used as performance metrics, and the findings demonstrate that, in comparison to all benchmarks, the proposed system yields significant performance gains. Moreover, hardware impairment limits the system performance at high transmit power levels. © 2024, CC0.

关键词： Internet of things

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Data-driven Deep Reinforcement Learning for Automated Driving

Data-driven Deep Reinforcement Learning for Automated Drivin...

引用

International Conference on Intelligent Transportation

作者： Avinash Prabu Lingxi Li Yaobin Chen Brian King Department of Electrical and Computer Engineering Transportation and Autonomous Systems Institute (TASI) also with the Indiana University-Purdue University Indianapolis Indianapolis IN USA

Path-tracking control is an integral part of motion planning in autonomous vehicles, where a control system on the vehicle will provide acceleration and steering angle commands to ensure accurate tracking of its longitudinal and lateral movements in reference to a pre-defined trajectory. In this paper, a scenario and machine learning-based data-driven control approach is proposed for a path-tracking controller. Firstly, a deep reinforcement learning (DRL) model is developed to facilitate the control of the vehicle's longitudinal speed. A deep deterministic policy gradient algorithm is employed to train the reinforcement learning model. The main objective of this model is to maintain a safe distance from a lead vehicle (if present) or track a velocity set by the driver. Secondly, a lateral steering controller is developed to control the steering angle of the vehicle with the main goal of following a reference trajectory. Finally, the longitudinal and lateral control models are coupled to obtain a complete path-tracking controller at a wide range of vehicle speeds. The state-of-the-art model-based path-tracking controller is also built (using the model predictive control and Stanley control) to evaluate the performance of the proposed model. The results showed that the performance of the proposed data-driven DRL control model is effective compared with model-based control approaches (in terms of the velocity error, lateral yaw angle error, and lateral distance error).

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