Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Synthetic Data Meets Real-World Challenges: Transfer Learning for Driver Pose Estimation Using RAMSIS","authors":"Junjie Gou,&nbsp;Xian Wu,&nbsp;Jianwang Shao","doi":"10.1002/ett.70240","DOIUrl":"https://doi.org/10.1002/ett.70240","url":null,"abstract":"<div>\u0000 \u0000 <p>Driver posture recognition holds significant application value in intelligent driving and human-machine interaction. However, the difficulty in acquiring driving posture data and the high labeling costs result in large errors in posture recognition models, which severely limit the development of related algorithm research. This paper presents a driving posture transfer learning method based on the RAMSIS synthetic dataset, which can effectively reduce algorithm development costs and significantly improve model performance. First, we constructed a synthetic dataset based on RAMSIS, which includes images of 21 typical driving postures from 54 human body models and corresponding 3D keypoint ground truth labels. The data generation method efficiently acquires sample data, saving substantial labeling efforts, while also incorporating constraints from the automotive ergonomics environment to enhance the realism of the synthetic data—a crucial aspect in dataset construction. Next, we applied a pre-trained model to generate keypoint pseudo-labels for real vehicle driving images, which were then combined with the synthetic data to form a mixed dataset. Finally, we employed a fine-tuning strategy to perform transfer learning on the pre-trained model using the mixed dataset, and explored the impact of synthetic data proportion on model performance. Experimental results show that the proposed method achieves an optimal MPJPE of 30.4 mm, significantly outperforming other models. This provides new data support for driving posture estimation, with excellent model performance in posture recognition, thus offering potential for quantitative posture evaluation. It lays the foundation for broader applications in vehicle human-machine interaction and demonstrates promising engineering prospects.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delay Sensitive Music Transmission Architecture for 5G VANET: Integrated Network Slicing and Predictive Beamforming","authors":"Yilin Wan","doi":"10.1002/ett.70246","DOIUrl":"https://doi.org/10.1002/ett.70246","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid development of 5G-enabled vehicular ad hoc network (VANET) has opened new possibilities for real-time multimedia services, including music streaming in intelligent transmission systems. However, maintaining seamless, low-latency music transmission in high-mobility environments remains an issue. Traditional VANE suffer from improved packet loss, jitter, and transmission delays due to unpredictable vehicular movement, fluctuating network conditions, and inefficient resource allocation. It proposes a delay-sensitive music transmission architecture for 5G VANET by combining network slicing (NS) and predictive beamforming to increase real-time streaming efficiency. The primary goal is to decrease transmission latency, increase signal stability, and optimize resource allocation for seamless music playback in a dynamic vehicular environment. The proposed architecture utilizes a twofold approach such as NS allocating a dedicated ultra-reliable low-latency communication (URLLC) slice for music transmission, with a quality of service (QoS)-aware manager adjusting parameters to ensure low latency. Secondly, the adaptive radial movement optimized intelligent long short-term memory network- (ARMO-IntelliLSTM) based deep learning (DL) model predicts vehicle trajectories, enabling the system to preadjust beamforming parameters for continuous signal stability. The multiple-input, multiple-output- (mMIMO) based beamforming module dynamically adapts beam angle and handoff decisions on real-time channel state information. Simulation results demonstrate the effectiveness of the proposed architecture in reducing latency (3 ms), jitter (2.7 ms), and in increasing packet delivery ratio (PDR) (98.3%), beamforming accuracy (95%), handoff success rate (98.5%), and throughput (110 Mbps). Finally, integrating NS and predictive beamforming offers a robust solution for delay-sensitive music transmission in 5G VANET.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of High-Definition Music Streaming VANET Broadcast Protocol for QoS","authors":"Xinbei Shi","doi":"10.1002/ett.70248","DOIUrl":"https://doi.org/10.1002/ett.70248","url":null,"abstract":"<div>\u0000 \u0000 <p>The high-speed and the growing popularity of vehicular ad hoc networks (VANETs) and the ever-growing need to carry high-quality Multimedia Services, especially high-definition (HD) streaming music, have extensively compounded the pressure on efficient communication mechanisms. Established VANET broadcast protocols can hardly allow the low latency, high data transmission rates, and low packet loss demanded by continuous music streaming. In order to overcome these challenges, an optimized VANET broadcast protocol was deployed, which includes machine learning and superior optimization algorithms to increase the quality of service (QoS). A predictive Dynamic Transient