IEEE Transactions on Machine Learning in Communications and Networking最新文献

{"title":"TDoA-Based Self-Supervised Channel Charting With NLoS Mitigation","authors":"Mohsen Ahadi;Omid Esrafilian;Florian Kaltenberger;Adeel Malik","doi":"10.1109/TMLCN.2026.3688619","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3688619","url":null,"abstract":"Channel Charting (CC) has emerged as a promising framework for data-driven radio localization, yet existing approaches often struggle to scale globally and to handle the distortions introduced by non-line-of-sight (NLoS) conditions. In this work, we propose a novel CC method that leverages Channel Impulse Response (CIR) data enriched with practical features such as Time Difference of Arrival (TDoA) and Transmission Reception Point (TRP) locations, enabling a TDoA-based self-supervised localization function on a global scale. The proposed framework is further enhanced with short-interval User Equipment (UE) displacement measurements, which improve the continuity and robustness of the learned positioning function. Our algorithm incorporates a mechanism to identify and mask NLoS-induced noisy measurements, leading to significant performance gains. We present the evaluations of our proposed models in a real 5G testbed and benchmarked against centimeter-accurate Real-Time Kinematic (RTK) positioning, in an O-RAN–based 5G network by OpenAirInterface (OAI) software at EURECOM. It demonstrates results that outperform the state-of-the-art semi-supervised and self-supervised CC approaches in a real-world scenario. The results show localization accuracies of 2–4 meters in 90% of cases, across varying NLoS ratios. Furthermore, we provide public datasets of CIR recordings, along with the true position labels used in this paper’s evaluation.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"780-793"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11500565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828987","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}

{"title":"Optimizing Utility–Privacy Tradeoff in Federated Learning Over Fading Channels","authors":"Yujia Mu;Xizixiang Wei;Yonina C. Eldar;Cong Shen","doi":"10.1109/TMLCN.2026.3706257","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3706257","url":null,"abstract":"This paper develops a utility-privacy optimized federated learning (FL) framework that aggregates local updates transmitted over fading channels using only channel state information at the receiver (CSIR). We first establish a rigorous convergence analysis of distributed stochastic gradient descent (SGD) under arbitrary aggregation weights and channel fading, deriving an upper bound that explicitly captures the combined effects of channel quality and SGD noise. Using this bound, we formulate and solve an optimization problem that yields closed-form or efficiently computable aggregation weights. The resulting method provably achieves the standard <inline-formula> <tex-math>${mathcal {O}}(1/t)$ </tex-math></inline-formula> convergence rate for strongly convex objectives, even under arbitrarily varying fading channels. We further characterize the differential privacy (DP) induced by channel noise and develop a principled mechanism that enables explicit utility-privacy tradeoffs through weight selection. Extensive experiments on MNIST, CIFAR-10, Shakespeare, and F-EMNIST demonstrate that the proposed fading-aware aggregation eliminates the training failures suffered by FedAvg under deep fading and approaches the performance of ideal error-free communication. The results provide a unified convergence-communication-privacy framework for robust and privacy-aware FL over wireless fading channels.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1058-1075"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11575631","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443404","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}

{"title":"Resilient AAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning","authors":"Eslam Eldeeb;Hirley Alves","doi":"10.1109/TMLCN.2026.3656262","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3656262","url":null,"abstract":"Reinforcement learning (RL) has emerged as a powerful tool for intelligent decision-making in wireless networks. However, most existing RL frameworks rely on continuous online interaction with the environment, which is often impractical in real-world scenarios due to safety risks, high costs, and lack of scalability across different environments. To overcome these limitations, we propose a novel few-shot meta-offline RL framework for resilient AAV trajectory and scheduling optimization in wireless IoT networks. Our approach integrates conservative Q-learning (CQL) with model-agnostic meta-learning (MAML), enabling AAVs to learn effective policies from static offline datasets and rapidly adapt to unseen environments with only a few training shots. The proposed framework is resilience-aware, allowing robust operation under unpredictable conditions such as weather-induced link failures. Numerical results show that the proposed algorithm consistently outperforms baseline schemes, including deep Q-networks (DQNs), CQL, and DQN-MAML. Specifically, it converges significantly faster, achieves lower joint age-of-information (AoI) and transmission power, and maintains reliable performance in outage-prone environments. Moreover, the meta-learning component enhances scalability by achieving more than 50% gain in return for policies trained over new objectives with limited data.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"409-421"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11359337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148493556","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}

