IEEE Transactions on Network Science and Engineering最新文献_第4页

Collective Decision-Making Over Nonlinear Potential Fields 非线性势场的集体决策

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-30 DOI: 10.1109/TNSE.2026.3659655

Mohammad Tuqan;Daniel Burbano

{"title":"Collective Decision-Making Over Nonlinear Potential Fields","authors":"Mohammad Tuqan;Daniel Burbano","doi":"10.1109/TNSE.2026.3659655","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3659655","url":null,"abstract":"Collective decision-making is a fundamental cognitive process through which groups coordinate actions and resolve competing alternatives. It plays a vital role across domains ranging from animal behavior and human societies to robotics, cybersecurity, and urban systems. Motivated by recent findings in fish schools, where individuals switch between cohesive and exploratory behaviors based on a nonlinear decision landscape, we propose a model of collective decision-making in which each agent evolves according to gradient flow over a bistable potential. Grounded in statistical mechanics, the model employs a double-well energy landscape, a canonical structure that captures transitions between metastable states and naturally encodes the competition between two discrete options, enabling the study of consensus formation or polarization. Unlike classical opinion dynamics models, which describe the continuous evolution of beliefs, our framework assumes that agents possess intrinsic discrete decision states and focuses on transitions between them due to social interactions. Using Lyapunov analysis and spectral graph theory, we derive explicit forward-invariant sublevel sets around each decision equilibrium, yielding closed-form estimates of the basins of attraction and revealing how intrinsic agent dynamics and network topology jointly govern convergence to a shared decision. We demonstrate the effectiveness of our framework through both synthetic simulations and real-world case studies, including collective strike decisions in the Kapferer Tailor Shop network and intermittent navigation behavior in fish schools.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6556-6570"},"PeriodicalIF":7.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Confidence Based Asynchronous Integrated Communication and Localization Networks Using IR-UWB Signals 基于信誉度的异步集成通信和使用IR-UWB信号的定位网络

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-28 DOI: 10.1109/TNSE.2026.3658887

Fan Liu;Bofeng Zheng;Jikai Yin;Yachuan Bao;Tingting Zhang;Qinyu Zhang

{"title":"Confidence Based Asynchronous Integrated Communication and Localization Networks Using IR-UWB Signals","authors":"Fan Liu;Bofeng Zheng;Jikai Yin;Yachuan Bao;Tingting Zhang;Qinyu Zhang","doi":"10.1109/TNSE.2026.3658887","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3658887","url":null,"abstract":"Driven by increasing demands for integrated communication and localization (ICL), ultra-wideband (UWB) technology has attracted significant attention due to its high temporal resolution and strong resilience to interference. This paper proposes an asynchronous impulse radio UWB downlink network that enables ICL using a unified waveform, eliminating the need for pilot signals or pulse-level synchronization. A differential symbol detection strategy is developed, incorporating a symbol confidence model with an optimized threshold to improve demodulation and localization reliability. Building on this framework, a low-complexity confidence-weighted clock offset estimation algorithm is developed to address timing drift. Additionally, a robust frame synchronization model based on start-of-frame delimiter detection is introduced for frame header detection. Simulation results validate the capability of the system to support reliable data demodulation and localization simultaneously. Extensive real-world experiments are conducted using a commercial UWB platform with static anchors and a mobile agent. With clock calibration and anchor selection, the system achieves localization accuracy within 10 cm in 90% of cases.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6790-6808"},"PeriodicalIF":7.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-Empowered Smart Contract Vulnerability Detection for Decentralized Blockchain Systems 基于ai的分布式区块链系统智能合约漏洞检测

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-28 DOI: 10.1109/TNSE.2026.3658785

Chi Jiang;Caixing Shao;Xiaoyan Huang;Ming Tao;Yin Zhang;Yan Zhang

{"title":"AI-Empowered Smart Contract Vulnerability Detection for Decentralized Blockchain Systems","authors":"Chi Jiang;Caixing Shao;Xiaoyan Huang;Ming Tao;Yin Zhang;Yan Zhang","doi":"10.1109/TNSE.2026.3658785","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3658785","url":null,"abstract":"Blockchain technology is evolving toward next-generation blockchain systems, where AI-empowered optimization plays a key role in enhancing scalability, efficiency, and security. However, in the context of smart contract vulnerability detection, however, most vulnerabilities originate from a few critical code fragments, resulting in a severe signal-to-noise imbalance. This imbalance introduces representation noise and redundant information in complex contract code, obscuring the true vulnerability semantics and hindering the effectiveness of AI-based models such as graph neural networks (GNNs). To address this challenge, we propose CoTA, a feature augmentation framework that leverages two easily obtainable signals: code-cohort feature and task-shared feature. Code-cohort features capture the observation that contracts with similar structural/behavioral patterns often implement similar business functions, and thus share vulnerability priors (e.g., higher reentrancy risk in fund-transfer-heavy cohorts). Task-shared feature, on the other hand, learned via a multi-task setup that jointly trains coarse contract-level multi-label classification and fine-grained function-level labeling to distill shared representations. Extensive experiments on real-world dataset show that CoTA achieves consistent improvements in detection accuracy, offering a practical and effective solution for smart contract security analysis.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6488-6502"},"PeriodicalIF":7.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Movable Antenna-Enabled Secure Transmission for Active RIS-Aided ISAC Systems 主动ris辅助ISAC系统的可移动天线安全传输