Search Optimizer-driven Categorical Boosting (DTS-CatBoost) model is introduced to anticipate network congestion by analyzing traffic patterns, enabling proactive transmission adjustments. For network congestion control and routing optimization, DTS is employed to dynamically select the most stable broadcast nodes, optimizing data dissemination paths. Furthermore, the protocol leverages forward error correction (FEC), which is integrated to enhance data reliability in high-mobility scenarios. The proposed method is implemented using Python 3.10.1. Key performance metrics include packet delivery ratio (PDR), end-to-end latency, bandwidth utilization, protocol overhead, and playback smoothness. Experimental results demonstrate that the suggested model DTS-CatBoost significantly improves QoS, reducing playback interruptions, enhancing data transmission efficiency, and ensuring seamless HD music streaming in vehicular networks. It highlights the potential use of AI-driven adaptive streaming algorithms in transforming multimedia transmission across VANETs, paving the way for scalable and reliable streaming solutions in next-generation intelligent transportation systems.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145062697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Optimized Channel Compression-Reconstruction Network for RF Chain Selection and Channel Estimation in mm-Wave MIMO Systems","authors":"Ch V. V. S. Srinivas,&nbsp;Somasekhar Borugadda","doi":"10.1002/ett.70235","DOIUrl":"https://doi.org/10.1002/ett.70235","url":null,"abstract":"<div>\u0000 \u0000 <p>A millimeter-wave (mm-wave) massive multiple input multiple output (MIMO) system is considered the safest technique to improve data rate and maintain high communication reliability for future wireless systems. Several studies have attempted to develop a model for improving the power and spectral efficiency of mmWave massive MIMO systems. However, they failed due to the inefficiency of mediating extensive communications. Therefore, this research presents an effective mmWave massive MIMO by applying standard methods to maximize the overall system performance. The main goal of this research is to select the Radio Frequency (RF) chains optimally using the Hybrid Differential Evolution Firefly Optimization (DEFO) algorithm. Then, a strong auto-encoder-driven channel compression network (CCN) and a channel reconstruction network (CRN) model are proposed to perform the channel estimation for RF chain selection. In addition, the quantum beetle swarm optimization (QBSO) algorithm is developed to tune the parameters of CCN and CRN models to accomplish higher precision and faster convergence speed. In the experimental scenario, the efficiency of the proposed model is demonstrated by evaluating and comparing the performance calculations with existing methods. The analysis verified that the proposed model accomplishes higher spectral efficiency of 5.345 bits/s/Hz and 8.67 bps/Hz/W energy efficiency, respectively, for the mmWave massive MIMO system.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145037895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Handling Class Imbalance in SAGIN Heterogeneous Devices via Location-Slack-Fuzzy Broad Learning System","authors":"Suyan Yao,&nbsp;Song Sun,&nbsp;Yang Zhang,&nbsp;Chuanyun Xu,&nbsp;Wuxing Chen","doi":"10.1002/ett.70242","DOIUrl":"https://doi.org/10.1002/ett.70242","url":null,"abstract":"<div>\u0000 \u0000 <p>In the space-air-ground integrated network (SAGIN) scenario, due to the integration of remote sensors, unmanned aerial vehicles, and satellite-ground communications, heterogeneous and highly imbalanced data frequently appear, which poses huge challenges to learning algorithms. While broad learning system (BLS) is efficient, its least squares optimization struggles with SAGIN's imbalanced and noisy distributions. To address these problems, we propose an imbalance-aware slack factor fuzzy broad learning system (ISFFBLS). The method introduces position information to guide model training. To enhance the impact of minority class data, we construct a fuzzy weighted least squares classifier that assigns weights to training samples through a fuzzy membership matrix. Then, a dynamic adjustment mechanism evaluates the classification difficulty of each sample and updates its weight accordingly. The position parameter controls the weight distribution of majority class samples. Finally, to ensure the optimality and stability of the classification boundary, we develop an iterative optimization framework to further optimize the slack factor and fuzzy membership until convergence. Experiments on 18 imbalanced datasets show that ISFFBLS performs better than recent imbalanced learning methods, especially in identifying minority class samples.