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

{"title":"Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification","authors":"Eslam Eldeeb;Mohammad Shehab;Hirley Alves;Mohamed-Slim Alouini","doi":"10.1109/TMLCN.2025.3557734","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3557734","url":null,"abstract":"Semantic and goal-oriented (SGO) communication is an emerging technology that only transmits significant information for a given task. Semantic communication encounters many challenges, such as computational complexity at end users, availability of data, and privacy-preserving. This work presents a TinyML-based semantic communication framework for few-shot wireless image classification that integrates split-learning and meta-learning. We exploit split-learning to limit the computations performed by the end-users while ensuring privacy-preserving. In addition, meta-learning overcomes data availability concerns and speeds up training by utilizing similarly trained tasks. The proposed algorithm is tested using a data set of images of hand-written letters. In addition, we present an uncertainty analysis of the predictions using conformal prediction (CP) techniques. Simulation results show that the proposed Semantic-MSL outperforms conventional schemes by achieving a <inline-formula> <tex-math>${20} %$ </tex-math></inline-formula> gain in classification accuracy using fewer data points yet less training energy consumption.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"491-501"},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10948463","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817929","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":"Spatio-Temporal Predictive Learning Using Crossover Attention for Communications and Networking Applications","authors":"Ke He;Thang Xuan Vu;Lisheng Fan;Symeon Chatzinotas;Björn Ottersten","doi":"10.1109/TMLCN.2025.3555975","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3555975","url":null,"abstract":"This paper investigates the spatio-temporal predictive learning problem, which is crucial in diverse applications such as MIMO channel prediction, mobile traffic analysis, and network slicing. To address this problem, the attention mechanism has been adopted by many existing models to predict future outputs. However, most of these models use a single-domain attention which captures input dependency structures only in the temporal domain. This limitation reduces their prediction accuracy in spatio-temporal predictive learning, where understanding both spatial and temporal dependencies is essential. To tackle this issue and enhance the prediction performance, we propose a novel crossover attention mechanism in this paper. The crossover attention can be understood as a learnable regression kernel which prioritizes the input sequence with both spatial and temporal similarities and extracts relevant information for generating the output of future time slots. Simulation results and ablation studies based on synthetic and realistic datasets show that the proposed crossover attention achieves considerable prediction accuracy improvement compared to the conventional attention layers.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"479-490"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817995","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":"Neuromorphic Wireless Split Computing With Multi-Level Spikes","authors":"Dengyu Wu;Jiechen Chen;Bipin Rajendran;H. Vincent Poor;Osvaldo Simeone","doi":"10.1109/TMLCN.2025.3556634","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3556634","url":null,"abstract":"Inspired by biological processes, neuromorphic computing leverages spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have shown that embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption. To scale neuromorphic computing to larger workloads, split computing—where an SNN is partitioned across two devices—is a promising solution. In such architectures, the device hosting the initial layers must transmit information about the spikes generated by its output neurons to the second device. This establishes a trade-off between the benefits of multi-level spikes, which carry additional payload information, and the communication resources required for transmitting extra bits between devices. This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs. We propose digital and analog modulation schemes for an orthogonal frequency division multiplexing (OFDM) radio interface to enable efficient communication. Simulation and experimental results using software-defined radios reveal performance improvements achieved by multi-level SNN models and provide insights into the optimal payload size as a function of the connection quality between the transmitter and receiver.