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

{"title":"Deep Joint Source Channel Coding for Privacy-Aware End-to-End Image Transmission","authors":"Mehdi Letafati;Seyyed Amirhossein Ameli Kalkhoran;Ecenaz Erdemir;Babak Hossein Khalaj;Hamid Behroozi;Deniz Gündüz","doi":"10.1109/TMLCN.2025.3564907","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3564907","url":null,"abstract":"Deep neural network (DNN)-based joint source and channel coding is proposed for privacy-aware end-to-end image transmission against multiple eavesdroppers. Both scenarios of colluding and non-colluding eavesdroppers are considered. Unlike prior works that assume perfectly known and independent identically distributed (i.i.d.) source and channel statistics, the proposed scheme operates under unknown and non-i.i.d. conditions, making it more applicable to real-world scenarios. The goal is to transmit images with minimum distortion, while simultaneously preventing eavesdroppers from inferring certain private attributes of images. Simultaneously generalizing the ideas of privacy funnel and wiretap coding, a multi-objective optimization framework is expressed that characterizes the trade-off between image reconstruction quality and information leakage to eavesdroppers, taking into account the structural similarity index (SSIM) for improving the perceptual quality of image reconstruction. Extensive experiments on the CIFAR-10 and CelebA, along with ablation studies, demonstrate significant performance improvements in terms of SSIM, adversarial accuracy, and the mutual information leakage compared to benchmarks. Experiments show that the proposed scheme restrains the adversarially-trained eavesdroppers from intercepting privatized data for both cases of eavesdropping a common secret, as well as the case in which eavesdroppers are interested in different secrets. Furthermore, useful insights on the privacy-utility trade-off are also provided.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"568-584"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925029","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":"Causally-Aware Reinforcement Learning for Joint Communication and Sensing","authors":"Anik Roy;Serene Banerjee;Jishnu Sadasivan;Arnab Sarkar;Soumyajit Dey","doi":"10.1109/TMLCN.2025.3562557","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3562557","url":null,"abstract":"The next-generation wireless network, 6G and beyond, envisions to integrate communication and sensing to overcome interference, improve spectrum efficiency, and reduce hardware and power consumption. Massive Multiple-Input Multiple Output (mMIMO)-based Joint Communication and Sensing (JCAS) systems realize this integration for 6G applications such as autonomous driving, as it requires accurate environmental sensing and time-critical communication with neighbouring vehicles. Reinforcement Learning (RL) is used for mMIMO antenna beamforming in the existing literature. However, the huge search space for actions associated with antenna beamforming causes the learning process for the RL agent to be inefficient due to high beam training overhead. The learning process does not consider the causal relationship between action space and the reward, and gives all actions equal importance. In this work, we explore a causally-aware RL agent which can intervene and discover causal relationships for mMIMO-based JCAS environments, during the training phase. We use a state dependent action dimension selection strategy to realize causal discovery for RL-based JCAS. Evaluation of the causally-aware RL framework in different JCAS scenarios shows the benefit of our proposed solution over baseline methods in terms of the higher reward. We have shown that in the presence of interfering users and sensing signal clutters, our proposed solution achieves 30% higher data rate in comparison to the communication-only state-of-the-art beam pattern learning method while maintaining sensing performance.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"552-567"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10971373","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896251","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 Learning-Based Data-Assisted Channel Estimation and Detection","authors":"Hamidreza Hashempoor;Wan Choi","doi":"10.1109/TMLCN.2025.3559472","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3559472","url":null,"abstract":"We introduce a novel structure empowered by deep learning models, accompanied by a thorough training methodology, for enhancing channel estimation and data detection in multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Central to our approach is the incorporation of a Denoising Block, which comprises three meticulously designed deep neural networks (DNNs) tasked with accurately extracting noiseless embeddings from the received signal. Alongside, we develop the Correctness Classifier, a classification algorithm adept at distinguishing correctly detected data by leveraging the denoised received signal. By selectively utilizing these identified data symbols as additional pilot signals, we augment the available pilot signals for channel estimation. Our Denoising Block also enables direct data detection, rendering the system well-suited for low-latency applications. To enable model training, we propose a hybrid likelihood objective of the detected symbols. We analytically derive the gradients with respect to the hybrid likelihood, enabling us to successfully complete the training phase. When compared to other conventional methods, experiments and simulations show that the proposed data-aided channel estimator significantly lowers the mean-squared-error (MSE) of the estimation and thus improves data detection performance. Github repository link is <uri>https://github.com/Hamidreza-Hashempoor/5g-dataaided-channel-estimate</uri>.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"534-551"},"PeriodicalIF":0.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875215","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":"Enhancing Routing Performance Through Trajectory Planning With DRL in UAV-Aided VANETs","authors":"Jingxuan Chen;Dianrun Huang;Yijie Wang;Ziping Yu;Zhongliang Zhao;Xianbin Cao;Yang Liu;Tony Q. S. Quek;Dapeng Oliver Wu","doi":"10.1109/TMLCN.2025.3558204","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3558204","url":null,"abstract":"Vehicular Ad-hoc Networks (VANETs) have gained significant attention as a key enabler for intelligent transportation systems, facilitating vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Despite their potential, VANETs face critical challenges in maintaining reliable end-to-end connectivity due to their highly dynamic topology and sparse node distribution, particularly in areas with limited infrastructure coverage. Addressing these limitations is crucial for advancing the reliability and scalability of VANETs. To bridge these gaps, this work introduces a heterogeneous UAV-aided VANET framework that leverages uncrewed aerial vehicles (UAVs), also known as autonomous aerial vehicles, to enhance data transmission. The key contributions of this paper include: 1) the design of a novel adaptive dual-model routing (ADMR) protocol that operates in two modes: direct vehicle clustering for intra-cluster communication and UAV/RSU-assisted routing for inter-cluster communication; 2) the development of a modified density-based clustering algorithm (MDBSCAN) for dynamic vehicle node clustering; and 3) an improved UAV trajectory planning method based on a multi-agent soft actor-critic (MASAC) deep reinforcement learning algorithm, which optimizes network reachability. Simulation results reveal that the UAV trajectory optimization method achieves higher network reachability ratios compared to existing approaches. Also, the proposed ADMR protocol improves the packet delivery ratio (PDR) while maintaining low end-to-end latency. These findings demonstrate the potential to enhance VANET performance, while also providing valuable insights for the development of intelligent transportation systems and related fields.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"517-533"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10951108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845541","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":"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}