IEEE Transactions on Cognitive Communications and Networking最新文献

{"title":"Multi-Oriented Open Set Adversarial Attacks to Automatic Modulation Classification","authors":"Yandie Yang;Sicheng Zhang;Kuixian Li;Yun Lin","doi":"10.1109/TCCN.2026.3661500","DOIUrl":"10.1109/TCCN.2026.3661500","url":null,"abstract":"Automatic Modulation Classification (AMC) plays a crucial role in spectrum monitoring and communication security. Open Set Recognition (OSR) methods have been widely applied to AMC tasks to address the challenge of recognizing known modulation types and identifying unknown modulation signals in open-world scenarios. However, existing research primarily emphasize the accuracy of open set recognition while overlooking potential security threats posed by adversarial attacks. To address this gap, we investigate the security vulnerabilities of AMC methods under adversarial attacks in open electromagnetic environments from the perspective of artificial intelligence security. We propose two types of multi-oriented open set adversarial attacks, including Label-oriented Open Set Adversarial Attacks (OSLoA) and Feature-oriented Open Set Adversarial Attack (OSFoA). Based on the discrimination mechanism of the OSR model, we propose the OSLoA method. This method increases the confidence of misclassification for unknown signals, which causes them to be recognized as known classes. Additionally, we introduce the innovative OSFoA method. It reduces the distance between the class activation features of signals from unknown classes and those of the known classes into which unknown signals are most likely to be misclassified. As a result, the unknown classes are pushed closer to the known classes in the feature space, further enhancing the attack effectiveness. Notably, during the computation of class activation features, only those features that make positive contributions to the prediction output are retained. Comprehensive experiments were conducted on both public and real-world datasets. The results demonstrate that the proposed OSLoA and OSFoA methods achieve excellent performance and further reveal the vulnerability of AMC methods to open adversarial security threats.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6281-6292"},"PeriodicalIF":7.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trajectory Optimization for Data Collection in Hybrid UAV and UGV Low-Altitude Economy Network","authors":"Tao Huang;Gengyuan Lu;Mingan Luan;Zheng Chang;Ying-Chang Liang","doi":"10.1109/TCCN.2026.3661515","DOIUrl":"10.1109/TCCN.2026.3661515","url":null,"abstract":"The rapid evolution of unmanned aerial vehicle (UAV) technology has given rise to the new concept of the “low-altitude economy (LAE)” and promoted innovation in various application fields. However, the efficiency of low-altitude services is still significantly constrained by the energy consumption and payload limitations of UAVs. Accordingly, in this paper, we propose a hybrid UAV and unmanned ground vehicle (UGV) LAE framework to address the limitation in UAV-based LAE networks. Specifically, in virtue of the advantages of UGVs in advanced communication, large batteries, and computation modules, the hybrid mode can provide an additional degree of freedom for environmental information collection in LAE networks. Nevertheless, for the considered hybrid framework, which involves the collaborative optimization of numerous agents across multiple dimensions, traditional methods are inefficient and difficult to implement effectively. Hence, we present a machine learning (ML)-based method to handle this issue. More in detail, we first design an iterative self-organizing data analysis techniques strategy to form sensing nodes (SNs) clusters, where the cluster heads (CHs) collect data from SNs in advance in order to reduce the transmission delay. Then, a multi-UGV Hamiltonian trajectory planning algorithm is involved to design the trajectory of the UGVs. After the data has been transmitted from the CHs to UGVs, the UAVs are used to collect data from the UGVs and transmit the data to the remote base station. Correspondingly, we propose a multi-agent deep reinforcement learning algorithm using multi-agent deep deterministic policy gradient to design the trajectory of the UAVs. The simulation results demonstrate the effectiveness and advantages of our proposed hybrid UAV-UGV strategy and the ML-based algorithm.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6031-6044"},"PeriodicalIF":7.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TACM-MR: Topographically Augmented Channel Model Multi-Receiver Dataset","authors":"Kenneth L. Witham;Nishanth Marer Prabhu;Marius Necsoiu;Chad M. Spooner;Gunar Schirner","doi":"10.1109/TCCN.2026.3661494","DOIUrl":"10.1109/TCCN.2026.3661494","url":null,"abstract":"Electromagnetic spectrum congestion requires new methods to recognize and understand other transmitters for effective sharing. Existing Automatic Modulation Recognition (AMR) research utilizes datasets for single channel (SISO) or collocated systems (MIMO) with simplistic propagation models, lacking realistic propagation channels and the spatial diversity of distributed multi-receiver scenarios. We propose the Topographically Augmented Channel Model 2026.1 (TACM2026.1), a spatially-separated but contemporaneous multi-receiver dataset using propagation channels derived from simulated topographies. We modify six contemporary