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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

引用

arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Constructing corpus for query-oriented XML text summarization

Constructing corpus for query-oriented XML text summarizatio...

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International Conference on Management of E-Commerce and E-Goverment

作者： Wu, Shihan Liu, Dexi Jiao, Xianpei Jiangxi Key Laboratory of Data and Knowledge Engineering School of Information Technology Jiangxi University of Finance and Economics Nanchang Jiangxi China

ISBN: (纸本)9780769542454

XML Retrieval is becoming the focus study of the field of Information Retrieval and database. Summarization of the results which come from the XML search engines will alleviate the read burden of user's. However, as the basis of this study, the construction of the query-oriented XML text summarization corpus has not yet received enough attention. In this paper, we introduce our works on constructing this kind of corpus, including the selection of topics and XML elements/documents, construction process and the feature of the constructed corpus. Up to now, the corpus has 25 English query topics, including 422 elements for summarization, and 32 Chinese topics which including 402 elements. For each topic, 4 pieces of extracted summaries and 4 pieces of generated summaries are made manually by 4 experts. © 2010 IEEE.

关键词： XML

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MRST-- An Efficient Monitoring Technology of Summarization on Stream data

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Journal of Computer Science & Technology 2007年第2期22卷 190-196页

作者：樊小泊解婷婷李翠平陈红 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China

Monitoring on data streams is an efficient method of acquiring the characters of data stream. However the available resources for each data stream are limited, so the problem of how to use the limited resources to process infinite data stream is an open challenging problem. In this paper, we adopt the wavelet and sliding window methods to design a multi-resolution summarization data structure, the Multi-Resolution Summarization Tree （MRST） which can be updated incrementally with the incoming data and can support point queries, range queries, multi-point queries and keep the precision of queries. We use both synthetic data and real-world data to evaluate our algorithm. The results of experiment indicate that the efficiency of query and the adaptability of MRST have exceeded the current algorithm, at the same time the realization of it is simpler than others.

关键词： Haar wavelet sliding window stream data

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Factor decomposition of regional carbon emissions from energy consumption for China

Factor decomposition of regional carbon emissions from energ...

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3rd International Conference on Energy and Environmental Protection, ICEEP 2014

作者： Qi, Ya-Wei School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9783038351375

This paper calculates the carbon emissions from energy consumption of 30 provinces in China through 2000-2010, and research correlation of factors such as regional economic gap and regional characteristics of carbon emissions in the process of regional economic coordinated development. The LMDI decomposition model is used to decompose the growth rate of China's carbon emissions into 4 types of driving factors, i.e. GDP, industrial pollutants emission intensity, industrial structure and imbalance of regional economic development, to analyze influence of scale effect, technical effect, industrial structure effect and regional spatial structure effect on carbon emissions in the process of China's regional economic development. The results show that: The scale effect is determinants of carbon emissions increasing. The technical effect is the most important force to inhibit the increment of carbon emissions. Industrial structure effect and regional spatial structure effect on carbon emissions are not yet stable, but have a certain pull impact on increasing carbon emissions. © (2014) Trans Tech Publications, Switzerland.

关键词： Energy utilization

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SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking 25

SoAy: A Solution-based LLM API-using Methodology for Academi...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Yuanchun Wang Jifan Yu Zijun Yao Jing Zhang Yuyang Xie Shangqing Tu Yiyang Fu Youhe Feng Jinkai Zhang Jingyao Zhang Bowen Huang Yuanyao Li Huihui Yuan Lei Hou Juanzi Li Jie Tang School of Information Renmin University of China Beijing China & Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Institute of Education Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Information Renmin University of China Beijing China & Engineering Research Center of Database and Business Intelligence MOE Beijing China School of Information Renmin University of China Beijing China Zhipu AI Beijing China

ISBN: (纸本)9798400712456

Applying large language models (LLMs) to academic API usage shows promise in reducing researchers' efforts to seek academic information. However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution. This solution simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency. LLMs are aligned with this solution-oriented, code-based reasoning method by automatically enumerating valid API coupling sequences and transforming them into queries and executable *** evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://***/RUCKBReasoning/SoAy.

关键词： academic information seeking

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Cluster labeling extraction and ranking feature selection for high quality XML pseudo relevance feedback fragments set

Cluster labeling extraction and ranking feature selection fo...

