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Reinforcement Learning Based Dynamic Power Control for UAV Mobility Management

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Reinforcement Learning Based Dynamic Power Control for UAV M...

Asilomar Conference on Signals, systems & computers

作者： Irshad A. Meer Karl-Ludwig Besser Mustafa Ozgerl H. Vincent Poor Cicek Cavdar Department of Electrical and Computer Engineering Princeton University USA Division of Communication Systems KTH Royal Institute of Technology Sweden

Modern communication systems need to fulfill multiple and often conflicting objectives at the same time. In particular, new applications require high reliability while operating at low transmit powers. Moreover, reliability constraints may vary over time depending on the current state of the system. One solution to address this problem is to use joint transmissions from a number of base stations (BSs) to meet the reliability requirements. However, this approach is inefficient when considering the overall total transmit power. In this work, we propose a reinforcement learning-based power allocation scheme for an unmanned aerial vehicle (UAV) communication system with varying communication reliability requirements. In particular, the proposed scheme aims to minimize the total transmit power of all BSs while achieving an outage probability that is less than a tolerated threshold. This threshold varies over time, e.g., when the UAV enters a critical zone with high-reliability requirements. Our results show that the proposed learning scheme uses dynamic power allocation to meet varying reliability requirements, thus effectively conserving energy.

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Combinatorial Multi-armed Bandits: Arm Selection via Group Testing

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arXiv 2024年

作者： Mukherjee, Arpan Ubaru, Shashanka Murugesan, Keerthiram Shanmugam, Karthikeyan Tajer, Ali Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States IBM T.J. Watson Research Center United States

This paper considers the problem of combinatorial multi-armed bandits with semi-bandit feedback and a cardinality constraint on the super-arm size. Existing algorithms for solving this problem typically involve two key sub-routines: (1) a parameter estimation routine that sequentially estimates a set of base-arm parameters, and (2) a super-arm selection policy for selecting a subset of base arms deemed optimal based on these parameters. State-of-the-art algorithms assume access to an exact oracle for super-arm selection with unbounded computational power. At each instance, this oracle evaluates a list of score functions, the number of which grows as low as linearly and as high as exponentially with the number of arms. This can be prohibitive in the regime of a large number of arms. This paper introduces a novel realistic alternative to the perfect oracle. This algorithm uses a combination of group-testing for selecting the super arms and quantized Thompson sampling for parameter estimation. Under a general separability assumption on the reward function, the proposed algorithm reduces the complexity of the super-arm-selection oracle to be logarithmic in the number of base arms while achieving the same regret order as the state-of-the-art algorithms that use exact oracles. This translates to at least an exponential reduction in complexity compared to the oracle-based approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Parameter estimation

Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective

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arXiv 2023年

作者： Abu-Rasheed, Hasan Weber, Christian Fathi, Madjid Department of Electrical Engineering and Computer Science Institute of Knowledge Based Systems and Knowledge Management University of Siegen Siegen Germany

Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner’s current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context-aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. During the last decade, several approaches have emerged in the literature to define the learning context and the factors that may capture it. Those approaches led to different definitions of contextualized learner-profiles. In this paper, we review the state-of-the-art approaches for defining a user’s learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view.

关键词： Recommender systems

Optimal Deep Learning Enabled Statistical Analysis Model for Traffic Prediction

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computers, Materials & Continua 2022年第9期72卷 5563-5576页

作者： Ashit Kumar Dutta S.Srinivasan S.N.Kumar T.S.Balaji Won Il Lee Gyanendra Prasad Joshi Sung Won Kim Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India Department of Electrical&Electronics Engineering Amal Jyothi College of EngineeringKanjirappallyKerala686518India Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India Department of Electronics and Communication Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Business Administration Hanbat National National UniversityDaejeon34158Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control,navigation,route mapping,*** traffic prediction model aims to predict the traffic conditions based on the past traffic *** more accurate traffic prediction,this study proposes an optimal deep learning-enabled statistical analysis *** study offers the design of optimal convolutional neural network with attention long short term memory(OCNN-ALSTM)model for traffic *** proposed OCNN-ALSTM technique primarily preprocesses the traffic data by the use of min-max normalization ***,OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time *** enhancing the predictive outcomes of the OCNN-ALSTM technique,the bird swarm algorithm(BSA)is employed to it and thereby overall efficacy of the network gets *** design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the *** experimental validation of the OCNNALSTM technique is performed using benchmark datasets and the results are examined under several *** simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions.

关键词： Statistical analysis predictive models deep learning traffic prediction bird swarm algorithm

Differential Equation Approximations for Population Games using Elementary Probability

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arXiv 2023年

作者： Kara, Semih Martins, Nuno C. The Electrical and Computer Engineering Department The Institute for Systems Research University of Maryland College ParkMD20742 United States

Population games model the evolution of strategic interactions among a large number of uniform agents. Due to the agents' uniformity and quantity, their aggregate strategic choices can be approximated by the solutions of a class of ordinary differential equations. This mean-field approach has found to be an effective tool of analysis. However its current proofs rely on advanced mathematical techniques, making them less accessible. In this article, we present a simpler derivation, using only undergraduate-level probability. Copyright © 2023, The Authors. All rights reserved.