{"title":"Latent-Attention-Based Transformer for Near ML Polar Decoding in Short-Code Regime","authors":"Hongzhi Zhu;Wei Xu;Xiaohu You","doi":"10.1109/TMLCN.2026.3656874","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3656874","url":null,"abstract":"Transformer architectures have emerged as promising deep learning (DL) tools for modeling complex sequence-to-sequence interactions in channel decoding. However, existing transformer-based decoders for error correction codes (ECCs) demonstrate inferior decoding performance and generalization capabilities compared to conventional algebraic decoders, especially in short-code regimes. In this work, we propose a novel latent-attention based transformer (LAT) decoder for polar codes that addresses these limitations through three pivotal innovations. First, we develop a latent-attention mechanism that supersedes the conventional self-attention mechanism. This architectural modification enables independent learning of the Query and Key matrices for code-aware attention computation, decoupling them from the Value matrix. As a result, the LAT decoder emphasizes position-wise decoding interactions while mitigating interference from context correlations that are misaligned with the inherent code structure. Second, we devise an advanced training framework incorporating three synergistic components: entropy-aware importance sampling that emphasizes low-probability regions in the signal constellation space, experience reflow that introduces empirical labels to improve characterization of decoding boundaries, and dynamic label smoothing for likelihood-based regularization. Third, we propose a code-aware masking scheme which allows dynamic adaptation to varying code configurations without modifying the network architecture. Numerical evaluations demonstrate that the proposed LAT decoder achieves near maximum-likelihood (ML) performance in terms of both bit error rate (BER) and block error rate (BLER) for short-length polar codes. Moreover, the architecture exhibits robust generalization capabilities across a wide range of code rates and code lengths.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"373-390"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11359753","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148494643","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}

{"title":"Knowledge Graph-Enhanced Spatiotemporal Dual-Path Anomaly Detection for 6G Communication Networks","authors":"Zeyu Tan;Hang Zhan;Yiting Yang;Jiaheng Wang;Wanqing Cao;Yongming Huang","doi":"10.1109/TMLCN.2026.3705712","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3705712","url":null,"abstract":"As wireless communication technology advances toward sixth-generation (6G) systems, intelligent networks are evolving into user-centric, decentralized, and highly heterogeneous environments. Such systems’ multi-source heterogeneous time series data exhibit unprecedented scale, complexity, and strong spatiotemporal coupling. Challenges commonly encountered in traditional domains—such as complex temporal dependencies, high dimensionality, and scarcity of labeled anomalies—become more pronounced in 6G scenarios and are further compounded by intricate spatial topologies and dynamic inter-entity relationships. To address these challenges, this paper proposes a Knowledge Graph-enhanced Spatiotemporal Dual-path Anomaly Detection framework (KG-STDAD). The framework integrates community-level semantic information from a Knowledge Graph (KG) with a Transformer architecture with distinct temporal and spatial processing paths enhanced by variational autoencoder (VAE)-based reconstruction capabilities, enabling joint modeling of spatiotemporal dependencies. Specifically, a Transformer-VAE module is designed to capture dynamic temporal patterns. At the same time, community ID embedding, community depth masking, and edge encoding in the attention are introduced to enhance spatial modeling guided by structural knowledge from communication KGs. Extensive experiments on three datasets demonstrate the superior performance of KG-STDAD. Ablation studies further confirm the key contributions of the dual-path architecture and knowledge graph-based encoding. This work provides an accurate and explainable anomaly detection solution for user-centric 6G networks and new insights into the fusion of knowledge-driven and data-driven methods.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1244-1260"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11573335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747982","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}

