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3D Multi-Trajectory and Pick-Up Optimization of UAV for Minimizing Delivery Time With Weight Restriction

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 17562-17573页

作者： Park, Gitae Lee, Woongsup Lee, Kisong Dongguk Univ Dept Informat & Commun Engn Seoul 04620 South Korea Yonsei Univ Grad Sch Informat Seoul 03722 South Korea

In this study, we explore a three-dimensional trajectory and pick-up design of an unmanned aerial vehicle (UAV) for parcel delivery. In particular, we consider the real-world scenario in which a weight-restricted UAV cannot pick up all parcels within a single route;therefore the parcel delivery must be divided into multiple trajectories while avoiding no-fly zones. We formulate this problem mathematically as the minimization of total delivery time, which jointly optimizes the pick-up indicators, the lengths of the time slots, and the horizontal and vertical trajectories. To address the non-convexity of the formulated mixed-integer nonlinear programming, we employ a successive convex approximation to convert the problem into a convex form concerning optimization variables and utilize a penalty convex-concave procedure to preserve the binary characteristics of the pick-up indicators. Subsequently, we propose an iterative algorithm based on a block decent algorithm to efficiently identify the optimal solution by solving the relaxed convex problem. To address the problem of high computational complexity associated with the optimization-based algorithm, we also present an unsupervised deep learning (DL)-based heuristic algorithm. The simulation results confirm that the proposed schemes achieve considerably shorter delivery times than the baseline schemes in various scenarios. Furthermore, the DL-based scheme requires about 10% longer delivery time than the optimization-based scheme, but it can approximate the UAV strategy with substantially reduced computation time.

关键词： Autonomous aerial vehicles Trajectory Three-dimensional displays Land vehicles approximation algorithms Wireless communication Payloads Unmanned aerial vehicle parcel delivery 3D trajectory pick-up design convex optimization deep learning

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A Survey on the Densest Subgraph Problem and Its Variants

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ACM COMPUTING SURVEYS 2024年第8期56卷 1页

作者： Lanciano, Tommaso Miyauchi, Atsushi Fazzone, Adriano Bonchi, Francesco KTH Royal Inst Technol Stockholm Sweden Sapienza Univ Dipartimento Ingn Informat Automat & Gest Antonio Via Ariosto 25 I-00185 Rome Italy CENTAI Inst Corso Inghilterra 3 I-10138 Turin Italy

The Densest Subgraph Problem requires us to find, in a given graph, a subset of vertices whose induced subgraph maximizes a measure of density. The problem has received a great deal of attention in the algorithmic literature since the early 1970s, with many variants proposed and many applications built on top of this basic definition. Recent years have witnessed a revival of research interest in this problem with several important contributions, including some groundbreaking results, published in 2022 and 2023. This survey provides a deep overview of the fundamental results and an exhaustive coverage of the many variants proposed in the literature, with a special attention to the most recent results. The survey also presents a comprehensive overview of applications and discusses some interesting open problems for this evergreen research topic.

关键词： Graphs density densest subgraph approximation algorithms applications

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Peak AoI Minimization With Directional Charging for Data Collection at Wireless-Powered Network Edge

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2747-2761页

作者： Chen, Quan Guo, Song Xu, Wenchao Li, Jing Wei, Kang Cai, Zhipeng Gao, Hong Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Kowloon Hong Kong 999077 Peoples R China Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong 999077 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321017 Peoples R China

Age of Information (AoI) has emerged as a new metric to measure data freshness from the destination's perspective. To optimize the system AoI, most existing works focused on the point of scheduling of update transmissions. While at wireless-powered network edge, the source nodes can only transmit their updates after being charged ready, which means the system AoI is not only determined by the update transmission strategies, but also the charging strategies. Thus, in this article, we investigate the first work to optimize the weighted peak AoI from the point of charging at wireless-powered network edge. First, the problem of optimizing the weighted sum of average peak AoI with a directional charger is formulated, and then transformed to a charging time optimization problem with respect to the charging orientations and peak AoI, and an approximate algorithm is proposed to obtain the required charging time for each source node. Second, an age-based scheduling algorithm is proposed to compute the charging decisions and transmission decisions simultaneously, which can not only optimize the weighted sum of average peak AoI, but also guarantee the maximum peak AoI of each source node is bounded. The proposed algorithm is proved to have an approximation ratio of up to (1+ phi ), where phi is a small value related to the weight of each source node. When there exist multiple chargers, an approximate algorithm is also proposed to minimize the weighted sum of average peak AoI by scheduling the orientations of these chargers cooperatively. Finally, the extensive simulations demonstrate the high performance of the proposed algorithms in terms of peak AoI.