关键词： Ordinary differential equations

Fuzzy Object Ambiguity Determination and Human Attention Assessment for Domestic Service Robots

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Fuzzy Object Ambiguity Determination and Human Attention Ass...

International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Kevin Fan Melanie Jouaiti Kerstin Dautenhahn Chrystopher L. Nehaniv Department of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada Departments of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada

Domestic service robots have the promising potential of bringing significant services to the general population, and more importantly, successful applications of universal domestic service robots can potentially help mitigate critical societal issues such as senior care. In order to do so, domestic service robots need to integrate seamlessly into home environments. However, home environments are dynamic, complex and filled with personal items. Therefore, ambiguity can quickly arise for robots operating in such rich environments. In this paper, we propose an object ambiguity determination system that can determine the level of ambiguity in robot object selection tasks with fuzzy logic data integration. Additionally, we propose a functional human attention assessment system with fuzzy logic that enables the robot to determine user attention before committing to general disambiguation processes. Our preliminary results show that the proposed fuzzy logic inference systems can reliably estimate the robot object selection task ambiguity from object confidence level and the number of potential target objects that satisfy the user's command. Furthermore, fuzzy inference is applied to decide human eye gaze direction robustly. These subsystems can be utilized in the context of human-robot interaction to guide the robot when to seek feedback from a human partner in order to disambiguate reference in domestic service tasks. The source code of all proposed systems is available publicly on GitHub. 1

关键词： Fuzzy logic Service robots Source coding Sociology Human-robot interaction Data integration Reliability

Consensus-Based Leader-Follower Formation Tracking for Control-Affine Nonlinear Multiagent systems

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arXiv 2023年

作者： Enwerem, Clinton Baras, John S. The Department of Electrical & Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

In the typical multiagent formation tracking problem centered on consensus, the prevailing assumption in the literature is that the agents’ nonlinear models can be approximated by integrator systems, by their feedback-linearized equivalents, or by dynamics composed of deterministic linear and nonlinear terms. The resulting approaches associated with such assumptions, however, are hardly applicable to general nonlinear systems. To this end, we present consensus-based control laws for multiagent formation tracking in finite-dimensional state space, with the agents represented by a more general class of dynamics: control-affine nonlinear systems. The agents also exchange information via a leader-follower communication topology modeled as an undirected and connected graph with a single leader node. By leveraging standard tools from algebraic graph theory and Lyapunov analysis, we first derive a locally asymptotically stabilizing formation tracking law. Next, to demonstrate the effectiveness of our approach, we present results from numerical simulations of an example in robotics. These results — together with a comparison of the formation errors obtained with our approach and those realized via an optimization-based method — further validate our theoretical propositions. © 2023, CC BY.

关键词： Multi agent systems

Resource Efficiency vs Performance Isolation Tradeoff in Network Slicing

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arXiv 2023年

We consider the tradeoff between resource efficiency and performance isolation that emerges when multiplexing the resource demands of Network Slices (NSs). On the one hand, multiplexing allows the use of idle resources, which increases resource efficiency. On the other hand, the performance of each NS becomes susceptible to traffic surges in other NSs, which degrades performance isolation. The analysis of this tradeoff enables network operators to determine the effect of performance isolation on the operating cost of each NS. To study the tradeoff, we solve an optimization problem where we find the multiplexing policy that requires the least provisioned resources to honor the Service Level Agreements (SLAs) of all NSs. The SLA of each NS i states that its resource demand should be met for PiH fraction of time, and for PiL ≤ PiH fraction of time, it should be met regardless of the demands of other NSs. For resource demands that follow ergodic Markov chains, we show that the well-known Max-Weight scheduler is an optimal multiplexing policy. Since the Max-Weight scheduler does not require any knowledge of the statistics of the resource demands, we also propose its use in non-markovian settings. For resource demands obtained in the LTE module of ns-3, we show that the Max-Weight scheduler reduces the provisioned bandwidth by 36.2% when no performance isolation is required. Lastly, for these non-markovian resource demands, the Max-Weight scheduler maintains its optimality since it requires as much provisioned bandwidth as the best non-causal scheduler. © 2023, CC BY.

关键词： Bandwidth

Graph-Based Visual-Kinematic Fusion and Monte Carlo Initialization for Fast-Deployable Cable-Driven Robots

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Graph-Based Visual-Kinematic Fusion and Monte Carlo Initiali...

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： R. Khorrambakht H. Damirchi M.R. Dindarloo A. Saki S.A. Khalilpour Hamid D. Taghirad Stephan Weiss Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY School of Computer Science University of Adelaide Australia Faculty of Electrical Engineering University of Technology Tehran Iran Department of Smart Systems Technologies University of Klagenfurt Austria

Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework that presents a unified localization and calibration system which requires no initial values for the kinematic parameters. This is achieved by formulating a Monte-Carlo algorithm that initializes a factor-graph representation of the calibration and localization problem. With this, we are able to jointly identify both the kinematic parameters and the visual odometry scale alongside their corresponding uncertainties. We demonstrate the practical applicability of the framework using our state-estimation dataset recorded with the ARAS-CAM suspended cable driven parallel robot, and published as part of this manuscript.

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