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145037853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HA-ESNet: A Hierarchical Attention With Echo State Network-Based Dynamic Low-Complexity Channel Estimation in FSO Communication Links Under Turbulent Channel Conditions","authors":"M. R. Kavitha,&nbsp;M. R. Geetha,&nbsp;T. Rajesh","doi":"10.1002/ett.70236","DOIUrl":"https://doi.org/10.1002/ett.70236","url":null,"abstract":"<div>\u0000 \u0000 <p>In today's rapidly evolving communication landscape, free space optical (FSO) communication systems face significant challenges when operating under atmospheric turbulence conditions. The specific characteristics of gamma–gamma turbulence introduce signal fading, scintillation, and potential link failures, impacting the reliability and performance of data transmission. To ensure high-quality and reliable communication in such challenging environments, there is a critical need for low-complexity parameter estimation techniques with low bit error rate (BER) and mean square error (MSE). Addressing these challenges, this paper proposes a low-complexity channel estimation design named hierarchical attention echo state network (HA-ESNet) model over gamma–gamma turbulence channels in FSO communications. The HA-ESNet model leverages advanced deep learning techniques, attention mechanisms, and the echo state network (ESN) architecture to enhance parameter estimation accuracy and robustness. The hierarchical attention mechanism allows the network to selectively focus on informative channel characteristics while suppressing noise and irrelevant information. This selective attention enables the model to prioritize critical features and adapt to changing channel conditions effectively. The HA-ESNet model's unique architecture combines the benefits of hierarchical attention mechanisms and ESN components to optimize signal transmission, adapt to channel variability, and improve training efficiency. By capturing the nonlinear dynamics of FSO channels through reservoir computing with echo state properties, the HA-ESNet model can effectively model and adapt to the complex turbulence-induced dynamics. Simulation results demonstrate the strong performance of the HA-ESNet model in estimating parameters over turbulent FSO channels. The model achieves low BER, low MSE, and minimal computational complexity, showcasing its robustness and adaptability in capturing the dynamics of turbulent channels. The innovative approach of HA-ESNet significantly enhances the reliability and performance of FSO communication systems in challenging atmospheric conditions, offering a promising solution for improving data transmission in FSO networks.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145037896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Passive Radar Signal Sorting Method Based on Improved Information Entropy","authors":"Xiaopeng Huang,&nbsp;Xianjun Zhu,&nbsp;Pengfei Bao,&nbsp;Xianzhong Zhou","doi":"10.1002/ett.70245","DOIUrl":"https://doi.org/10.1002/ett.70245","url":null,"abstract":"<div>\u0000 \u0000 <p>Depending on expert experience to assign weights is a traditional passive radar signal sorting method, including passive radiation source information (such as signal source azimuth, pulse width, repetition period, carrier frequency, etc.). However, this method is influenced by subjective experience, and manual experience methods have disadvantages such as strong subjectivity and high arbitrariness. In response to the problem of excessive expert experience in traditional passive radar signal sorting, this study proposes an improved information entropy method to calculate the weights of relevant information from passive radiation source information. The improved information entropy method has the characteristics of theoretical perfection, qualitative and quantitative combination, and scientific weight setting. This article first uses existing data to calculate the initial weight matrix of the associated information. Second, it obtains the associated weights based on the initial weight matrix. Once again, it proposes conflict mitigation strategies to eliminate inconsistencies. Then it performs correlation verification on the radiation source information to improve its practicality. Finally, this study was experimentally validated using the expert assignment method, and the experimental results showed the effectiveness and optimization of this method.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145037893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Industrial Internet of Things Cyber Threats Detection Through Deep Feature Learning and Stacked Sparse Autoencoder Based Classification","authors":"R. Vijay Anand,&nbsp;G. Magesh,&nbsp;I. Alagiri,&nbsp;Madala Guru Brahmam,&nbsp;C. Senthil Kumar,&nbsp;M. Kesavan,&nbsp;Azween Bin Abdullah","doi":"10.1002/ett.70224","DOIUrl":"https://doi.org/10.1002/ett.70224","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent times, the industrial system has integrated with industrial Internet of Things (IoT) applications to enable the ease of production process and ensure worker safety. Security is a major concern in the industrial Internet of Things (IIoT) environment owing to the distributed nature of architecture and dynamic traffic flows. Generally, the cyber-attack detection model is classified as misuse and anomaly detection. The misuse detection method is employed based on the concept of signature matching, and the anomaly method is based on the detection of known and unknown attacks. Present security models have realized the issue of over-fitting, low classification accuracy, and a high false positive rate when given a massive volume of network traffic data. The proposed work focused on “IIoT