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

{"title":"QoS-Aware State-Augmented Learning for Wireless Coexistence Parameter Management","authors":"Mohammad Reza Fasihi;Brian L. Mark;Omar A. Alotaibi","doi":"10.1109/TMLCN.2026.3707514","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3707514","url":null,"abstract":"Efficient and fair coexistence in unlicensed spectrum is critical for next-generation wireless networks such as 5G New Radio in Unlicensed Spectrum (NR-U) and Wi-Fi, which contend for shared resources under diverse Quality of Service (QoS) requirements. Existing reinforcement learning (RL) approaches either rely on multi-objective formulations with heuristic weight tuning or primal-dual constrained methods that struggle with stability in dynamic environments. To address these limitations, we propose QaSAL-CPM, a novel QoS-aware State-Augmented Learning framework for Coexistence Parameter Management (CPM). Building on the recently introduced concept of state augmentation, QaSAL-CPM embeds dual variables directly into the agent’s observation space, enabling real-time responsiveness to constraint violations without retraining or complex penalty tuning. This design achieves strict QoS guarantees for high-priority traffic while maintaining fairness across traffic classes, even under heavy contention. Unlike prior methods, QaSAL-CPM separates offline learning from lightweight online execution, making it practical for real-world deployments such as Ultra-Reliable Low-Latency Communications (URLLC) and the Internet of Things (IoT). Through extensive simulations of 5G NR-U/Wi-Fi coexistence scenarios, we show that QaSAL-CPM enforces 95th-percentile delay compliance and improves policy robustness. These results demonstrate that state-augmented constrained RL offers a scalable and adaptive solution for real-time coexistence optimization in dynamic wireless networks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1120-1137"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11579434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148494636","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":"Constrained Deep Reinforcement Learning for Cost-Efficient and Timely Packet Delivery","authors":"Ozan Aygün;V. Norman Vitale;Antonia M. Tulino;Hao Feng;Elza Erkip;Jaime Llorca","doi":"10.1109/TMLCN.2026.3742244","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3742244","url":null,"abstract":"Next-generation networks are expected to support real-time interactive services in the context of physical system automation (e.g., smart factory, autonomous vehicles) as well as augmented consumer experiences (e.g., AR/VR/XR). A key challenge in enabling such services is to ensure that packets are delivered within strict per-packet deadlines while minimizing network resource usage. Existing network control approaches, including Lyapunov-based algorithms and deep reinforcement learning (DRL) techniques, are not well suited to the distinct queueing dynamics of delay-constrained systems or the need for cost-efficient operation under timely throughput guarantees. In this paper, we formalize the minimum-cost delay-constrained network control (MDNC) problem as a constrained Markov decision process (CMDP), referred to as Markovian MDNC, and address it through a primal-dual Constrained DRL (CDRL) framework. We propose MA-CDNC (Multi-Agent Cost-Efficient Delay-Constrained Network Control), a multi-agent CDRL solution that couples centralized routing with distributed scheduling to meet timely throughput constraints while minimizing overall operational cost. Simulation results demonstrate that MA-CDNC consistently outperforms state-of-the-art baselines, achieving lower resource allocation costs while meeting target timely throughput levels. Furthermore, we show that carefully designing agent decision spaces and tuning system parameters yields substantial performance improvements, offering practical guidance for scalable, learning-based control of latency-sensitive services.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1588-1609"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11733697","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188817","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":"Optimal Stopping-Based Online Node Selection for Multi-Parameter Federated Learning in IoT Networks Under Multiuser Interference","authors":"Moncef Zarrouk;Faissal El Bouanani;Zakaria El Allali","doi":"10.1109/TMLCN.2026.3659060","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3659060","url":null,"abstract":"Federated Learning (FL) is a promising approach that enables multiple devices to collaboratively train a machine learning model over a wireless IoT network. It maintains data privacy by providing only model updates, rather than the original data. Nevertheless, the primary challenge remains in the selection of the most suitable clients to participate in each training round, which is influenced by resource constraints and the quality of the central entity–client link, including multiuser interference (MUI). Existing FL node selection schemes either rely on single-metric heuristics (e.g., based solely on local model accuracy or on link quality) or on complex Reinforcement learning (RL)/AutoFL controllers that are difficult to train and analyze under wireless interference. This leaves a gap for lightweight, analytically tractable schemes that