关键词： Optimization approximation algorithms Scheduling Scheduling algorithms Data communication Computer science Internet of Things Age of information data collection directional charging peak AoI wireless-powered network edge

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Rate Splitting Multiple Access: Optimal Beamforming Structure and Efficient Optimization algorithms

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 15642-15657页

作者： Fang, Tianyu Mao, Yijie ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

Joint optimization for common rate allocation and beamforming design have been widely studied in rate splitting multiple access (RSMA) empowered multiuser multi-antenna transmission networks. Due to the highly coupled optimization variables and non-convexity of the joint optimization problems, emerging algorithms such as weighted minimum mean square error (WMMSE) and successive convex approximation (SCA) have been applied to RSMA which typically approximate the original problem with a sequence of disciplined convex subproblems and solve each subproblem by an optimization toolbox. While these approaches are capable of finding a viable solution, they are unable to offer a comprehensive understanding of the solution structure and are burdened by high computational complexity. In this work, for the first time, we identify the optimal beamforming structure and common rate allocation for the weighted sum-rate (WSR) maximization problem of RSMA. We then propose a computationally efficient optimization algorithm that jointly optimizes the beamforming and common rate allocation without relying on any toolbox. Specifically, we first approximate the original WSR maximization problem with a sequence of convex subproblems based on fractional programming (FP). By exploiting the Karush-Kuhn-Tucker (KKT) conditions of each subproblem, the optimal beamforming structure is derived. An efficient hyperplane fixed point iteration method is then proposed to find the optimal Lagrangian dual variables. Numerical results show that the proposed algorithm achieves the same performance but takes only 0.5% or less simulation time compared with the state-of-the-art WMMSE, SCA, and FP algorithms. The proposed algorithms pave the way for the practical and efficient optimization algorithm design for RSMA and its applications in 6G.

关键词： Array signal processing Optimization Resource management approximation algorithms Streams Signal processing algorithms Interference Rate-splitting multiple access beamforming optimization weighted sum-rate maximization optimal beamforming structure

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Distributed Multiagent Reinforcement Learning With Action Networks for Dynamic Economic Dispatch

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第7期35卷 9553-9564页

作者： Hu, Chengfang Wen, Guanghui Wang, Shuai Fu, Junjie Yu, Wenwu Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Sch Math Dept Syst Sci Nanjing 211189 Peoples R China Beihang Univ Res Inst Frontier Sci Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

A new class of distributed multiagent reinforcement learning (MARL) algorithm suitable for problems with coupling constraints is proposed in this article to address the dynamic economic dispatch problem (DEDP) in smart grids. Specifically, the assumption made commonly in most existing results on the DEDP that the cost functions are known and/or convex is removed in this article. A distributed projection optimization algorithm is designed for the generation units to find the feasible power outputs satisfying the coupling constraints. By using a quadratic function to approximate the state-action value function of each generation unit, the approximate optimal solution of the original DEDP can be obtained by solving a convex optimization problem. Then, each action network utilizes a neural network (NN) to learn the relationship between the total power demand and the optimal power output of each generation unit, such that the algorithm obtains the generalization ability to predict the optimal power output distribution on an unseen total power demand. Furthermore, an improved experience replay mechanism is introduced into the action networks to improve the stability of the training process. Finally, the effectiveness and robustness of the proposed MARL algorithm are verified by simulation.