{"title":"Reconstruction of SINR Maps From Sparse Measurements Using Group Equivariant Non-Expansive Operators","authors":"Lorenzo Mario Amorosa;Francesco Conti;Nicola Quercioli;Flavio Zabini;Tayebeh Lotfi Mahyari;Yiqun Ge;Patrizio Frosini","doi":"10.1109/TMLCN.2026.3716686","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3716686","url":null,"abstract":"As sixth generation (6G) wireless networks evolve, accurate signal-to-interference-noise ratio (SINR) maps are becoming increasingly critical for effective resource management and optimization. However, acquiring such maps at high resolution is often cost-prohibitive, creating a severe data scarcity challenge. This necessitates machine learning (ML) approaches capable of robustly reconstructing the full map from extremely sparse measurements. To address this, we introduce a novel reconstruction framework based on Group Equivariant Non-Expansive Operators (GENEOs). Unlike data-hungry ML models, GENEOs are low-complexity operators that embed domain-specific geometric priors, such as translation and rotational equivariance, directly into their structure. This provides a strong inductive bias, enabling effective reconstruction from very few samples. Our key insight is that for network management, preserving the topological structure of the SINR map, such as the geometry of coverage holes and interference patterns, is often more critical than minimizing pixel-wise error. We validate our approach on realistic ray-tracing-based urban scenarios, evaluating performance with both traditional statistical metrics (mean squared error (MSE)) and, crucially, a topological metric (1-Wasserstein distance). Results show that our method consistently achieves superior statistical and topological accuracy across diverse urban scenarios. Compared to the best-performing baselines, GENEO reduces MSE up to 45% and decreases the 1-Wasserstein distance up to 54%. Crucially, these substantial performance gains are maintained even under the most extreme tested conditions, such as a 1% sampling rate with a 30% measurement error, and when measurements are highly spatially biased. This demonstrates the practical advantage of GENEOs for creating structurally accurate SINR maps that are more reliable for downstream network optimization tasks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1353-1370"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11623287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748482","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}

{"title":"LLM-Driven Alert Correlation and Lifecycle Prediction With Threat Intelligence for Attack Forensics","authors":"Ying-Dar Lin;Chun-Yi Chang;Didik Sudyana;Yuan-Cheng Lai;Ren-Hung Hwang;Po-Ching Lin;Eric Hsiao-Kuang Wu;Wei-Bin Lee","doi":"10.1109/TMLCN.2026.3736372","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3736372","url":null,"abstract":"Security Operations Centers face overwhelming volumes of alerts from firewalls, intrusion detection systems, and other sources. These alerts often lack temporal and semantic coherence, hindering analysts from reconstructing full MITRE ATT&CK lifecycles or anticipating subsequent techniques. To address this, we propose a framework for alert correlation and lifecycle prediction, named LANCE (Lifecycle Analysis with Neural Correlation Engine). LANCE leverages large language models (LLMs) to consolidate heterogeneous alerts into structured, time-ordered sequences, while Cyber Threat Intelligence (CTI) provides semantic grounding. A Gemma 3-4B LLM serves as the backbone, with lightweight parameter adaptation enabling specialization for cybersecurity tasks without sacrificing real-time efficiency. The design combines three elements: structured prompts that transform raw alerts into coherent narratives, CTI-guided supervision that aligns telemetry with ATT&CK techniques, and efficient adaptation strategies that minimize computational overhead. Results show that without CTI, the highest achievable accuracy is only 27.82%, while integrating CTI improves performance to 64.76%. Using all available telemetry yields the highest prediction accuracy, whereas removing even one source, such as firewall logs, drops accuracy by nearly 30%. Together, these findings show that CTI integration, comprehensive telemetry, and efficient LLM reasoning enable accurate, real-time forensic analysis and proactive defense.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1491-1512"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11703819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130836","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}

{"title":"Diffusion Models for Accurate Channel Distribution Generation","authors":"Muah Kim;Rick Fritschek;Rafael F. Schaefer","doi":"10.1109/TMLCN.2026.3732251","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3732251","url":null,"abstract":"Strong generative models can accurately learn wireless channel distributions, enabling data-driven channel emulation when mathematical descriptions are intractable. Moreover, differentiable channel models facilitate gradient-based training of neural encoders within end-to-end (E2E) communication frameworks. While prior studies have primarily relied on generative adversarial networks (GANs) for this purpose, this paper investigates diffusion models (DMs) as an alternative approach for channel approximation. We develop a DM-based channel modeling framework that integrates efficiently into E2E coded-modulation systems and propose a stable pre-training algorithm for faster and more reliable convergence. Through controlled simulations across a range of canonical channel models, we show that DMs achieve near-optimal symbol error rates (SERs) and mode coverage. In particular, the advantage of DMs in mode coverage becomes evident for complex channel models with temporal correlation and strong nonlinearity, whereas both DMs and GANs accurately capture the distributions of relatively simple channel models. We further analyze the trade-off between mode coverage and sampling efficiency using skipped sampling and the denoising diffusion implicit model, quantified via sliced Wasserstein distance and E2E SER. The results demonstrate that appropriate parameter choices can substantially reduce sampling time with minimal degradation in generative performance. Overall, this study establishes a baseline and empirical foundation for diffusion-based channel generation, providing insights into their scalability and potential for realistic communication environments.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1474-1490"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11683922","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131606","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}