jointly exploit model quality and link reliability in interfered IoT networks. In the presence of MUI at each device, this paper exploits optimal stopping theory (OST) to develop a novel online node selection strategy that combines the local model accuracy (LMA) and the received Signal-to-Interference-plus-Noise Ratio (SINR) into a single metric with an adjustable weight <inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>. The system mandates that the LMA and SINR of each selected node exceed specific thresholds. The probability of selecting at least <inline-formula> <tex-math>$m$ </tex-math></inline-formula> nodes from the existing set is maximized by conducting a comprehensive analysis to determine the optimal parameter <inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>, whereas an extensive discussion regarding the impact of various parameters on the system’s efficiency has been provided. The proposed OST-based approach obtains superior global model accuracy (GMA) and convergence in comparison to baseline methods, with a lower system complexity, as evidenced by semi-synthetic experiments that utilize the CRAWDAD kth/rss dataset.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"391-408"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148492303","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":"RLSR: A Reinforcement Learning-Based Segment Routing for QoS-Aware Software-Defined Network","authors":"Wang Wumian;Sajal Saha;Anwar Haque;Greg Sidebottom","doi":"10.1109/TMLCN.2026.3693163","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3693163","url":null,"abstract":"Traffic routing is crucial for the efficient operation of the Internet. As network traffic grows, adaptive and intelligent routing algorithms are needed to meet various Quality of Service (QoS) requirements. Reinforcement Learning (RL)-based routing approaches have demonstrated superior performance over traditional methods. In this study, we propose RL-based Segment Routing (SR), called RLSR, a QoS-aware routing algorithm for Software-Defined Networking (SDN). Our approach ensures loop-free path exploration and leverages learned network QoS states to accelerate convergence when optimizing paths for multiple traffic demands. By integrating SR, RLSR enables flow-based source packet routing while reducing communication overhead between the SDN controller and network switches. Experimental evaluations demonstrate that RLSR improves load balancing by reducing the maximum link utilization from 71.67% to 50% compared with a non–RL baseline. In addition, the proposed knowledge-reuse mechanism accelerates learning, reducing the total number of training episodes by up to 27% compared with a baseline RL configuration without knowledge reuse. Further analysis shows that a higher discount factor (<inline-formula> <tex-math>$gamma = 0.9$ </tex-math></inline-formula>) leads to the fastest convergence among the tested settings. These findings highlight the effectiveness of RLSR in optimizing traffic engineering and network performance in dynamic environments.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"813-829"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11520440","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003521","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":"Toward Power-Aware Federated Activity Detection in Cell-Free Massive MIMO-Based mMTC Networks","authors":"Ali Elkeshawy;Wael Jaafar;Haïfa Farès;Amor Nafkha","doi":"10.1109/TMLCN.2026.3716158","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3716158","url":null,"abstract":"The rapid growth of massive machine-type communications (mMTC), combined with advances in edge intelligence, is paving the way for low-latency, low-overhead connectivity. However, the sporadic nature of device activity in mMTC scenarios calls for efficient methods to determine which devices are active at any given time. This motivates collaborative learning within a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture, where the wide geographical distribution of access points (APs) and their joint coordination make distributed learning efficient and secure. Consequently, federated learning (FL) emerges as a promising solution. Indeed, FL enables participants to train a shared model without exchanging raw local data, thereby enhancing data privacy at the AP side and lowering the fronthaul load while leveraging heterogeneous, location-dependent data. The present study proposes a novel FL framework where the CF-mMIMO participants are APs. Due to differences in device behavior, mobility patterns, and environmental factors across the network, the data collected at each AP is often non-independent and non-identically distributed (non-IID). This heterogeneity slows down the convergence of standard FL training and increase variability among client updates, particularly under heterogeneous radio feature distributions. To address this, we propose a client selection strategy that prioritizes APs based on their average received signal power. Our approach shows competitive performance compared to baseline methods, while also addressing the scalability and privacy requirements of mMTC systems. Furthermore, our study analyzes the fairness achieved by APs across devices and presents a representative, percentage-scale analysis of power-consumption gains relative to detection performance when some APs are dropped (i.e., taken out of