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Resource Allocation for Multi-Modal Semantic Communication in UAV Collaborative Networks

IEEE Transactions on communications 2025年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China Nanjing University of Posts and Telecommunications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Southeast University School of Cyber Science and Engineering Nanjing211189 China Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing210000 China Xidian University State Key Laboratory of Integrated Services Networks Xian710071 China Nanjing University of Posts and Telecommunications College of Communication and Information Engineering China Utah State University Department of Electrical and Computer Engineering LoganUT210003 United States

Semantic communication is envisioned as a potential communication paradigm enabled by artificial intelligence and is promising to break the Shannon limit for future 6G networks. This paradigm benefits unmanned aerial vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting task-relevant semantic information. However, resource allocation in the multiple collaborative UAV scenarios remains unexplored, particularly regarding multi-modal semantic communication. To tackle this challenge, this paper investigates a semantic-aware intelligent resource allocation method for multi-UAV-assisted semantic communication networks in the UAV image-sensing task-oriented scenario. A multi-modal semantic communication framework with multi-UAV relay collaboration is developed. At the semantic level, a novel quality of experience (QoE) and the transmission cost model are introduced, based on which a semantic-aware resource allocation problem is formulated, aiming to maximize QoE while minimizing the transmission cost by jointly optimizing the UAV trajectory, the spectrum bandwidth, the transmit power and the number of the transmitted semantic symbols. To deal with optimization challenges involving hybrid variables and coordination among UAVs, a multi-UAV hybrid decision-controlled deep reinforcement learning (DRL) scheme is proposed. Simulation results demonstrate the effectiveness of the proposed scheme compared with the benchmark schemes in achieving a good balance between the QoE and the transmission cost. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G

arXiv

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arXiv 2025年

作者： Zhang, Jiayi Liu, Ziheng Zhu, Yiyang Shi, Enyu Xu, Bokai Yuen, Chau Niyato, Dusit Debbah, Mérouane Jin, Shi Ai, Bo Shen, Xuemin School of Electronic and Information Engineering The Frontiers Science Center for Smart High-Speed Railway System Beijing jiaotong University Beijing100044 China School of Electrical and Electronics Engineering Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University 639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The introduction of intelligent interconnectivity between the physical and human worlds has attracted great attention for future sixth-generation (6G) networks, emphasizing massive capacity, ultra-low latency, and unparalleled reliability. Wireless distributed networks and multi-agent reinforcement learning (MARL), both of which have evolved from centralized paradigms, are two promising solutions for the great attention. Given their distinct capabilities, such as decentralization and collaborative mechanisms, integrating these two paradigms holds great promise for unleashing the full power of 6G, attracting significant research and development attention. This paper provides a comprehensive study on MARL-assisted wireless distributed networks for 6G. In particular, we introduce the basic mathematical background and evolution of wireless distributed networks and MARL, as well as demonstrate their interrelationships. Subsequently, we analyze different structures of wireless distributed networks from the perspectives of homogeneous and heterogeneous. Furthermore, we introduce the basic concepts of MARL and discuss two typical categories, including model-based and model-free. We then present critical challenges faced by MARL-assisted wireless distributed networks, providing important guidance and insights for actual implementation. We also explore an interplay between MARL-assisted wireless distributed networks and emerging techniques, such as information bottleneck and mirror learning, delivering in-depth analyses and application scenarios. Finally, we outline several compelling research directions for future MARL-assisted wireless distributed networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Dynamic partial FPGA reconfiguration in a prototype microprocessor system

Dynamic partial FPGA reconfiguration in a prototype micropro...

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17th International Conference on Field Programmable Logic and Applications

作者： Schleupen, Kai Lekuch, Scott Mannion, Ryan Guo, Zhi Najjar, Walid Vahid, Frank IBM System and Technology Group Department of Computer Science and Engineering University of California Riverside United States Brocade Communications System

ISBN: (纸本)9781424410590

Modem FPGAs' parallel computing capability and their ability to be reconfigured make them an ideal platform to build accelerators for supercomputing systems. As a multicore processor, the recently announced Cell Broadband EngineTM1 offers tremendous computing power. In this paper, we introduce a prototype system that combines these two types of computing devices together in a reconfigurable blade and we describe its architecture, memory system and abundant interfaces. On the reconfigurable blade it is desirable that the FPGA devices can be partially reconfigured at run-time. This paper presents the dynamic partial reconfiguration (DPR) technique and its design flow for the reconfigurable blade. We report our experimental results of the blade doing partial reconfiguration. DPR allows the reconfigurable blade to be a powerful, run-time changeable computing engine. A sample application is presented that was both simulated for the Cell processor and dynamically loaded to run on the FPGA.

关键词： Field programmable gate arrays (FPGA)

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Ultra-Wideband (UWB) geolocation in NLOS multipath fading environments

Ultra-Wideband (UWB) geolocation in NLOS multipath fading en...