AMR Machine Learning (ML) models to process multiple concurrent receivers. To accommodate variations in signal occupied bandwidth, we identify the <inline-formula> <tex-math>$E_{s}/N_{0}$ </tex-math></inline-formula> metric for signal quality and the <inline-formula> <tex-math>$sum E_{s}/N_{0}$ </tex-math></inline-formula> metric to quantify aggregate signal quality. Our systematic evaluation demonstrates that classification performance depends on aggregate received power (<inline-formula> <tex-math>$sum E_{s}/N_{0}$ </tex-math></inline-formula>) rather than its distribution across receivers. The Iso-Power configuration, equalizing power across receivers, demonstrates models genuinely aggregate spatial diversity rather than simply selecting dominant receivers. This learned diversity combining is comparable (1.72% difference at <inline-formula> <tex-math>$E_{s}/N_{0}=50text {dB}$ </tex-math></inline-formula> with eight receivers) to the optimal diversity combination (MRC)) but requires no channel state information. Practically, eight-receiver systems achieve 75% accuracy at <inline-formula> <tex-math>$1.26times $ </tex-math></inline-formula> the range of single-receiver systems (4km <inline-formula> <tex-math>$rightarrow ~5$ </tex-math></inline-formula>km), enabling distributed spectrum sensing without complex infrastructure and channel information.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6466-6477"},"PeriodicalIF":7.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agentic AI-Driven Federated Feature Distillation for Adaptive Resource–Performance Tradeoffs in Wireless Edge Networks","authors":"Pengchao Han;Zhenshuai Yin;Chang Liu;Yi Fang","doi":"10.1109/TCCN.2026.3661503","DOIUrl":"10.1109/TCCN.2026.3661503","url":null,"abstract":"Federated learning (FL) has emerged as a key enabler of collaborative intelligence in wireless edge networks by allowing distributed clients to train models without sharing raw data. However, FL faces significant challenges, including high communication costs, a rigid framework, and privacy concerns, as it shares local model parameters with a homogeneous architecture across clients. Federated knowledge distillation (FedKD) addresses these issues by allowing collaboration among clients with black-box heterogeneous model architectures. However, FedKD often suffers from limited model performance due to the restricted scope of shared knowledge, i.e., model outputs. In this paper, we propose a novel Federated feature distillation (FFD) framework with a guaranteed convergence bound to enhance FedKD by enabling knowledge distillation among clients’ intermediate features. However, transmitting intermediate features and performing feature distillation introduce additional communication and computation overheads, which is difficult to optimize due to the dynamic and stochastic characteristics of model training. To address this, we embrace agentic artificial intelligence (AI) paradigm and propose an actor-critic reinforcement learning algorithm that adaptively selects and weights features for distillation. Extensive experiments across various datasets demonstrate that our proposed algorithm achieves superior model accuracy while significantly reducing resource costs compared to baseline approaches, highlighting the potential of integrating agentic AI with FL to enable efficient and intelligent collaboration in wireless edge networks.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6076-6088"},"PeriodicalIF":7.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CPFL: A Cluster-Based Parallel Blockchain Scheme for Secure and Efficient Federated Learning in Intelligent Connected Vehicles","authors":"Qi Qi;Yan Lei;Yixin Li;Hongyi Li;Qi Zhou","doi":"10.1109/TCCN.2026.3661499","DOIUrl":"10.1109/TCCN.2026.3661499","url":null,"abstract":"The rapid evolution of Intelligent Connected Vehicles (ICVs) has transformed automobiles into data-rich edge nodes, enabled by progress in artificial intelligence (AI), federated learning (FL), and 5G-enabled communication. While FL facilitates privacy-preserving model training by retaining data locally, its practical deployment in ICVs faces two key challenges: vulnerability to malicious parameter updates and degraded convergence under non-independent and identically distributed (non-IID) data. To address these issues, this paper proposes a secure and efficient cluster-based blockchain scheme for Byzantine resilience and model synchronization of FL. The scheme introduces a mainchain for periodic global model aggregation and multiple subchains that coordinate localized training. These subchains dynamically group vehicles into clusters based on the similarity of their model parameters, which is quantified using the Wasserstein distance. This clustering approach effectively handles non-IID data by ensuring that similar nodes train together. Each subchain employs a hybrid consensus protocol, combining stake-weighted validator election with the Byzantine fault-tolerant HotStuff BFT consensus, to robustly validate local models. Malicious updates are filtered using a combination of distance-based aggregation and gradient thresholding. Furthermore, a dynamic reputation mechanism incentivizes reliable participation through token staking and behavior-based rewards. Extensive experiments on MNIST and CIFAR-10 datasets demonstrate our scheme’s superiority over traditional FL methods, particularly in mitigating the impacts of non-IID data and Byzantine attacks.