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-28 DOI: 10.1109/TNSE.2026.3657852

Hongyi Bian;Feilong Zhao;Minghao Chen;Lingling Liu;Yu Yao;Hao Jiang;Feng Shu

{"title":"Movable Antenna-Enabled Secure Transmission for Active RIS-Aided ISAC Systems","authors":"Hongyi Bian;Feilong Zhao;Minghao Chen;Lingling Liu;Yu Yao;Hao Jiang;Feng Shu","doi":"10.1109/TNSE.2026.3657852","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3657852","url":null,"abstract":"The integrated sensing and communication (ISAC) technology empowered by movable antennas (MAs) has increased the possibility of wireless communication development. In this paper, we investigate a discrete MA-enabled active reconfigurable intelligent surface (RIS)-aided ISAC system. In this system, the position of discrete MAs can be dynamically adjusted to enhance channel conditions, thereby improving the communication security of legitimate users against potential eavesdropping risks from target. In particular, we formulate an optimization problem for the max-min secrecy rate (SR) by jointly optimizing beamforming matrix, phase shift matrix and MA discrete positions. An efficient alternating optimization (AO) based on penalty-successive convex approximation (SCA) and penalty-dual-decomposition (PDD) approach is proposed to obtain a high-quality initial solution for beamforming matrix and phase shift in the non-convex problem. Next, we propose a dueling double deep Q-Network (D3QN)-twin delayed and deep deterministic policy gradient (TD3) method to cope with the hybrid action space issue of the non-convex problem with highly coupled variables. Numerical results demonstrate that the introduction of MAs can effectively improve SR performance compared to fixed-position antenna (FPA) structure in RIS-aided ISAC systems. In addition, the proposed AO-based D3QN-TD3 method validate the suitability and superiority compared to the D3QN-TD3 method without AO algorithm.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6325-6344"},"PeriodicalIF":7.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Two-Layer UAV Spoofing Detection Based on Multi-Source/Multi-Dimensional Data Analysis for Low-Altitude Networks 基于多源多维数据分析的低空网络无人机两层鲁棒欺骗检测

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-27 DOI: 10.1109/TNSE.2026.3658496

Liangbo Xie;Ting Xiao;Mu Zhou;Wei Nie;Xiaolong Yang

{"title":"Robust Two-Layer UAV Spoofing Detection Based on Multi-Source/Multi-Dimensional Data Analysis for Low-Altitude Networks","authors":"Liangbo Xie;Ting Xiao;Mu Zhou;Wei Nie;Xiaolong Yang","doi":"10.1109/TNSE.2026.3658496","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3658496","url":null,"abstract":"The navigation safety of Unmanned Aerial Vehicles (UAVs) is crucial for the stability and reliability of low-altitude networks. However, the Global Navigation Satellite System (GNSS) they depend on is vulnerable to spoofing, becoming a key challenge to low-altitude network security. Existing detection methods typically focus on single attack types or single-sensor data, which limits effectiveness against hybrid spoofing. Therefore, we propose a multi-source, multi-dimensional data analysis-based spoofing detection model, combining complementarity of GNSS and Inertial Measurement Unit (IMU) to conduct a two-layer detection. By employing a sliding window mechanism, the first layer performs rapid threshold detection by leveraging statistical features differences between spoofed and genuine signal data, while the second layer conducts a refined position-attitude consistency check if the first layer reports no spoofing. When no spoofing is detected by two layers, GNSS data is utilized to correct IMU drift, forming a closed-loop “detection-correction” system that enhances long term consistency. Monte Carlo simulations demonstrate that the proposed method effectively detects both step and ramp hybrid spoofing attacks. Under attacks with a maximum spoofing offset of <inline-formula><tex-math>$ d_{text{max}}=9;text{m}$</tex-math></inline-formula>, the detection probability exceeds 95%, false alarm rate remains below 3%, and average detection time is reduced by 57% compared to direct-comparison and Kalman filter innovation-based approaches.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6737-6753"},"PeriodicalIF":7.9,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RF-Based Identification Framework Against Unauthorized UAV Networking in Low-Altitude Economy 低空经济中基于射频识别的无人机组网识别框架