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2005 13th IEEE International Conference on Networks jointly held with the 2005 7th IEEE Malaysia International Conference on communications

作者： Saeed, Rashid A. Khatun, Sabira Ali, Borhanuddin Mohd Khazani, Mohd A. Computer and Communications System Engineering Department Engineering Faculty Universiti Putra Malaysia 43400 Kuala Lumpur Malaysia

ISBN: (纸本)1424400007

Geolocation aware services are becoming attractive with the deployment of next generation wireless networks and broadband multimedia wireless networks (i.e. ubiquitous and pervasive computing) especially in indoor and campus areas. In order to construct an ad-hoc network the upcoming ultra-wideband (UWB) radio technology holds great promise for revolutionizing wireless communications especially in indoor environments (Office, Residential, Industrial). The two proposals considered in this paper are: a Multi Band OFDM approach, based on the transmission of non-impulse OFDM signals combined with Frequency Hopping (FH), and the direct-sequence (DS) UWB approach, based on impulse radio transmission of UWB DS-coded pulses. In this paper, the problem of ranging error estimation in obstructed line-of-sight (OLOS) multipath channels is investigated. A Time-of-Arrival (ToA) based ranging scheme using UWB radio link is proposed. Results show that the accuracy enhancement depends on two principal factors: the strength of multipath components and the variance of non-line-of-sight (NLOS) delays, which it shows that DS-UWB is best suited for ranging, due to its larger bandwidth and its higher frequencies of operation. © 2005 IEEE.

关键词： Telecommunication networks

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A QUALITATIVE MODEL OF THE HIV VITAL CYCLE 14

A QUALITATIVE MODEL OF THE HIV VITAL CYCLE

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14TH ANNUAL INTERNATIONAL CONF OF THE IEEE engineering IN MEDICINE AND BIOLOGY SOCIETY

作者： GIACOMINI, M RUGGIERO, C GAGLIO, S Dept. of Communications. Computer and System Science University of Genoa Italy Department of Electrical Engineering University of Palermo Italy

ISBN: (纸本)0780307852

Qualitative modelling is a recent artificial intelligence approach to physical system modelling. This approach has been successfully applied in several fields. On the basis of an analysis and qualitative modelling of cell growth, we reckon that a qualitative model of the vital cycle of HIV can be proposed , including the phases in which HIV can be attacked. The actions of antiviral drugs can also be qualitatively modelled, provided their action mechanism is known, even only in a broad sense.

关键词： Cell proliferation

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Comparative Study for Classification Methods to Predict and Detect Epilepsy Seizure 5

Comparative Study for Classification Methods to Predict and ...

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5th IEEE Middle East and Africa Conference on Biomedical engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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A non-traditional omnidirectional vision system with stereo capabilities for autonomous robots

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7th Congress of the Italian Association for Artificial Intelligence, AIIA 2001

作者： Adorni, Giovanni Bolognini, Luca Cagnoni, Stefano Mordonini, Monica Department of Communications Computers and System Science University of Genoa Department of Computer Engineering University of Parma Italy

ISBN: (纸本)3540426019

In this paper we describe a vision system based on the use of both an omnidirectional vision sensor and a standard CCD camera. This hybrid system is aimed at compensating for drawbacks of both sensors and at offering new opportunities deriving by their joint use. It can be used in several tasks, such as implementation of peripheral/foveal vision strategies, stereo vision, etc. The paper describes the device on which the vision system is based and its use as a stereo system for obstacle detection in a semi-structured environment, based on a perspective removal algorithm. © Springer-Verlag Berlin Heidelberg 2001.

关键词： CCD cameras

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Voice conversion using deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks 40

Voice conversion using deep Bidirectional Long Short-Term Me...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Sun, Lifa Kang, Shiyin Li, Kun Meng, Helen Human-Computer Communications Laboratory Department of System Engineering and Engineering Management Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781467369978

This paper investigates the use of Deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks (DBLSTM-RNNs) for voice conversion. Temporal correlations across speech frames are not directly modeled in frame-based methods using conventional Deep Neural Networks (DNNs), which results in a limited quality of the converted speech. To improve the naturalness and continuity of the speech output in voice conversion, we propose a sequence-based conversion method using DBLSTM-RNNs to model not only the frame-wised relationship between the source and the target voice, but also the long-range context-dependencies in the acoustic trajectory. Experiments show that DBLSTM-RNNs outperform DNNs where Mean Opinion Scores are 3.2 and 2.3 respectively. Also, DBLSTM-RNNs without dynamic features have better performance than DNNs with dynamic features. © 2015 IEEE.

关键词： bidirectional long short-term memory dynamic features recurrent neural networks voice conversion

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A two-level schema for detecting recognition errors 8

A two-level schema for detecting recognition errors

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8th International Conference on Spoken Language Processing, ICSLP 2004

作者： Zhou, Zheng-Yu Meng, Helen Human-Computer Communications Laboratory Department of System Engineering and Engineering Management Chinese University of Hong Kong Hong Kong Hong Kong

This paper proposes a two-level schema for the automatic detection of possible errors in speech recognition hypotheses. Given the recognition hypothesis of an utterance, the first level in our schema applies an utterance classifier (UC) to decide if the hypothesis is error-free or erroneous. In the latter case, the utterance is passed on to the second level in our schema for further processing. A word classifier (WC) is applied to each of the word hypotheses in the utterance to decide whether or not it is a misrecognition. Hence the two-level schema can locate error-containing regions in the recognition hypotheses. These are the target regions to which we can apply more sophisticated and expensive language models for error correction as a next step. We have developed UC and WC based on Support Vector Machines (SVM). Experiments on Mandarin Chinese speech recognition using the Speech-Lab-In-A-Box corpora showed that the UC has a detection error rate of 16.5% for misrecognized utterances;the WC has a detection error rate of 19.8% for erroneous word hypotheses;and the overall two-level schema can catch 44.5% of the erroneous word hypotheses.

关键词： Speech recognition

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