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

{"title":"Downlink and Uplink User Selection in MU-MIMO: A Real-Time Unsupervised Probabilistic Framework","authors":"Kiril Danilchenko;João Paulo P. G. Marques;Catherine Rosenberg","doi":"10.1109/TMLCN.2026.3699580","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3699580","url":null,"abstract":"User selection is an important component of the Radio Resource Management of fully digital Multi-User Multiple-Input Multiple-Output (MU-MIMO) systems and its efficiency is critical to the performance of these systems. In this paper, we propose a novel framework called, Dynamic Probabilistic User Selection (DPUS), that formulates user selection as a probabilistic inference task using Markov Random Fields and Loopy Belief Propagation. Unlike existing methods, DPUS operates in an online unsupervised manner, eliminates the need for labeled data, and integrates fairness constraints with channel-awareness. Importantly, DPUS can apply to both the downlink and the uplink. Extensive simulations conducted in a large range of deployment scenarios show that DPUS consistently outperforms state-of-the-art heuristics in terms of fairness, scalability, and runtime. DPUS provides a robust solution for the efficient operation of fully digital MU-MIMO systems, paving the way for next-generation wireless networks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1042-1057"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11547220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443411","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":"X-ZeroSec: Zero-Shot Evasion Attack Detection in Deep Learning-Based Beamforming Prediction With XAI","authors":"Thulitha Senevirathna;Bartlomiej Siniarski;Madhusanka Liyanage;Shen Wang","doi":"10.1109/TMLCN.2026.3724613","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3724613","url":null,"abstract":"With the rise of next-generation wireless systems, Deep Learning (DL)-based security solutions are gaining widespread adoption. One key area DL impacts is beamforming prediction in mmWave communication. This study investigates how vulnerable AI-based models are to adversarial attacks, which can disrupt network performance in complex distributed MIMO (D-MIMO) environments. Since AI plays a crucial role in selecting beams efficiently, such vulnerabilities pose a serious threat to the reliability of 5G and 6G networks. Existing work fails in achieving generalizable performance on unseen attacks in low-latency requirements. To enhance resilience, we propose an innovative XAI-based framework dubbed X-ZeroSec that leverages SHAP, specifically Shapley values from cooperative game theory, to detect and mitigate adversarial attacks. Our approach introduces the concept of explanation distillation, where feature attributions from a “teacher” detector model train a generalized XAI-based “student” model, yielding improved and generalized detection rates, especially for zero-shot attacks. The framework enhances robustness against various adversarial patterns, achieving over 10-16% improved accuracy compared to state-of-the-art detectors. Comprehensive evaluations demonstrate that integrating XAI into AI-driven beamforming fortifies model defenses, preserving mmWave system performance while meeting evolving security requirements. X-ZeroSec framework supports diverse DL-based applications and it is implemented on the UCD NETSLAB 5G O-RAN testbed, demonstrating promising end-to-end latency of less than 4 milliseconds.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1421-1441"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11658799","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148928328","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":"A Learnable Multi-Layer Structure for Joint Tx/Rx IQIs and CFO Compensation in FDE Systems","authors":"Lantian Wei;Kazunori Hayashi;Tadashi Wadayama","doi":"10.1109/TMLCN.2026.3737420","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3737420","url":null,"abstract":"This paper proposes radio frequency (RF) impairments compensation schemes for frequency domain equalization (FDE) systems, including single-carrier cyclic-prefix (SCCP) and orthogonal frequency division multiplexing (OFDM). Our approach addresses impairments caused by transmitter (Tx) and receiver (Rx) in-phase/quadrature-phase imperfections (IQIs) as well as carrier frequency offset (CFO) in SCCP and OFDM systems. We introduce a distortion compensation approach with a multi-layer structure that effectively compensates distortions caused by Tx/Rx IQIs, CFO, and frequency selective channel at each layer. Based on this approach, we develop a learnable multi-layer filter in a model-driven deep learning framework, where all weights of the multi-layer filter are determined solely from the final layer output of the filter in a data-driven manner. Simulation results demonstrate that the proposed learnable multi-layer filter achieves comparable bit error rate (BER) performance to its non-adaptive counterpart that assumes perfect channel state information, while outperforming existing adaptive methods in terms of convergence speed and robustness in the online adaptation scenarios with limited training data. Furthermore, we present a technique to estimate the distortion parameters from the trained filter weights, potentially enabling real-time system monitoring. The proposed methodology with the multi-layer structure is promising for enhancing reliability and performance across various wireless communication configurations in the presence of hardware impairments, particularly in scenarios where accurate channel and impairment information is unavailable.