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-27 DOI: 10.1109/TNSE.2026.3658563

Yuquan Xiao;Qinghe Du;Zixiao Zhao;Bin Li;Xiao Tang;Shijiao Zhang;Houbing Song

{"title":"RF-Based Identification Framework Against Unauthorized UAV Networking in Low-Altitude Economy","authors":"Yuquan Xiao;Qinghe Du;Zixiao Zhao;Bin Li;Xiao Tang;Shijiao Zhang;Houbing Song","doi":"10.1109/TNSE.2026.3658563","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3658563","url":null,"abstract":"As uncrewed aerial vehicles (UAV) become increasingly prevalent for the low-altitude economy, security concerns are raised such as unauthorized access and control, collisions and interference with human aircraft, and illegal eavesdropping. Therefore, ensuring secure UAV networking holds significant implications over low-altitude wireless networks. In this paper, we first conduct a comprehensive review focusing on the security challenges and solutions of low-altitude UAV networks. Then, we introduce a radio frequency (RF)-based UAV identification framework to accurately detect and identify different UAVs, which is significant for supporting secure access and authentication in UAV networks. Specifically, we utilize short-time fourier transform (STFT) to extract two-dimensional features from the raw signals, which contain both time-domain and frequency-domain information and can be used to recognize the type and flight mode of UAVs. With the extracted two-dimensional features, we employ a convolutional neural network built with depth wise separable convolution (DSC) to achieve multi-class classifications. Based on a real drone dataset and comparisons against baselines, our experiments show that the proposed DSC-STFT can achieve higher accuracy in different classification tasks with lower computational complexity. Additionally, it also holds good robustness on different types and signal-noise ratio (SNR) levels of noisy data.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6538-6555"},"PeriodicalIF":7.9,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning for Velocity and Range Estimation of Drones in Urban Occlusion Environments 城市遮挡环境下无人机速度和距离估计的深度学习

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-27 DOI: 10.1109/TNSE.2026.3658147

Shuai Xu;Weijie Yuan;Yunlong Cai

{"title":"Deep Learning for Velocity and Range Estimation of Drones in Urban Occlusion Environments","authors":"Shuai Xu;Weijie Yuan;Yunlong Cai","doi":"10.1109/TNSE.2026.3658147","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3658147","url":null,"abstract":"This paper investigates the challenge of radar echo loss caused by urban occlusions in low-altitude aerial drone surveillance. We propose a deep learning framework, built on an enhanced U-Net, to restore radar signals and improve joint velocity and range estimation. A Slow-Fast Time Echo Matrix (SFTEM) is constructed to encode temporal and phase features, serving as the input to the network. Matched filtering is first applied to form the SFTEM as the network input, followed by Doppler FFT on the restored SFTEM for range–velocity estimation. To address severe pulse loss, the model incorporates axial attention and residual connections, while a Partial-Edge Preserved Pulse Loss (PEPPL) model is introduced to simulate realistic urban blockages. Experimental results demonstrate a mean range error of about <inline-formula><tex-math>$ 2.08; {rm m}$</tex-math></inline-formula>, which is well below the range resolution (5–15 m), and a velocity error of <inline-formula><tex-math>$0.749,mathrm{m/s}$</tex-math></inline-formula>, even under conditions with over 90% central pulse loss, highlighting the robustness and effectiveness of the proposed approach for aerial drone sensing in complex urban environments.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6453-6469"},"PeriodicalIF":7.9,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Importance Ranking in Complex Networks via Influence-Aware Causal Node Embedding 基于影响感知因果节点嵌入的复杂网络重要性排序

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-27 DOI: 10.1109/TNSE.2026.3658546

Jiahui Gao;Kuang Zhou;Yuchen Zhu;Keyu Wu

{"title":"Importance Ranking in Complex Networks via Influence-Aware Causal Node Embedding","authors":"Jiahui Gao;Kuang Zhou;Yuchen Zhu;Keyu Wu","doi":"10.1109/TNSE.2026.3658546","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3658546","url":null,"abstract":"Understanding and quantifying node importance is a fundamental problem in network science and engineering, underpinning a wide range of applications such as influence maximization, social recommendation, and network dismantling. Prior research often relies on centrality measures or advanced graph embedding techniques using structural information, followed by downstream classification or regression tasks to identify critical nodes. However, these approaches typically decouple node representation learning from the ranking objective and depend heavily on the topological structure of target networks, leading to feature–task inconsistency and poor cross-network generalization. This paper proposes a novel framework that leverages causal representation learning to obtain robust and invariant node embeddings for cross-network ranking tasks. Specifically, we introduce an influence-aware causal node embedding module within an autoencoder architecture to extract node embeddings that are causally related to node importance. Furthermore, we design a unified optimization framework incorporating a causal ranking loss that jointly optimizes reconstruction and ranking objectives, thereby enabling mutual reinforcement between node representation learning and ranking optimization. This design allows the proposed model to be trained on synthetic networks and to generalize effectively across diverse real-world networks. Extensive experiments on multiple benchmark datasets demonstrate that the proposed model consistently outperforms state-of-the-art baselines in terms of both ranking accuracy and cross-network transferability, offering new insights for network analysis and engineering applications—particularly in scenarios where the target network’s structure is inaccessible in advance due to privacy or security constraints.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6754-6771"},"PeriodicalIF":7.9,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications 生成扩散接收机：实现导频高效MIMO-OFDM通信