关键词： Power demand Heuristic algorithms Prediction algorithms Couplings approximation algorithms Power system stability Convex functions Distributed optimization dynamic economic dispatch multiagent reinforcement learning (MARL) smart grids

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Multimodal Learning-Based Interval Type-2 Fuzzy Neural Network

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第11期32卷 6409-6423页

作者： Sun, Chenxuan Wu, Xiaolong Yang, Hongyan Han, Honggui Zhao, Dezheng Beijing Univ Technol Minsit Educ Fac Informat Technol Engn Res Ctr Digital CommunityBeijing Key Lab Com Beijing 100124 Peoples R China Intelligence Technol CEC Co Ltd Beijing 102209 Peoples R China

Interval type-2 fuzzy neural network (IT2FNN) has extensive applications for modeling nonlinear systems with multidimensional structured data. However, the traditional IT2FNN based on the structured topology struggles to identify nonlinear systems using semistructured and unstructured data. To tackle this issue, a multimodal learning-based IT2FNN (ML-IT2FNN) is developed for joint learning of the multimodal data. First, an encoding layer with a multimodal perception strategy is designed to identify the multimodal information. The parameterized modalities are utilized to map the features of the semistructured and unstructured data into the structured spaces. Second, a multimodal representation mechanism is introduced to extract the features of multiple modalities from the structured spaces. In this mechanism, type-2 fuzzy sets with soft boundaries are used to intricate coupling relationships among modalities by adapting to the nuances of multimodal data. Third, a constrained hybrid learning algorithm, combining parallel and sequential updating frameworks, is presented to optimize the parameters of ML-IT2FNN. The type-2 fuzzy parameters and the coupling parameters with constraints are updated adaptively to facilitate the intramodal identification performance and cross-modal interaction performance. Finally, a series of examples in nonlinear systems are introduced to verify ML-IT2FNN. Empirical results demonstrate that ML-IT2FNN surpasses the cutting-edge approaches with accuracy.

关键词： Nonlinear systems Couplings Feature extraction Data mining approximation algorithms Uncertainty Neural networks Coupling relationship interval type-2 fuzzy neural network multimodal information parameterized modalities

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3D Multi-UAV Computing Networks: Computation Capacity and Energy Consumption Tradeoff

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第7期73卷 10627-10641页

作者： Xu, Yu Zhang, Tiankui Liu, Yuanwei Yang, Dingcheng Xiao, Lin Tao, Meixia Nanchang Univ Informat Engn Sch Nanchang 330031 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Kyung Hee Univ Dept Elect Engn Yongin 17104 South Korea Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

Unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) networks have greatly practical significance for improving the computation capacity of ground terminal devices (TDs) in Internet of Things (IoT), while the triggered energy consumption becomes a non-negligible critical issue. In this article, we propose a novel MEC framework assisted by multiple UAVs. Our goal is to balance the computation capacity and the energy consumption to maximize the network utility, by jointly optimizing power control, UAV-TD pairing, computation resource allocation, and UAVs' 3D trajectory. In particular, a flexible fairness-aware mechanism is set to maintain the fairness among these TDs. To solve the problem, we characterize the expected offloading rate on the basis of statistical channel information. Then, we propose a joint optimization algorithm based on the successive convex approximation technique and the block coordinate descent structure. The proposed trajectory optimization algorithm caters the UAV-MEC missions as the proposed algorithm is dedicated for solving the constrained UAV 3D path planning problems, and the results can be implemented in practice. Numerical results demonstrate the tradeoff between the computation capacity and the energy consumption, and validate the superiority of the proposed algorithm in the terms of improving the network utility.

关键词： Autonomous aerial vehicles Three-dimensional displays Energy consumption Trajectory planning Fading channels Energy efficiency approximation algorithms 3D trajectory optimization mobile edge computing (MEC) power control unmanned aerial vehicle (UAV)

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Learning-Based Maximum Likelihood Estimator for Angle-of-Arrival Localization

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 5388-5405页

作者： Zhou, Chengyi Liu, Meiqin Zhang, Senlin Zheng, Ronghao Dong, Shanling Liu, Zhunga Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Natl Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Xi An Jiao Tong Univ Natl Key Lab Human Machine Hybrid Augmented Intell Xian 710049 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

The estimation of target positions from angle-of-arrival (AOA) measurements has been extensively researched, and various estimators have been proposed to tackle this challenge. Among these, the maximum likelihood estimator (MLE) is notable for its well-recognized properties, including asymptotic unbiasedness and efficiency. However, traditional MLEs, such as the Gauss-Newton algorithm, often encounter difficulties due to the need for a first-order linearization step in computing the Jacobian matrix. This requirement introduces the potential for significant errors and convergence issues, especially in highly nonlinear systems. To overcome this limitation, this paper introduces a learning framework to address the maximum likelihood estimation problem, where the iterative increments are treated as the output of the agent's actions. Building upon this framework, we develop a learning-based MLE. Comprehensive numerical simulation results demonstrate the effectiveness and superiority of our approach. First, it effectively resolves convergence issues associated with linearization in traditional MLEs. Second, it exhibits robust adaptability by successfully solving both two-dimensional and three-dimensional AOA localization problems. Last, the proposed method significantly enhances localization accuracy compared to existing estimators.