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"502-516"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10946192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835442","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":"AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model","authors":"Berend J. D. Gort;Godfrey M. Kibalya;Angelos Antonopoulos","doi":"10.1109/TMLCN.2025.3553100","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3553100","url":null,"abstract":"Effective resource management in edge-cloud networks is crucial for meeting Quality of Service (QoS) requirements while minimizing operational costs. However, dynamic and fluctuating workloads pose significant challenges for accurate workload prediction and efficient resource allocation, particularly in resource-constrained edge environments. In this paper, we introduce AERO (Adaptive Edge-cloud Resource Orchestration), a novel lightweight forecasting model designed to address these challenges. AERO features an adaptive period detection mechanism that dynamically identifies dominant periodicities in multivariate workload data, allowing it to adjust to varying patterns and abrupt changes. With fewer than 1,000 parameters, AERO is highly suitable for deployment on edge devices with limited computational capacity. We formalize our approach through a comprehensive system model and extend an existing simulation framework with predictor modules to evaluate AERO’s performance in realistic cloud-edge environments. Our extensive evaluations on real-world cloud workload datasets demonstrate that AERO achieves comparable prediction accuracy to complex state-of-the-art models with millions of parameters, while significantly reducing model size and computational overhead. In addition, simulations show that AERO improves orchestration performance, reducing energy consumption and response times compared to existing proactive and reactive approaches. Our live deployment experiments further validate these findings, demonstrating that AERO consistently delivers superior performance. These results highlight AERO as an effective solution for improving resource management and reducing operational costs in dynamic cloud-edge environments.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"463-478"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10935743","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740375","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":"Closed-Loop Clustering-Based Global Bandwidth Prediction in Real-Time Video Streaming","authors":"Sepideh Afshar;Reza Razavi;Mohammad Moshirpour","doi":"10.1109/TMLCN.2025.3551689","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3551689","url":null,"abstract":"Accurate throughput forecasting is essential for ensuring the seamless operation of Real-Time Communication (RTC) applications. These demands for accurate throughput forecasting become particularly challenging when dealing with wireless access links, as they inherently exhibit fluctuating bandwidth. Ensuring an exceptional user Quality of Experience (QoE) in this scenario depends on accurately predicting available bandwidth in the short term since it plays a pivotal role in guiding video rate adaptation. Yet, current methodologies for short-term bandwidth prediction (SBP) struggle to perform adequately in dynamically changing real-world network environments and lack generalizability to adapt across varied network conditions. Also, acquiring long and representative traces that capture real-world network complexity is challenging. To overcome these challenges, we propose closed-loop clustering-based Global Forecasting Models (GFMs) for SBP. Unlike local models, GFMs apply the same function to all traces enabling cross-learning, and leveraging relationships among traces to address the performance issues seen in current SBP algorithms. To address potential heterogeneity within the data and improve prediction quality, a clustered-wise GFM is utilized to group similar traces based on prediction accuracy. Finally, the proposed method is validated using real-world datasets of HSDPA 3G, NYC LTE, and Irish 5G data demonstrating significant improvements in accuracy and generalizability.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"448-462"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10929655","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716486","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":"Randomized Quantization for Privacy in Resource Constrained Machine Learning at-the-Edge and Federated Learning","authors":"Ce Feng;Parv Venkitasubramaniam","doi":"10.1109/TMLCN.2025.3550119","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3550119","url":null,"abstract":"The increasing adoption of machine learning at the edge (ML-at-the-edge) and federated learning (FL) presents a dual challenge: ensuring data privacy as well as addressing resource constraints such as limited computational power, memory, and communication bandwidth. Traditional approaches typically apply differentially private stochastic gradient descent (DP-SGD) to preserve privacy, followed by quantization techniques as a post-processing step to reduce model size and communication overhead. However, this sequential framework introduces inherent drawbacks, as quantization alone lacks privacy guarantees and often introduces errors that degrade model performance. In this work, we propose randomized quantization as an integrated solution to address these dual challenges by embedding randomness directly into the quantization process. This approach enhances privacy while simultaneously