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1550-1568"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11707750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130833","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":"Scalable Reinforcement Learning-Based Environment-Aware Scheduler for Enhancing Reliability and Availability in 6G Networks","authors":"Roya Khanzadeh;Fjolla Ademaj-Berisha;Bernhard Etzlinger;Hans-Peter Bernhard;Andreas Springer","doi":"10.1109/TMLCN.2026.3711563","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3711563","url":null,"abstract":"The sixth generation (6G) of wireless networks must ensure high reliability, availability, and fairness, even in dynamic and challenging environments. Environmental-aware knowledge, such as that obtained from Radio Environment Maps (REMs), offers predictive insights into channel conditions and can guide more effective scheduling decisions. This paper proposes a scalable deep reinforcement learning (DRL) framework that exploits such knowledge to optimize multi-user scheduling under limited resources, in order to enhance reliability and availability while maintaining fairness. Unlike standard deep Q-network (DQN), which evaluates Q-values per action, we propose a novel learning method that estimates per-user Q-values, enabling user selection with per-decision complexity that scales linearly with the number of users. Simulation results show that the proposed approach consistently balances reliability and availability while maintaining fairness, outperforming Round Robin (RR) and Proportional Fair (PF) schedulers, especially in environments with high clutter density and frequent non-line-of-sight conditions. In particular, the proposed method improves reliability by over 400% with only a 2% drop in availability, compared to the RR scheduler. Relative to PF, it improves availability and fairness by 23% and 40%, respectively, without sacrificing reliability. These improvements are observed under balanced propagation conditions, where the probabilities of line-of-sight and non-line-of-sight are equal due to a clutter density of 50%. These results highlight the potential of the proposed environmental-aware DRL scheduler to support trustworthy 6G communication in complex and dynamic environments.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1177-1198"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11602100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148494640","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":"LLM4SG: Adapting Large Language Model for Scatterer Generation via Synesthesia of Machines","authors":"Zengrui Han;Lu Bai;Ziwei Huang;Xiang Cheng","doi":"10.1109/TMLCN.2026.3717538","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3717538","url":null,"abstract":"In this paper, a novel large language model (LLM)-based method for scatterer generation (LLM4SG) is proposed for sixth-generation (6G) artificial intelligence (AI)-native communications. To provide a solid data foundation, we construct a new synthetic intelligent sensing-communication dataset for Synesthesia of Machines (SoM) in vehicle-to-vehicle (V2V) communications, named SynthSoM-V2V, covering multiple V2V scenarios with multiple frequency bands and multiple vehicular traffic densities (VTDs). Leveraging the powerful representation capabilities of LLMs, LLM4SG is designed to capture the general mapping relationship from light detection and ranging (LiDAR)-derived environmental geometry to electromagnetic scatterers via SoM. To address the inherent and significant differences across multi-modal data, a synergistically optimized four-module architecture, i.e., preprocessor, embedding, backbone, and output modules, is designed by considering sensing characteristics and electromagnetic propagation. The embedding module achieves effective cross-domain alignment of the sensing-communication domain and the natural language domain. The backbone network is adapted in a task-guided manner with low rank adaptation (LoRA), where a carefully selected subset of layers is fine-tuned to preserve general knowledge and reduce training cost. The proposed LLM4SG is evaluated for scatterer generation by benchmarking against ray-tracing (RT) and conventional deep learning models. Simulation results demonstrate that the proposed LLM4SG achieves superior performance in both full-sample and cross-condition generalization testing. It significantly outperforms conventional deep learning models across different frequency bands, scenarios, and VTDs, and demonstrates the capability to provide the massive and high-quality channel small-scale fading data required by AI-native 6G systems.