cyber-attack detection using lightweight hybrid deep learning algorithm” to identify intrusion. At first, the data imbalance problem is resolved through the Euclidean-based synthetic minority oversampling technique (EbSmoT) to prevent the model from becoming biased toward one class. Then, the Information Gain and Fisher score-based technique (IG-FST) is employed to eliminate redundant features and avoid overfitting problems during training. Moreover, the Bi-LSTM ResNet-based convolutional autoencoder (BR-CAE) is executed to obtain higher-level feature representation. Finally, a Stacked Sparse autoencoder-based Particle Swarm Probabilistic Neural Network (SAE-PSPNN) is used for attack detection and classification. The performance of the proposed method can be evaluated using several performance metrics through two different datasets, such as the UNSW-NB15 dataset and the ToN_IoT dataset. The proposed framework achieved an accuracy of 99.86% on the ToN_IoT dataset and 99.62% on the UNSW-NB15 dataset.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145037894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Decentralized Secure Blockchain Model and Lightweight Cryptosystem for Enhancing the EHR Data Security","authors":"Jyothy Sondekola Tippeswamy,&nbsp;Mrinal Sarvagya","doi":"10.1002/ett.70243","DOIUrl":"https://doi.org/10.1002/ett.70243","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>An electronic health record (EHR) is a sensitive collection of data about the health of many people in a smart healthcare system. Because of the importance of persons in smart healthcare systems, an effective security architecture is critical for maintaining privacy in Electronic Health Records (EHRs) databases. EHRs are often stored on centralized servers, which increases the risk of security breaches and necessitates trust in a single authority that cannot adequately protect data from internal threats.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This research focuses on protecting patient privacy and data security during sensitive data transmission between healthcare providers for diagnosis. This paper describes a methodology for developing a revolutionary decentralized, secure blockchain model and lightweight cryptosystem to improve EHR data security.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The EHR data are initially collected from the dataset, and the user transaction information is initialized for communication between multiple nodes. Here, the medical data are double encrypted using the Integrated ElGamal Hyper Elliptic cryptosystem (IntEH), whereas the public and private keys can be generated using an elliptic scheme to enhance the security of health data. For added protection, the encrypted data is held on the off-chain Inter Planetary File System (OIPFS) blockchain.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>For the proposed model, several performance measures are examined, including key generation time, execution time, encryption time, and throughput. Compared to other existing models, the proposed model can achieve a lower key generation time of 152 s for the healthcare dataset.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The analytical results show the performance of the proposed design, which reflects the proposed strategy, which gives a lower delay rate in milliseconds and a higher data loss ratio at various patient block counts.</p>\u0000 </section>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-Enhanced Bidirectional LSTM for Accurate False Data Injection Attack Detection in Smart Grid","authors":"Hassam Ishfaq,&nbsp;Waqas Amin,&nbsp;Sadia Ashfaq,&nbsp;Nermish Mushtaq,&nbsp;Xuyang Shi","doi":"10.1002/ett.70238","DOIUrl":"https://doi.org/10.1002/ett.70238","url":null,"abstract":"<div>\u0000 \u0000 <p>Due to an increase in the integration of renewable energy sources in the smart grid, the importance of the smart grid is increasing day by day. Moreover, the decentralization of the smart grids makes them prone to cyberattacks such as Distributed denial of service (DDOS) attacks, False data injection attacks (FDIA), and so forth. These attacks raise serious concerns about the integrity and stability of a smart grid. Therefore, the detection of these attacks has a prominent impact on the stability of a smart grid. For this purpose, the presented work proposes a robust detection system that leverages the fusion of Bidirectional Long Short-Term Memory (Bi-LSTM) networks with an Attention Mechanism. The proposed architecture of Bi-LSTM captures both forward and backward temporal dependencies in sensor data, enhancing the model's ability to detect anomalies that cause FDIA in time-series data. So, it amplifies the efficiency of the proposed model by bringing about stress on the most emphatic time steps while enhancing interpretability and accuracy classification (classification accuracy). The proposed model is evaluated on a time-series smart grid data set, and the experimental results have been compared with the other state-of-the-art techniques, such as LSTM-CNN and LSTM-Autoencoder. The results clearly demonstrate that the proposed model is capable enough to identifying the anomaly with an accuracy rate of about 92.32%.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}