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"5966-5982"},"PeriodicalIF":7.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Design for RIS-Assisted Over-the-Air Federated Learning With Imperfect Cascaded CSI","authors":"Qiaochu An;Hongbin Zhu;Qiang Ye;Ning Zhang;Yuanming Shi;Yong Zhou","doi":"10.1109/TCCN.2026.3661518","DOIUrl":"10.1109/TCCN.2026.3661518","url":null,"abstract":"Over-the-air computation (AirComp) enables spectral-efficient global model aggregation for federated learning (FL) by supporting concurrent transmission and harnessing co-channel interference. However, unfavorable channel conditions and inaccurate channel estimation are two performance-limiting factors of AirComp-assisted FL. In this paper, we leverage reconfigurable intelligent surface (RIS) to assist AirComp for gradient aggregation with imperfect cascaded channel state information (CSI), taking into account both the expectation-based and worst-case error models (i.e., stochastic and deterministic models). Guided by the convergence analysis, we minimize the gradient aggregation distortion measured by the mean-squared-error (MSE), taking into account the unit modulus constraints of RIS phase-shifts. To alleviate the performance degradation due to imperfect channel estimation, we propose two robust algorithms to optimize the receive beamforming at the edge server, RIS phase-shifts, and transmit power at the edge devices. Both algorithms are designed under an alternating optimization framework, where Schur’s complement and penalty convex–concave procedure are adopted for the worst-case error model, and Lagrange duality and difference-of-convex programming are used for the expectation-based error model. Simulations are conducted to validate the learning performance superiority of the proposed algorithms over baseline schemes, inducing the robustness against CSI errors.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6045-6060"},"PeriodicalIF":7.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proactive Handover Management in 6G Low-Altitude Economy Networks for Aerial Vehicles Using Artificial General Intelligence","authors":"Shikhar Verma;Sayantan Bose;Mostafa M. Fouda;Zubair Md Fadlullah;Diptendu Sinha Roy","doi":"10.1109/TCCN.2026.3660763","DOIUrl":"10.1109/TCCN.2026.3660763","url":null,"abstract":"The rise of unmanned aerial vehicles (UAVs) and electric vertical take-off and landing (eVTOL) aircraft is accelerating the growth of the low-altitude economy (LAE), enabling mobility beyond conventional ground-based transport. However, aerial vehicles or flying vehicles (FVs) in LAE environments face significant communication challenges due to high mobility, frequent handovers between base stations (BSs), and the susceptibility of mmWave bands to blockage and path loss. Reactive handover mechanisms—triggered only after link degradation—often lead to disconnections and degraded service quality, particularly in dense urban areas. Moreover, uneven FV distribution can cause BS load imbalances, further compromising quality of service (QoS). To address these challenges, we propose a proactive BS association framework for intelligent handover management using artificial general intelligence (AGI). Our approach leverages deep learning to jointly predict future received signal strength indicator (RSSI) and BS load, enabling an autonomous decision algorithm to select optimal BSs for stable, high-throughput connectivity while minimizing unnecessary handovers. Simulation results demonstrate that the proposed joint prediction-based strategy significantly reduces handover frequency and improves average throughput compared to reactive, single-metric baselines and two additional benchmark predictors introduced for extended evaluation. These findings underscore the potential of predictive, AGI-driven mobility management to enhance the stability and performance of communication networks in the emerging LAE ecosystem.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"5953-5965"},"PeriodicalIF":7.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coordinated Rate-Splitting Multiple Access for Emergency UAV-Enabled Integrated Sensing and Communication","authors":"Xinyi Wei;Ruoguang Li;Yingyang Chen;Lianming Xu;Li Wang;Zhu Han","doi":"10.1109/TCCN.2026.3660777","DOIUrl":"10.1109/TCCN.2026.3660777","url":null,"abstract":"In disaster scenarios, infrastructure damage and wireless resource scarcity pose significant challenges for providing prompt and reliable communication and sensing (C&S) services. Recently, unmanned aerial vehicle (UAV) enabled integrated sensing and communication (ISAC) has emerged as a promising technique to tackle the above issues by leveraging flexibility and mobility of multiple UAVs to offer high-quality and cost-efficient C&S services. In parallel, rate-splitting multiple access (RSMA) facilitates customized transmission by partitioning messages into private and common parts with adjustable rates, thereby making it well-suited for on-demand data transmission in disaster scenarios. In this paper, we propose a framework that utilizes coordinated RSMA for ISAC (Coordinated RSMA-ISAC) in an emergency UAV system. This framework enables multiple transmit UAVs to simultaneously communicate with several communication survivors (CSs) and detect a potentially trapped survivor (TS) in a coordinated manner with imperfect