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1281-1296"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11630674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747985","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":"Graph Neural Network-Aided Online Calibration of Phase Shifter Networks","authors":"Idan Roth;Lutz Lampe","doi":"10.1109/TMLCN.2026.3679501","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3679501","url":null,"abstract":"Large-scale antenna systems with hybrid analog and digital beamforming for millimeter-wave (mmWave) communications play an essential role in enabling reliable, high data-rate transmissions for 5G and beyond wireless networks. However, their phase shifter networks (PSNs), which steer and shape the radiation patterns, are highly susceptible to phase deviations resulting from manufacturing defects, hardware degradation, and temperature shifts. Periodic calibration of PSNs is necessary to maintain operational effectiveness. To overcome the challenges associated with model-based iterative algorithm approaches, this paper introduces the first machine learning-based method for in situ online calibration of PSNs in mmWave hybrid beamforming systems. Specifically, we demonstrate that graph neural networks (GNNs) are well suited to effectively parameterize the mapping from the received pilots to PSN phase deviation estimates. To benchmark the performance of learning-based methods, we also derive the hybrid Cramér-Rao bound (HCRB) for the phase deviation estimates. Simulation results validate the effectiveness of our approach, showing that the GNN achieves performance close to the HCRB, outperforms traditional optimization-based methods, and exhibits robustness in dynamically changing environments.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"629-646"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11458880","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665556","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":"Multi-Temporal Device Clustering for Federated Learning-Internet of Things Intrusion Detection Systems","authors":"Mohannad Abu Issa;Abdelrahman Eldosouky;Ashraf Matrawy;Mohamed Ibnkahla","doi":"10.1109/TMLCN.2026.3706262","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3706262","url":null,"abstract":"FL intrusion detection systems (IDSs) have emerged as a defense-in-depth layer for securing internet of things (IoT) networks by training detection models and classifying IoT traffic locally on distributed devices. However, developing effective federated learning (FL)-IoT IDSs remains significantly challenging due to key IoT characteristics, including their distributed nature, resource constraints, and data heterogeneity. Thus, improving detection performance and accelerating model convergence in FL-IoT IDSs while accounting for these IoT characteristics is essential. Moreover, existing FL-IoT IDSs lack mechanisms for preparing reliable pseudo-labeled IoT traffic for subsequent training rounds. To this end, we propose a multi-temporal device clustering FL-IoT IDS framework that partitions federated learning detection rounds (FLDRs) in time and clusters devices within each partition to enhance detection effectiveness without compromising IoT resources. We also establish the FLDR as an organizational framework to structure the periodic intrusion-detection processes in FL-IoT IDSs. The framework employs a post-processing (PP) module that applies confidence thresholding and outlier removal in unsupervised and hybrid supervised–unsupervised settings, enabling on-device filtering of pseudo-labeled traffic and reuse of retained pseudo-labeled traffic in the subsequent round. It also incorporates a data-driven clustering technique that anticipates non-independent and identically distributed (IID) behavior in upcoming FLDRs, thereby mitigating the impact of non-IID intrusion distributions. For evaluation, we implement dual-, triple-, and quadruple-temporal variants of the framework and assess different machine learning components. The PP module is benchmarked against supervised and no-PP baselines, while the data-driven clustering technique is compared with random and similarity-based clustering approaches. The data-driven quadruple-temporal clustering technique is also benchmarked against two existing FL-IoT IDS algorithms. We further assess resource consumption in terms of computational usage and communication overhead. Experiments on balanced and imbalanced intrusion scenarios derived from the Edge-IIoTset and MQTT-IoT-IDS datasets demonstrate that the proposed framework achieves superior detection accuracy, faster convergence, and fewer false positives and false negatives, thereby reducing unnecessary alarms and lowering the risk of undetected intrusions. The PP module proves effective for on-device pseudo-label filtering, with hybrid supervised–unsupervised methods achieving performance close to that of the supervised baseline, while the data-driven clustering technique consistently yields balanced and robust performance. Moreover, the proposed data-driven quadruple-temporal variant outperforms two existing FL-IoT IDS algorithms on imbalanced datasets. Importantly, the overall resource utilization remains comparable to that of the traditional FL-IoT ","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1076-1102"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11575686","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443393","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":"Targeted Adversarial Attacks Against Deep Learning-Based Wideband Spectrum Sensing","authors":"Shilian Zheng;Zhihao Ye;Luxin Zhang;Keqiang Yue;Zhijin Zhao","doi":"10.1109/TMLCN.2026.3694666","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3694666","url":null,"abstract":"Deep learning-based wideband spectrum sensing models have achieved impressive performance in subband occupancy detection, but their susceptibility to adversarial attacks raises serious concerns. In this paper, we construct a targeted adversarial attack framework specifically designed for multi-label wideband spectrum