IEEE Transactions on Machine Learning in Communications and Networking最新文献_第4页

Reinforcement Learning-Driven Honeypots: Tactic-Based Defense for Industrial Control Systems 强化学习驱动蜜罐：基于战术的工业控制系统防御

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-04-17 DOI: 10.1109/TMLCN.2026.3684465

Ying-Dar Lin;Rasul Mankaev;Didik SUDYANA;Yuan-Cheng Lai;Ren-Hung Hwang

{"title":"Reinforcement Learning-Driven Honeypots: Tactic-Based Defense for Industrial Control Systems","authors":"Ying-Dar Lin;Rasul Mankaev;Didik SUDYANA;Yuan-Cheng Lai;Ren-Hung Hwang","doi":"10.1109/TMLCN.2026.3684465","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3684465","url":null,"abstract":"Traditional honeypots in Industrial Control Systems (ICS) often fail to sustain attacker engagement, exposing their deception after a few predictable responses and limiting visibility to early reconnaissance. This weakness reduces their value for defenders seeking insight into later stages of the kill chain. To address this, we propose an adaptive honeypot that embeds reinforcement learning (RL) into an ICS honeypot, enabling it to shape responses in real time based on attacker behavior. Guided by the MITRE ATT&CK for ICS framework, each network interaction is mapped to a tactic, and the RL agent applies tabular Q-learning to select deception strategies such as delaying, modifying, or blocking ICS protocol messages to drive sessions deeper into the attack chain. We evaluated the system using scripted Metasploit-based attacks derived from MITRE Campaigns across three honeypot configurations: Static0 (default ICS honeypot), Static1 (modified Modbus handler to support uncommon but valid function codes), and Dynamic (RL-driven honeypot). Results show that while static honeypots stall at tactic levels 4 and 7, the RL-driven honeypot reaches the latest tactic within 700 attack–response cycles and more than doubles the average engagement time. Even in the first 400 sessions, a small portion of attacks already reach the final tactic, providing early threat intelligence. These findings show that an RL-enhanced honeypot can adapt autonomously, extract deeper threat intelligence, and give defenders broader visibility across ICS tactics.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"760-779"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11482844","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spectral-Guided Mask Generation for Error Correction Code Transformers 纠错码变压器的光谱引导掩模生成

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-03-28 DOI: 10.1109/TMLCN.2026.3698041

Linh Nguyen;Quoc Bao Phan;Tuy Tan Nguyen

{"title":"Spectral-Guided Mask Generation for Error Correction Code Transformers","authors":"Linh Nguyen;Quoc Bao Phan;Tuy Tan Nguyen","doi":"10.1109/TMLCN.2026.3698041","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3698041","url":null,"abstract":"Neural decoders have recently advanced error correction performance, with transformer-based architectures in the error correction code transformer (ECCT) framework achieving state-of-the-art results among neural decoders. However, the multiple-masks ECCT suffers from suboptimal mask selection, which impacts decoding performance. In this work, we propose spectral-guided ECCT, leveraging the Laplacian matrix and its eigenvectors from the variable-variable graph to identify weakly connected node clusters for optimized mask generation. By embedding spectral properties into the decoding process, our method constructs more effective masks that improve information flow across variable and check nodes. Experimental results demonstrate consistent gains over existing ECCT-based models, achieving superior decoding accuracy and reliability while significantly improving training efficiency. This work highlights the potential of spectral graph theory to address structural challenges in neural decoders and provides a principled direction for future error correction design.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"913-928"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11538155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ExposNet: A Deep Learning Framework for EMF Exposure Prediction in Complex Urban Environments exponet：复杂城市环境中EMF暴露预测的深度学习框架

IF 4.9

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-07-17 DOI: 10.1109/TMLCN.2026.3714431