reducing communication and computational overhead. To achieve this, we introduce Randomized Quantizer Projection Stochastic Gradient Descent (RQP-SGD), a method designed for ML-at-the-edge that embeds DP-SGD within a randomized quantization-based projection during model training. For federated learning, we develop Gaussian Sampling Quantization (GSQ), which integrates discrete Gaussian sampling into the quantization process to ensure local differential privacy (LDP). Unlike conventional methods that rely on Gaussian noise addition, GSQ achieves privacy through discrete Gaussian sampling while improving communication efficiency and model utility across distributed systems. Through rigorous theoretical analysis and extensive experiments on benchmark datasets, we demonstrate that these methods significantly enhance the utility-privacy trade-off and computational efficiency in both ML-at-the-edge and FL systems. RQP-SGD is evaluated on MNIST and the Breast Cancer Diagnostic dataset, showing an average 10.62% utility improvement over the deterministic quantization-based projected DP-SGD while maintaining (1.0, 0)-DP. In federated learning tasks, GSQ-FL improves accuracy by an average 11.52% over DP-FedPAQ across MNIST and FashionMNIST under non-IID conditions. Additionally, GSQ-FL outperforms DP-FedPAQ by 16.54% on CIFAR-10 and 8.7% on FEMNIST.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"395-419"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10919124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645337","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":"Paths Optimization by Jointing Link Management and Channel Estimation Using Variational Autoencoder With Attention for IRS-MIMO Systems","authors":"Meng-Hsun Wu;Hong-Yunn Chen;Ta-Wei Yang;Chih-Chuan Hsu;Chih-Wei Huang;Cheng-Fu Chou","doi":"10.1109/TMLCN.2025.3547689","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3547689","url":null,"abstract":"In massive MIMO systems, achieving optimal end-to-end transmission encompasses various aspects such as power control, modulation schemes, path selection, and accurate channel estimation. Nonetheless, optimizing resource allocation remains a significant challenge. In path selection, the direct link is a straightforward link between the transmitter and the receiver. On the other hand, the indirect link involves reflections, diffraction, or scattering, often due to interactions with objects or obstacles. Relying exclusively on one type of link can lead to suboptimal and limited performance. Link management (LM) is emerging as a viable solution, and accurate channel estimation provides essential information to make informed decisions about transmission parameters. In this paper, we study LM and channel estimation that flexibly adjust the transmission ratio of direct and indirect links to improve generalization, using a denoising variational autoencoder with attention modules (DVAE-ATT) to enhance sum rate. Our experiments show significant improvements in IRS-assisted millimeter-wave MIMO systems. Incorporating LM increased the sum rate and reduced MSE by approximately 9%. Variational autoencoders (VAE) outperformed traditional autoencoders in the spatial domain, as confirmed by heatmap analysis. Additionally, our investigation of DVAE-ATT reveals notable differences in the temporal domain with and without attention mechanisms. Finally, we analyze performance across varying numbers of users and ranges. Across various distances—5m, 15m, 25m, and 35m—performance improvements averaged 6%, 11%, 16%, and 22%, respectively.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"381-394"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10909334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583165","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 Novel Multiple Access Scheme for Heterogeneous Wireless Communications Using Symmetry-Aware Continual Deep Reinforcement Learning","authors":"Hamidreza Mazandarani;Masoud Shokrnezhad;Tarik Taleb","doi":"10.1109/TMLCN.2025.3546183","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3546183","url":null,"abstract":"The Metaverse holds the potential to revolutionize digital interactions through the establishment of a highly dynamic and immersive virtual realm over wireless communications systems, offering services such as massive twinning and telepresence. This landscape presents novel challenges, particularly efficient management of multiple access to the frequency spectrum, for which numerous adaptive Deep Reinforcement Learning (DRL) approaches have been explored. However, challenges persist in adapting agents to heterogeneous and non-stationary wireless environments. In this paper, we present a novel approach that leverages Continual Learning (CL) to enhance intelligent Medium Access Control (MAC) protocols, featuring an intelligent agent coexisting with legacy User Equipments (UEs) with varying numbers, protocols, and transmission profiles unknown to the agent for the sake of backward compatibility and privacy. We introduce an adaptive Double and Dueling Deep Q-Learning (D3QL)-based MAC protocol, enriched by a symmetry-aware CL mechanism, which maximizes intelligent agent throughput while ensuring fairness. Mathematical analysis validates the efficiency of our proposed scheme, showcasing superiority over conventional DRL-based techniques in terms of throughput, collision rate, and fairness, coupled with real-time responsiveness in highly dynamic scenarios.