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9th International Conference on Advanced data Mining and Applications, ADMA 2013

作者： Zhong, Minjuan Wan, Changxuan Liu, Dexi Liao, Shumei Luo, Siwen School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9783642539169

In traditional pseudo feedback, the main reason of the topic drift is the low quality of the feedback source. Clustering search results is an effective way to improve the quality of feedback set. For XML data, how to effectively perform clustering algorithm and then identify good xml fragments from the clustering results is a intricate problem. This paper mainly focus on the latter problem. Based on k-mediod clustering results, This work firstly proposes an cluster label extraction method to select candidate relevant clusters. Secondly, multiple ranking features are introduced to assist the related xml fragments identification from the candidate clusters. Top N fragments compose the high quality pseudo feedback set finally. Experimental results on standard INEX test data show that in one hand, the proposed cluster label extraction method could obtain proper cluster key terms and lead to appropriate candidate cluster selection. On the other hand, the presented ranking features are beneficial to the relevant xml fragments identification. The quality of feedback set is ensured. © 2013 Springer-Verlag.

关键词： Clustering algorithms

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JUFE at INEX 2011 Snippet Retrieval Track

JUFE at INEX 2011 Snippet Retrieval Track

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Lecture Notes in Computer Science

作者： Liu, Dexi Wan, Changxuan Liao, Guoqiong Zhong, Minjuan Liu, Xiping School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9783642357336

jiangxi University of Finance and Economics (JUFE) submitted 8 runs to the Snippet Retrieval Track at INEX *** report describes an XML snippet retrieval method based on Average Topic Generalization (ATG) model used by JUFE. The basic idea of the ATG is that different element in an XML document plays different role and hence should has distinguishing importance. The ATG model sets a weight automatically to each element according to its tag or path in the XML document. Then, the BM25EW model based on the ATG is proposed to retrieve and rank the relevant elements in an XML document collection. All windows in the most relevant elements are scored and those windows with higher scores are extracted as snippets. By comparing with the runs under different strategies, the performance are discussed and analyzed in detail. © Springer-Verlag Berlin Heidelberg 2012.

关键词： XML

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OEE-CFC: A dataset for Open Event Extraction from Chinese Financial Commentary

OEE-CFC: A Dataset for Open Event Extraction from Chinese Fi...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wan, Qizhi Wan, Changxuan Hu, Rong Liu, Dexi Xu, Wenwu Xu, Kang Zou, Meihua Liu, Tao Yang, Jie Xiong, Zhenwei School of Computer and Artificial Intelligence Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering China

ISBN: (纸本)9798891761681

To meet application needs, event extraction has shifted from simple entities to unconventional entities serving as event arguments. However, current corpora with unconventional entities as event arguments are limited in event types and lack rich multi-events and shared arguments. Financial commentary not only describes the basic elements of an event but also states the background, scope, manner, condition, result, and tool used for the event, as well as the tense, intensity, and emotions of actions or state changes. Therefore, it is not suitable to develop event types that include only a few specific roles, as these cannot comprehensively capture the event's semantics. Also, there are affluent complex entities serving as event arguments, multiple events, and shared event arguments. To advance the practicality of event extraction technology, this paper first develops a general open event template from the perspective of understanding the meaning of events, aiming to comprehensively reveal useful information about events. This template includes 21 event argument roles, divided into three categories: core event roles, situational event roles, and adverbial roles. Then, based on the constructed event template, Chinese financial commentaries are collected and manually annotated to create a corpus OEE-CFC supporting open event extraction. This corpus includes 17,469 events, 44,221 arguments, 3,644 complex arguments, and 5,898 shared arguments. Finally, based on the characteristics of OEE-CFC, we design four types of prompts, and two models for event argument extraction are developed, with experiments conducted on the prompts. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Indexing Future Trajectories of Moving Objects in a Constrained Network

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Journal of Computer Science & Technology 2007年第2期22卷 245-251页

作者：陈继东孟小峰 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China

Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the adaptive unit is not given by a single trajectory, but instead by two trajectory bounds based on different assumptions on the traffic conditions and obtained from the simulation. Our experiments, carried on two different datasets, show that the ANR-tree is essentially one order of magnitude more efficient than the TPR-tree, and is much more scalable.

关键词： database spatial database access methods moving objects

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