关键词： Location awareness Maximum likelihood estimation Sensors Optimization Noise measurement approximation algorithms Jamming Angle-of-arrival localization maximum likelihood estimator Gauss-Newton algorithm first-order linearization deep reinforcement learning

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Shipment Consolidation with Multiple Shipping Methods Under Nonlinear Cost Structures

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MANAGEMENT SCIENCE 2024年第5期70卷 2823-2841页

作者： Xu, Zhou Li, Feng Chen, Zhi-Long Hong Kong Polytech Univ Fac Business Kowloon Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Univ Maryland Robert H Smith Sch Business College Pk MD 20742 USA

We study a shipment consolidation problem commonly faced by companies that outsource logistics operations and operate in a commit-to-delivery mode. It involves delivering a given set of orders to their destinations by their committed due times using multiple shipping methods at the minimum total shipping and inventory cost. The shipping cost is generally nonlinear in shipping quantity and can be represented by a subadditive piecewise linear function. We investigate two shipping scenarios, one involving long-haul shipping only and the other involving joint long-haul and short-haul shipping. We develop analytical results and solution algorithms for the shipment consolidation problem under each shipping scenario. The problem under the first shipping scenario is shown to be strongly NP-hard. We find that a simple policy, called the First-Due-First-Delivered (FDFD) policy, which assigns orders with earlier delivery due times to shipping methods with earlier destination arrival times, is very effective. This policy enables us to develop a polynomial time algorithm, which not only solves the problem under the concave shipping cost structure optimally but also achieves a performance guarantee of 2 for the problem under the general subadditive shipping cost structure. For the problem under the second shipping scenario, we extend the FDFD policy for long-haul shipping and derive another policy, called the No-Wait policy, for short-haul shipping. We use these policies to develop a polynomial time algorithm and analyze its performance guarantee. Our computational experiments show that the algorithm significantly outperforms a commercial optimization practical situations.

关键词： shipment consolidation nonlinear cost structures long-haul and short-haul shipping First-Due-First-Delivered policy No-Wait policy dynamic programming approximation algorithms

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Action Candidate Driven Clipped Double Q-Learning for Discrete and Continuous Action Tasks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第4期35卷 5269-5279页

作者： Jiang, Haobo Li, Guangyu Xie, Jin Yang, Jian Nanjing Univ Sci & Technol PCA Lab Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Key Lab Intelligent Percept & Syst High Dimens In Minist Educ Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Image & Video Understanding Socia Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) problems. Clipped double Q-learning, as an effective variant of double Q-learning, employs the clipped double estimator to approximate the maximum expected action value. Due to the underestimation bias of the clipped double estimator, the performance of clipped Double Q-learning may be degraded in some stochastic environments. In this article, in order to reduce the underestimation bias, we propose an action candidate-based clipped double estimator (AC-CDE) for Double Q-learning. Specifically, we first select a set of elite action candidates with high action values from one set of estimators. Then, among these candidates, we choose the highest valued action from the other set of estimators. Finally, we use the maximum value in the second set of estimators to clip the action value of the chosen action in the first set of estimators and the clipped value is used for approximating the maximum expected action value. Theoretically, the underestimation bias in our clipped Double Q-learning decays monotonically as the number of action candidates decreases. Moreover, the number of action candidates controls the tradeoff between the overestimation and underestimation biases. In addition, we also extend our clipped Double Q-learning to continuous action tasks via approximating the elite continuous action candidates. We empirically verify that our algorithm can more accurately estimate the maximum expected action value on some toy environments and yield good performance on several benchmark problems. Code is available at https://***/Jiang-HB/ac_CDQ.

关键词： Q-learning Task analysis Benchmark testing approximation algorithms Toy manufacturing industry Markov processes Learning systems Clipped double Q-learning overestimation bias reinforcement learning underestimation bias

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