Yarui Zhang;Shanshan Wang;Joe Wiart

{"title":"ExposNet: A Deep Learning Framework for EMF Exposure Prediction in Complex Urban Environments","authors":"Yarui Zhang;Shanshan Wang;Joe Wiart","doi":"10.1109/TMLCN.2026.3714431","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3714431","url":null,"abstract":"The prediction of the electric field (E-field) plays a crucial role in monitoring radiofrequency electromagnetic field (RF-EMF) exposure induced by cellular networks. In this paper, a deep learning framework is proposed to predict E-field levels in complex urban environments. First, the drive test measurement in Paris and Lyon and publicly accessible databases used to construct the training dataset are introduced, with a detailed explanation provided on how these datasets are formulated and integrated to enhance their suitability for Convolutional Neural Networks (CNNs)-based models. Then, the proposed model, ExposNet, which is a lightweight CNN-based framework, is presented. Two variants of the network structure are proposed, enabling per-frequency prediction and total E-field prediction. Extensive experimental analyses are conducted, including the comparison with a standard U-Net baseline and a classical Kriging baseline. Moreover, a detailed ablation study is conducted to quantify the contribution of each input component, and additional generalization experiments are performed on different test subsets. The overall results indicate that, despite being trained and tested on real-world measurements, the model performs well and achieves better accuracy compared to previous studies.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1229-1243"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11614051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148628355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network Slice Placement in Large Scale Infrastructures Using Efficient and Highly Scalable Deep Reinforcement Learning Agent 利用高效、高可扩展的深度强化学习代理在大规模基础设施中放置网络片

IF 4.9

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-06-10 DOI: 10.1109/TMLCN.2026.3702022

Abdel Kader Chabi Sika Boni;Hassan Hassan;Khalil Drira

{"title":"Network Slice Placement in Large Scale Infrastructures Using Efficient and Highly Scalable Deep Reinforcement Learning Agent","authors":"Abdel Kader Chabi Sika Boni;Hassan Hassan;Khalil Drira","doi":"10.1109/TMLCN.2026.3702022","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3702022","url":null,"abstract":"Internet of Things (IoT) systems often host services with diverse and sometimes conflicting Quality of Service (QoS) requirements, presenting a significant challenge in delivering tailored QoS. This challenge becomes even more complex with the introduction of autonomy, which aims to minimize or eliminate human intervention while still maintaining QoS. Network Slicing has emerged as a promising solution by enabling the creation, management, and termination of logical networks known as network slices, each designed to meet the specific QoS needs of individual IoT services. The central question is: how can we autonomously create, manage, and terminate the maximum number of slices in dynamic environments? Deep Reinforcement Learning (DRL) agents have partially addressed this challenge by autonomously and adaptively placing slices using reward-based feedback mechanisms. However, for a DRL agent to be truly effective in Network Slicing, it must meet four key criteria: fast convergence, high slice placement performance, policy stability and strong scalability to maintain the first three qualities regardless of the underlying physical infrastructure’s size. To the best of our knowledge, no existing solution satisfies all four criteria. In this work, we present DAAFA, a DRL agent specifically designed to meet this challenge in Core Network slicing. DAAFA achieves high scalability by decoupling convergence speed from infrastructure size and ensures rapid, stable convergence through advanced feature extraction techniques. To validate its effectiveness, we developed two versions of DAAFA based on Deep Q-Networks (DQN) and Double Deep Q-Networks (DDQN), and compared them against ten state-of-the-art alternatives, each tuned for optimal performance. Simulation results across three infrastructures, scaling up to 360 nodes, confirm that DAAFA successfully meets all four essential criteria.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1005-1027"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11556488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heterogeneity-Agnostic AI/ML-Assisted Beam Selection for Multi-Panel Arrays 多面板阵列的非均匀性AI/ ml辅助波束选择

IF 4.9

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-07-20 DOI: 10.1109/TMLCN.2026.3715130