service), examining two AP-dropping strategies. These results bring valuable insights and set guidelines towards the implementation of FL-based activity detection in CF-mMIMO networks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1261-1278"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11621982","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747979","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":"GNN-Enhanced Routing in SD-WAN Over Realistic Space–Ground Networks With Constellation Dynamics and Weather-Driven Link Variability","authors":"Mengyao Li;Giacomo Sguotti;Sebastian Troia;Jean Pierre Asdikian;Guido Maier","doi":"10.1109/TMLCN.2026.3732630","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3732630","url":null,"abstract":"The integration of LEO satellite networks into Software-Defined Wide Area Networks (SD-WANs) enables near-global coverage and low latency, but also introduces challenges due to highly dynamic topologies, capacity-limited satellite access, and time-varying delays. Conventional reactive SD-WAN traffic steering mechanisms struggle to cope with these dynamics, particularly for latency-sensitive applications. This paper proposes a learning-based routing framework for Space-Ground Integrated Networks deployed at the SD-WAN customer-premises equipment. The framework performs per-request end-to-end path selection based on network state, including residual link capacities and propagation delays. Routing decisions are learned using a Double Deep Q-Network (DDQN) augmented with a Graph Neural Network (GNN) to capture the time-varying satellite–terrestrial topology. The proposed approach is evaluated on Iridium-NEXT constellation using real Two-Line Element (TLE) data, together with real-world delay and capacity measurements from the <monospace>Wetlinks</monospace> dataset. Results show that the GNN–DDQN model generalizes well to satellite configurations and traffic demands, achieving up to a 16.5% reduction in end-to-end delay compared to a KNN baseline. Additional experiments under clear-sky, rainy, and extreme rainfall conditions demonstrate the robustness of the proposed framework in capacity-constrained regimes.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1457-1473"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11686055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985660","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":"Extracting Bluetooth Fingerprints From Hardware Impairments Through Unsupervised Learning","authors":"Haytham Albousayri;Bechir Hamdaoui;Weng-Keen Wong","doi":"10.1109/TMLCN.2026.3736373","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3736373","url":null,"abstract":"The Internet of Things (IoT) has enabled massive connectivity through low-cost devices, but their limited hardware often introduces impairments—such as carrier frequency offset (CFO), phase noise, and IQ imbalance—that degrade communication reliability if not accurately estimated and compensated. Beyond their mitigation, these impairments exhibit device-specific stationary distributions, underscoring their potential as distinctive signatures for radio frequency fingerprinting (RFFP). In this work, we propose an unsupervised learning framework for jointly estimating key RF hardware impairments, eliminating the need for ground-truth impairment values. The estimated impairments are then leveraged as robust, environment-invariant features for device identification. Our approach is validated using real-world measurements from Bluetooth Low Energy (BLE) devices. The first set of results demonstrates the robustness of our model in accurately estimating impairments across a variety of challenging scenarios, achieving up to a <inline-formula> <tex-math>$60times $ </tex-math></inline-formula> reduction in mean squared error and approximately 68% lower per-impairment execution time compared to other baselines. The second set of results evaluates the effectiveness of the estimated impairments for RFFP, showing that they provide a lightweight yet robust features representation, achieving an average improvement of 20–85% in classification accuracy across domain shifts.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1527-1549"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11703746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131167","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":"Reliable and Energy-Efficient MAC Protocols in Industrial IoT Networks via Multi-Agent Reinforcement Learning","authors":"Luciano Miuccio;Daniela Panno;Salvatore Riolo;Anita Schilirò","doi":"10.1109/TMLCN.2026.3683651","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3683651","url":null,"abstract":"The transition from wired to wireless communications in industrial Internet of Things (IIoT) networks introduces stringent challenges in terms of reliability and energy efficiency, aggravated by harsh propagation conditions and contention for a shared radio medium. These constraints require advanced medium access control (MAC) protocols capable of jointly managing channel access, packet retransmissions, and buffer operations while accounting for the battery limitations of IoT devices (IoTDs). This paper proposes a multi-agent reinforcement learning (MARL) framework for the autonomous design of energy-efficient and reliable MAC protocols in uplink wireless IIoT networks supporting time–frequency multiplexing. Moving