{"title":"xApp Empowered Resource Management for Non-Terrestrial Users in 5G O-RAN Networks","authors":"Mohammed M. H. Qazzaz;Syed Ali Raza Zaidi;Aubida A. Al-Hameed;Abdelaziz Salama;Des Mclernon","doi":"10.1109/TMLCN.2026.3693224","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3693224","url":null,"abstract":"This paper introduces a proactive Unmanned Aerial Vehicle (UAV) mobility management xApp for Open Radio Access Network (O-RAN) Near Real-Time Radio Intelligent Controller (Near-RT RIC) environments, employing Double Deep Q-Network (DDQN) reinforcement learning (RL) enhanced with transfer learning to optimise handover decisions for UAVs operating along predetermined flight trajectories. Unlike reactive approaches that respond to signal degradation, the proposed framework anticipates network conditions and minimises both outage probability and handover frequency through predictive optimisation. The system leverages centralised weight averaging to consolidate knowledge from multiple flight scenarios into a global model capable of generalising to previously unseen operational environments without extensive retraining. A comprehensive evaluation demonstrates that the proposed framework achieves a favourable trade-off between handover frequency and connectivity reliability, reducing handover events by up to 54.6% compared to greedy approaches while maintaining outage probability at practically negligible levels. The results validate the effectiveness of intelligent learning-based approaches for UAV mobility management in next-generation O-RAN architectures, thereby contributing to seamless integration of aerial user equipment into cellular networks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"794-812"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11520434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003585","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}

{"title":"CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction With Comprehensive Robustness and Generalization Testing","authors":"Sikai Cheng;Reza Zandehshahvar;Haoruo Zhao;Daniel A. Garcia-Ulloa;Alejandro Villena-Rodriguez;Carles Navarro Manchón;Pascal Van Hentenryck","doi":"10.1109/TMLCN.2026.3694830","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3694830","url":null,"abstract":"Channel state information (CSI) prediction is a promising strategy for ensuring reliable and efficient operation of massive multiple-input multiple-output (mMIMO) systems by providing timely downlink (DL) CSI. While deep learning–based methods have advanced beyond conventional model-driven and statistical approaches, they remain limited in robustness to practical non-Gaussian noise, generalization across diverse channel conditions, and computational efficiency. This paper introduces CSI-4CAST, a hybrid deep learning architecture that integrates 4 key components, i.e., Convolutional neural network residuals, Adaptive correction layers, ShuffleNet blocks, and Transformers, to efficiently capture both local and long-range dependencies in CSI prediction. To enable rigorous evaluation, this work further presents a comprehensive benchmark, CSI-RRG for Regular, Robustness and Generalization testing, which includes more than 300,000 samples across 3,060 realistic scenarios for both TDD and FDD systems. The dataset spans multiple channel models, a wide range of delay spreads and user velocities, and diverse noise types and intensity degrees. Experimental results show that CSI-4CAST achieves superior prediction accuracy with substantially lower computational cost, outperforming baselines in 81.5% of TDD scenarios and 44.4% of FDD scenarios—the best performance among all evaluated models—while reducing FLOPs by <inline-formula> <tex-math>${5}times $ </tex-math></inline-formula> and <inline-formula> <tex-math>${3}times $ </tex-math></inline-formula> compared to LLM4CP, the strongest baseline. In addition, evaluation over CSI-RRG provides valuable insights into how different channel factors affect the performance and generalization capability of deep learning models. Both the dataset (Hugging Face) and evaluation protocols (GitHub) are publicly released to establish a standardized benchmark and to encourage further research on robust and efficient CSI prediction.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"882-912"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11526994","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171896","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}