sensing tasks. We further propose two optimization-based attack methods. MLS_CW formulates perturbation generation as a constrained optimization problem and solves it via gradient descent. MLS_DF is a greedy iterative method that approximates the solution to an underdetermined linear system through linearization. Both attacks are developed under strict power constraints and are capable of generating subtle yet highly effective perturbations. These attacks further incorporate a threshold-aware loss and selective subband targeting to enhance precision and stealthiness. Extensive experiments across various SIR conditions demonstrate that the proposed attacks significantly degrade the model’s detection performance in both untargeted and targeted scenarios, while maintaining high stealthiness. These results expose the severe vulnerability of DL-based spectrum sensing models and highlight the need for further research into their robustness under adversarial settings.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"950-965"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11523577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433228","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":"MF-Pnet: Deep Learning-Based Multi-Narrowband FM Signal Fusion for Enhanced Positioning","authors":"Shilian Zheng;Xinjiang Qiu;Luxin Zhang;Quan Lin;Keqiang Yue;Zhijin Zhao","doi":"10.1109/TMLCN.2026.3672679","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3672679","url":null,"abstract":"This paper proposes MF-Pnet, a deep learning-based positioning method designed to address the limited utilization of informative features in existing FM signal positioning approaches. Unlike prior methods that process the wideband FM signal as a whole, MF-Pnet extracts and fuses multiple narrowband IQ signals that carry potentially complementary information across different frequency bands. A multi-branch parallel feature extraction network is constructed to independently model the features from each narrowband signal, and an attention mechanism is introduced to adaptively weight their relative contributions. This design significantly enhances the overall feature representation and improves positioning accuracy. Comprehensive experiments conducted on a real-world FM dataset demonstrate that MF-Pnet outperforms the state-of-the-art FM-Pnet in both indoor and outdoor environments, exhibiting significant advantages in terms of accuracy, stability, and cross-day generalization capability. Furthermore, MF-Pnet maintains practical inference efficiency, making it well suited for deployment on resource-constrained devices.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"562-574"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11429040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558044","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":"Digital Twin-Driven Continual Deep Reinforcement Learning for Coexistence of Multiple Radio Access Technology IoT Links With Nonlinear Receivers","authors":"Nahed Belhadj Mohamed;Georges Kaddoum;Md. Zoheb Hassan","doi":"10.1109/TMLCN.2026.3669837","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3669837","url":null,"abstract":"This article investigates the coexistence of downlink Internet-of-Things (IoT) links enabled by multiple radio access technologies (RATs), including long-term evolution (LTE) and 5G new radio (NR). The coexistence of multiple RAT IoT links is significantly challenged by adjacent channel interference (ACI) and hardware impairments (HWI) that arise from practical low-complexity radio-frequency front ends. To mitigate these challenges, we propose a radio resource optimization scheme that dynamically adjusts link adaptation parameters (transmit power, modulation, and coding rate) to maximize overall throughput while explicitly accounting for ACI and HWI. However, the proposed optimization is an NP-hard mixed-integer non-linear programming problem that requires global channel state information and centralized optimization, making it impractical for large-scale, dynamic multi-RAT IoT networks. To enable distributed optimization under ACI and HWI, we reformulate the problem as a Markov game and develop a multi-agent deep reinforcement learning (MADRL) framework that derives equilibrium link adaptation policies from local observations. Direct deep reinforcement learning (DRL) training in real networks, however, incurs high communication overhead and can create adverse effects due to the random explorations. To overcome these limitations, we introduce a context-aware digital twin network (DTN) that provides a safe and efficient virtual environment for training. In particular, we propose a novel DTN-empowered MADRL scheme that employs a replay memory-based continual model updating strategy, enabling policies to be learned from DT-generated experiences and periodically refined with real network data. This approach alleviates the need for frequent physical network interactions and significantly reduces communication overhead. Extensive simulations demonstrate that the proposed framework is scalable, computationally efficient, and robust in dynamic IoT environments, while outperforming 3GPP-standardized link adaptation in the presence of non-negligible ACI and HWI.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"591-611"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11419167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558083","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}