away from conventional decentralized partially observable Markov decision process (Dec-POMDP)-based MARL designs, the framework adopts a partially observable Markov game (POMG), thereby enabling per-device policy learning. A novel reward mechanism is introduced, in which the base station broadcasts a resource-level feedback, and each device constructs a local reward based solely on its own observations and past actions, ensuring feasibility in real deployments. Simulation results show that the proposed framework achieves maximum reliability, whereas state-of-the-art MARL benchmarks based on global rewards fail to meet the required target, highlighting the importance of POMG modeling and local reward structures for reliable wireless IIoT networks. Furthermore, a comparison with conventional grant-based protocols, which inherently achieve maximum reliability, demonstrates that the proposed solution significantly reduces the active-mode duration, thereby improving overall energy efficiency.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"677-705"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11481086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737179","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":"Deep Reinforcement Learning-Based Scheduling for Wi-Fi Multi-Access Point Coordination","authors":"David Nunez;Francesc Wilhelmi;Maksymilian Wojnar;Katarzyna Kosek-Szott;Szymon Szott;Boris Bellalta","doi":"10.1109/TMLCN.2026.3682239","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3682239","url":null,"abstract":"Multi-access point coordination (MAPC) is a key feature of IEEE 802.11bn, with a potential impact on future Wi-Fi networks. MAPC enables joint scheduling decisions across multiple access points (APs) to improve throughput, latency, and reliability in dense Wi-Fi deployments. However, implementing efficient scheduling policies under diverse traffic and interference conditions in overlapping basic service sets (OBSSs) remains a complex task. This paper presents a method to minimize the network-wide worst-case latency by formulating MAPC scheduling as a sequential decision-making problem and proposing a deep reinforcement learning (DRL) mechanism to minimize worst-case delays in OBSS deployments. Specifically, we train a DRL agent using proximal policy optimization (PPO) within an 802.11bn-compatible Gymnasium environment. This environment provides observations of queue states, delay metrics, and channel conditions, enabling the agent to schedule multiple AP-station pairs to transmit simultaneously by leveraging spatial reuse (SR) groups. Simulations demonstrate that our proposed solution outperforms state-of-the-art heuristic strategies across a wide range of network loads and traffic patterns. The trained machine learning (ML) models consistently achieve lower 99th-percentile delays, showing up to a 30% improvement over the best baseline.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"744-757"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11478468","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796246","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":"ML-Enabled Dynamic Duplexing for Diverse Traffic Scenarios: A Deep Reinforcement Learning Approach","authors":"Masoumeh Mokhtari;Petteri Kela;Guillermo Pocovi;Roberto Maldonado;Klaus I. Pedersen","doi":"10.1109/TMLCN.2026.3702995","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3702995","url":null,"abstract":"The emergence of different traffic types with diverse requirements, such as eXtended Reality (XR), in 5G-Advanced and upcoming 6G networks, requires more intelligent duplexing strategies. Sub-band full duplex (SBFD) enables simultaneous uplink (UL) and downlink (DL) transmissions over an unpaired spectrum, thereby providing more frequent UL opportunities. While SBFD has strong potential to improve UL performance without risking DL performance, static radio frame configurations are inherently suboptimal under dynamic traffic conditions. In such cases, dynamic radio frame configurations are required to adapt to varying traffic demands and service requirements and fully realize the benefits of SBFD. This paper proposes an ML-enabled dynamic radio frame selection framework based on a Soft Actor-Critic (SAC) reinforcement learning agent with a two-step bootstrap mechanism. A bootstrapped general model is trained across varied traffic loads and packet sizes to ensure scalability and robustness. System-level simulations in dense urban deployments demonstrate that the bootstrap generalized model achieves good performance, while maintaining adaptability to unseen traffic conditions. Compared with conventional static time-division duplexing (TDD) baseline with “DDDSU”, the proposed scheme increases UL capacity by up to 268% for large-packet file transfer protocol model 3 (FTP3) traffic and 195% for small packets. For XR services, the ML-based solution extends UL capacity by 25%, confirming its effectiveness in supporting latency-sensitive traffic scenarios. The simulation results further indicate that SBFD can deliver a high satisfaction ratio for XR users by ensuring sufficient throughput for cell-edge users.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1028-1041"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11563648","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443410","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}