Ibrahim Kilinc;Robert W. Heath

{"title":"Heterogeneity-Agnostic AI/ML-Assisted Beam Selection for Multi-Panel Arrays","authors":"Ibrahim Kilinc;Robert W. Heath","doi":"10.1109/TMLCN.2026.3715130","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3715130","url":null,"abstract":"AI/ML-based beam selection methods coupled with location information can effectively reduce beam training overhead. Heterogeneous antenna hardware with varying dimensions, orientations, codebooks, element patterns, and polarization angles limits their feasibility and generalization because many models are tied to a fixed antenna configuration. In this paper, we propose a heterogeneity-agnostic AI/ML-assisted beam selection method for multi-panel arrays by predicting path information independent of antenna configuration. We derive a reference signal received power (RSRP) model that separates propagation characteristics from antenna configuration. We formulate an optimization framework to extract path information, consisting of angle of arrival (AoA), angle of departure (AoD), and path matrices incorporating path gain and channel depolarization, from beamformed RSRP measurements. We then develop a three-stage autoregressive network that predicts path information from user location, enabling RSRP calculation and beam selection for arbitrary antenna configurations without retraining or maintaining separate models for each configuration. Simulation results show that the proposed method approaches genie-aided spectral efficiency across the studied antenna-size, orientation, and codebook heterogeneity settings while reducing beam training overhead.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1318-1334"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11614503","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Service Function Chaining Architecture for Multi-Hop Split Inference 多跳分裂推理的业务功能链结构

IF 4.9

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-08-18 DOI: 10.1109/TMLCN.2026.3725194

Takanori Hara;Masahiro Sasabe

{"title":"Service Function Chaining Architecture for Multi-Hop Split Inference","authors":"Takanori Hara;Masahiro Sasabe","doi":"10.1109/TMLCN.2026.3725194","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3725194","url":null,"abstract":"Service Function Chaining (SFC) ensures traffic follows a predefined sequence of service functions, enabling dynamic and efficient network services. Inspired by this, we propose an SFC-based architecture for Multi-hop Split Inference (MSI), where split sub-models are interpreted as service functions and their composition forms a service chain representing the global model. This architecture dynamically establishes communication paths for sub-models, ensuring efficient and adaptive execution. We design Neural Service Functions (NSFs) to execute sub-models as transparent TCP proxies and integrate them with Software Defined Networking (SDN), Segment Routing over IPv6 (SRv6), and the extended Berkeley Packet Filter (eBPF)-based SFC proxy. This integration enables efficient inference over dynamic routing while maintaining compatibility with existing applications. Through evaluations using well-known image recognition models and datasets, we show that the proposed architecture is feasible for MSI, particularly for latency-sensitive inference with small batch sizes, and supports dynamic path reconfiguration to adapt to changing network conditions.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1389-1405"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11658952","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Erratum to “Graph Neural Network-Aided Online Calibration of Phase Shifter Networks” “图神经网络辅助移相网络在线定标”的勘误

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-04-29 DOI: 10.1109/TMLCN.2026.3684074

Idan Roth;Lutz Lampe

引用次数: 0

SAFE-ADVENT: Privacy-Preserving and Resilient Distributed Federated Learning for DDoS Attacks Detection in 5G-V2X Networks 安全降临：5G-V2X网络中用于DDoS攻击检测的隐私保护和弹性分布式联邦学习

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-03-28 DOI: 10.1109/TMLCN.2026.3697341

Badre Bousalem;Fahdah Alalyan;Vinicius F. Silva;Sylvain Cherrier;Wael Jaafar;Rami Langar

{"title":"SAFE-ADVENT: Privacy-Preserving and Resilient Distributed Federated Learning for DDoS Attacks Detection in 5G-V2X Networks","authors":"Badre Bousalem;Fahdah Alalyan;Vinicius F. Silva;Sylvain Cherrier;Wael Jaafar;Rami Langar","doi":"10.1109/TMLCN.2026.3697341","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3697341","url":null,"abstract":"Wireless communication technology is a vital enabler for 5G vehicle-to-everything (5G-V2X) communications, which are vulnerable to cyberattacks such as distributed denial of service (DDoS). DDoS detection requires sophisticated artificial intelligence (AI)-based approaches, whose main challenge is data privacy. Secured peer-to-peer (P2P) federated learning (FL) techniques could overcome this challenge by training detection models locally while sharing model parameters for secure aggregation. However, such a strategy scales poorly and incurs a high communication cost as the number of FL clients grows. In such a context, we propose SAFE-ADVENT, a secure P2P FL strategy that reduces the communication cost, incorporating transfer learning and client selection within the P2P FL system to detect DDoS attacks. By using data collected from our 5G-V2X testbed, under offline scenarios, SAFE-ADVENT converged faster than deep learning, centralized FL and SAC-based P2P FL benchmarks. Through live traffic testing and under adversarial attacks, SAFE-ADVENT outperformed all benchmarks in most scenarios. Finally, SAFE-ADVENT proved to be scalable by preserving high performance when increasing the number of FL agents.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"929-949"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11538115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