channel state information (CSI). In addition, an optimization problem is formulated to jointly optimize the UAV-CS association, UAV deployment, and transmit beamforming to maximize the weighted sum rate (WSR) of the system, subject to the sensing signal-to-noise ratio (SNR) requirement. To efficiently solve such a mixed-integer non-convex programming (MINCP) problem, an iterative algorithm is proposed by applying the generalized Benders decomposition (GBD) technique. Specifically, the original problem is decoupled into a master problem for pure integer programming and a primal problem for non-convex programming. Then, we further use successive convex approximation (SCA), semi-definite relaxation (SDR), and cutting-plane techniques to solve the decoupled problems. Simulation results verify the effectiveness of the proposed algorithm, and demonstrate that the coordinated RSMA-ISAC framework outperforms conventional space division multiple access (SDMA), non-orthogonal multiple access (NOMA), and orthogonal multiple access (OMA) in terms of both C&S performance.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"5999-6015"},"PeriodicalIF":7.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WiDeFus: A Wi-Fi-Based Lightweight Human Activity Recognition via CSI Component Decomposition and Adaptive Feature Fusion","authors":"Xingcan Chen;Chenglin Li;Wei Meng;Wendong Xiao","doi":"10.1109/TCCN.2026.3660764","DOIUrl":"10.1109/TCCN.2026.3660764","url":null,"abstract":"WiFi channel state information (CSI)-based human activity recognition (HAR) approaches face some fundamental limitations, such as high computational cost of the model due to irrelevant signal components obscure human-related features, position-sensitive representations cause activity misclassification under spatial variance, and environmental heterogeneity induces domain shifts that degrade generalization. To overcome these challenges, we propose a lightweight HAR approach based on WiFi CSI component decomposition and triple feature fusion (WiDeFus). Specifically, WiDeFus first isolates human-related components via quantum-inspired sparse decomposition, and leverage Hermite-Gaussian bases with group-element sparsity constraints to eliminate dynamic interference and noises. WiDeFus then introduces a triple-feature adaptive fusion network to achieve dynamic frequency-domain selection, automatically extract temporal features, and perform environment-robust calibration. These purified features are processed by a dendrite net (DD) that replaces nonlinear activations with multiplicative interactions for efficient inference. Experimental results show that WiDeFus is a lightweight HAR approach with high recognition accuracy and satisfactory cross-domain generalization performance.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6237-6246"},"PeriodicalIF":7.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"System Utility Function Optimization-Based Flight Trajectory and Resource Allocation for UAV-Assisted Integrated Sensing and Communication Systems","authors":"Zida Guo;Rong Chai;Ruijin Sun;Chengchao Liang;Qianbin Chen","doi":"10.1109/TCCN.2026.3660231","DOIUrl":"10.1109/TCCN.2026.3660231","url":null,"abstract":"In this paper, we consider an uncrewed aerial vehicle-assisted integrated sensing and communication system, consisting of an aerial base station (ABS) and a mobile radar (MR) equipped with a radar receiver. The ABS hovers at a specific position to provide communication services to user clusters, while the MR flies along a predefined trajectory to receive communication reflection signals for target sensing. By taking into account user communication and target sensing performance, as well as flight energy consumption of the MR, the system utility function is modeled as a weighted sum of the minimum average rate of user clusters, sensing channel gain, and flight energy consumption of the MR. The joint communication precoding design, ABS deployment, MR trajectory planning, and communication sensing scheduling problem is modeled as a constrained system utility function maximization problem. Given that the modeled optimization problem is a highly coupled and non-convex mixed-integer optimization problem, it is challenging to solve directly. To tackle this problem, we decompose the original problem into four subproblems and sequentially solve each subproblem using an alternating iteration algorithm. Specifically, for the communication precoding design subproblem, the zero-forcing algorithm is used to eliminate the interference among users and the original problem is transformed into a semi-definite programming problem. For the ABS deployment subproblem and the MR trajectory planning subproblem, the Taylor expansion and the successive convex approximation are employed and slack variables are introduced to convert the original problems into convex optimization problems. For the communication sensing scheduling subproblem, the variable relaxation method is adopted to relax the binary variables into continuous variables, and the optimization tool is used to obtain the solution. Then, two heuristic algorithms are proposed to restore the communication and sensing scheduling variables. Finally, the effectiveness of the proposed algorithms is verified through simulations.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6331-6343"},"PeriodicalIF":7.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}