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"353-368"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570563","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":"UAV-Assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis","authors":"Ruslan Zhagypar;Nour Kouzayha;Hesham ElSawy;Hayssam Dahrouj;Tareq Y. Al-Naffouri","doi":"10.1109/TMLCN.2025.3546181","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3546181","url":null,"abstract":"The development of the sixth-generation (6G) of wireless networks is driving computation toward the network edge, where Hierarchical Federated Learning (HFL) plays a pivotal role in distributing learning across edge devices. In HFL, edge devices train local models and send updates to an edge server for local aggregation, which are then forwarded to a central server for global aggregation. However, the unreliability of communication channels at the edge and backhaul links poses a significant bottleneck for HFL-enabled systems. To address this challenge, this paper proposes an unbiased HFL algorithm for Uncrewed Aerial Vehicle (UAV)-assisted wireless networks. While applicable to terrestrial base stations (BSs), the proposed algorithm relies on UAVs for local model aggregation thanks to their ability to enhance wireless channels with lower latency and improved coverage. The proposed algorithm adjusts update weights during local and global aggregations at UAVs to mitigate the impact of unreliable channels. To quantify channel unreliability in HFL, stochastic geometry tools are employed to assess success probabilities of local and global model parameter transmissions. Incorporating these metrics aims to mitigate biases towards devices with better channel conditions in UAV-assisted networks. The paper further examines the theoretical convergence of the proposed unbiased UAV-assisted HFL algorithm under adverse channel conditions and highlights the impact of the limited battery capacity of the UAV on the efficiency of the HFL algorithm. Additionally, the algorithm facilitates optimization of system parameters such as UAV count, altitude, battery capacity, etc. The simulation results underscore the effectiveness of the proposed unbiased HFL scheme, demonstrating a 5.5% higher accuracy and approximately 85% faster convergence compared to conventional HFL algorithms. We make our code available at the following GitHub repository: <inline-formula> <tex-math>$texttt {UAV-assisted Unbiased HFL Code}$ </tex-math></inline-formula>.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"420-447"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904929","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645156","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":"On Traffic Prediction With Knowledge-Driven Spatial–Temporal Graph Convolutional Network Aided by Selected Attention Mechanism","authors":"Yuwen Qian;Tianyang Qiu;Chuan Ma;Yiyang Ni;Long Yuan;Xiangwei Zhou;Jun Li","doi":"10.1109/TMLCN.2025.3545777","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3545777","url":null,"abstract":"Intelligent transportation systems grapple with the formidable task of precisely forecasting real-time traffic conditions, where the traffic dynamics exhibit intricacies arising from spatial and temporal dependencies. The urban road network presents a complex web of interconnected roads, where the state of traffic on one road can influence the conditions of others. Moreover, the prediction of traffic conditions necessitates the consideration of diverse temporal factors. Notably, the proximity of a time point to the present moment wields a more substantial impact on subsequent states. In this paper, we propose the knowledge-driven graph convolutional network (KGCN) aided by the gated recurrent unit with a selected attention mechanism (GSAM) to predict traffic flow. In particular, KGCN is employed to capture the correlation of the external knowledge factors for the road and the spatial dependencies, and the gated recurrent unit (GRU) is used to cope with temporal dependence. Furthermore, to improve traffic prediction accuracy, we propose the GRU combined with a selected attention mechanism with Gumble-Max to predict traffic at the temporal dimension, where a selector is chosen to dynamically assign the feature in various time intervals with different weights. Experimental results with real-life data show the proposed KGCN with GSAM can achieve high accuracy in traffic prediction. Compared to the traditional traffic prediction method, the proposed KGCN with GSAM can achieve higher efficacy and robustness when capturing global dynamic temporal dependencies, external knowledge factor correlations, and spatial correlations.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"369-380"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904899","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570620","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}