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-26 DOI: 10.1109/TNSE.2026.3657967

Yuzhi Yang;Omar Alhussein;Atefeh Hajijamali Arani;Zhaoyang Zhang;Mérouane Debbah

{"title":"Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications","authors":"Yuzhi Yang;Omar Alhussein;Atefeh Hajijamali Arani;Zhaoyang Zhang;Mérouane Debbah","doi":"10.1109/TNSE.2026.3657967","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3657967","url":null,"abstract":"This paper focuses on wireless multiple-input multiple-output (MIMO)-orthogonal frequency division multiplex (OFDM) receivers. Traditional wireless receivers have relied on mathematical modeling and Bayesian inference, achieving remarkable success in most areas but falling short in their ability to characterize channel matrices. Neural networks (NNs) have demonstrated significant potential in this aspect. Nevertheless, integrating traditional inference methods with NNs presents challenges, particularly in tracking the error progression. Given the inevitable presence of noise in wireless systems, generative models that are more resilient to noise are garnering increased attention. In this paper, we propose re-evaluating the MIMO-OFDM receiver using diffusion models, which is a common generative approach. With diffusion models, we can effectively leverage prior knowledge of channel matrices and incorporate traditional signal estimation components. Specifically, we explore the diffusion system and introduce an imagination-screening strategy to guide the denoising process. Furthermore, diffusion models enable adaptation to varying noise levels and pilot schemes using the same NN, significantly reducing training and deployment costs. Simulated results reveal that, for pilot densities ranging from 4–6 pilots per 64-subcarrier block and signal-to-noise ratios (SNRs) from -4 dB to 0 dB, our proposed receiver reduces channel-reconstruction error by up to two times compared to leading deep-learning models, with the most pronounced improvements observed in low-pilot conditions. Additionally, performance enhancements can be achieved with a larger imagination size, despite increased computational complexity.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6721-6736"},"PeriodicalIF":7.9,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11363381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Reinforcement Learning-Based Joint Resource Allocation and Trajectory Design for UAV-Assisted Multi-Cell ISAC Systems 基于深度强化学习的无人机辅助多单元ISAC系统联合资源分配与轨迹设计

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-23 DOI: 10.1109/TNSE.2026.3657390

Haiyong Zeng;Xuan Lu;Yuanyan Huang;Shoulin Huang;Shaohan Feng;Xu Zhu;Jie Cao

{"title":"Deep Reinforcement Learning-Based Joint Resource Allocation and Trajectory Design for UAV-Assisted Multi-Cell ISAC Systems","authors":"Haiyong Zeng;Xuan Lu;Yuanyan Huang;Shoulin Huang;Shaohan Feng;Xu Zhu;Jie Cao","doi":"10.1109/TNSE.2026.3657390","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3657390","url":null,"abstract":"The rapid development of multi-cell integrated sensing and communication (ISAC) systems requires new architectural and algorithmic solutions to tackle two key challenges: severe inter-cell interference that degrades communication quality for edge users, and the difficulty of maintaining reliable sensing performance in dynamic environments. This paper proposes an uncrewed aerial vehicle (UAV)-assisted multi-cell ISAC framework. Therein, the UAV acts as a flying base station to support terrestrial base stations in jointly satisfying users' communication and sensing requirements. To minimize the total transmit power while ensuring communication quality and sensing accuracy, a bi-level deep deterministic policy gradient-based joint resource optimization (DDPG-JRO) algorithm is developed. In the outer level, UAV trajectory optimization is formulated as a Markov decision process, where the trajectory decision for each time slot is updated through rewards from the inner level. In the inner level, user scheduling is determined based on the serving channel gain principle, and optimal power allocation is obtained through semidefinite relaxation and Gaussian randomization. The obtained inner-level solution is incorporated into the outer-level reward, thereby coupling power control, scheduling, and trajectory design within a unified learning framework. Numerical results validate the superiority and efficiency of the proposed DDPG-JRO algorithm.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6383-6401"},"PeriodicalIF":7.9,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0