End-to-End Deep Learning for CSI Estimation and Feedback in Large-Scale RIS-Assisted Wireless Systems 大规模ris辅助无线系统中CSI估计和反馈的端到端深度学习

IF 4.9

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-08-18 DOI: 10.1109/TMLCN.2026.3724779

Syed Samiul Alam;Yanxiao Zhao;Haolin Tang;Wei Wang;Erdem Topsakal;Min Song

{"title":"End-to-End Deep Learning for CSI Estimation and Feedback in Large-Scale RIS-Assisted Wireless Systems","authors":"Syed Samiul Alam;Yanxiao Zhao;Haolin Tang;Wei Wang;Erdem Topsakal;Min Song","doi":"10.1109/TMLCN.2026.3724779","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3724779","url":null,"abstract":"To fully harness the potential of reconfigurable intelligent surfaces (RIS)-assisted communication systems, accurate channel estimation and reliable acquisition of full channel state information (CSI) are essential. However, this task becomes increasingly challenging due to the high dimensionality of the channel, particularly in large-scale RIS deployments with hundreds or thousands of reflecting elements. In this paper, we propose a deep learning (DL)-based end-to-end framework that jointly performs CSI estimation and feedback for RIS-assisted wireless systems. The proposed framework first employs a denoising super-resolution network (DSRNet) to reconstruct full CSI from available partial CSI. This is followed by a novel encoder-decoder-based CSI feedback network, termed DCFNet, where the encoder compresses the estimated CSI into a compact codeword, and the decoder reconstructs the CSI using residual denoising and channel refinement sub-blocks. By jointly optimizing CSI estimation, compression, and reconstruction, the proposed framework reduces the mismatch between separately trained estimation and feedback modules. The simulation results indicate that the proposed E2E-CsiNet outperforms representative CSI feedback methods and deep learning–based end-to-end estimation and feedback baselines. It achieves superior accuracy in CSI reconstruction. In addition, DCFNet achieves a favorable accuracy–complexity tradeoff. It reduces trainable parameters and memory usage compared with several feedback baselines while maintaining a practical inference time. These results highlight the effectiveness of the proposed framework for scalable, large-scale RIS CSI acquisition, even under conditions of partial CSI availability and noisy feedback transmission.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1442-1456"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11658791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148928329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stubbornness-Aware Peer-to-Peer Bayesian Federated Learning Over Graphs 顽固性意识点对点贝叶斯联合学习图

IF 4.9

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-07-06 DOI: 10.1109/TMLCN.2026.3710445

Zengyan Tang;Dongyan Sui;Haoran Sun;Siyang Leng

{"title":"Stubbornness-Aware Peer-to-Peer Bayesian Federated Learning Over Graphs","authors":"Zengyan Tang;Dongyan Sui;Haoran Sun;Siyang Leng","doi":"10.1109/TMLCN.2026.3710445","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3710445","url":null,"abstract":"Edge devices such as sensors, vehicles, and industrial controllers often need to learn a shared predictive model without pooling raw data or relying on a central server, even when local data quality varies widely across devices. This paper proposes a fully decentralized, peer-to-peer Bayesian federated learning method that explicitly regulates the relative influence of newly observed local data and neighbor information via an agent-specific stubbornness parameter. In each communication round, every device performs a stubbornness-weighted Bayesian-type local update using a fresh sample, exchanges the resulting intermediate belief with one-hop neighbors, and then fuses received beliefs through graph-weighted aggregation. To accommodate different training stages and heterogeneous data, we further introduce 1) time-varying schedule that gradually transitions from fast early adaptation to stable late-stage refinement, and 2) data-quality-aware rule that assigns agent-specific stubbornness using a simple similarity score. We provide a finite-time analysis that yields performance guarantees and clarifies how emphasizing higher-quality, lower-noise agents accelerates learning. Experiments on distributed linear regression and decentralized image classification demonstrate faster convergence than common decentralized baselines and improved robustness when some agents hold biased, scarce, or uninformative data.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1158-1176"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595697","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148494639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0