Pub Date : 2025-02-17DOI: 10.1109/TMLCN.2025.3542760
Hossein Mehri;Hani Mehrpouyan;Hao Chen
Traffic pattern prediction has emerged as a promising approach for efficiently managing and mitigating the impacts of event-driven bursty traffic in massive machine-type communication (mMTC) networks. However, achieving accurate predictions of bursty traffic remains a non-trivial task due to the inherent randomness of events, and these challenges intensify within live network environments. Consequently, there is a compelling imperative to design a lightweight and agile framework capable of assimilating continuously collected data from the network and accurately forecasting bursty traffic in mMTC networks. This paper addresses these challenges by presenting a machine learning-based framework tailored for forecasting bursty traffic in multi-channel slotted ALOHA networks. The proposed machine learning network comprises long-term short-term memory (LSTM) and a DenseNet with feed-forward neural network (FFNN) layers, where the residual connections enhance the training ability of the machine learning network in capturing complicated patterns. Furthermore, we develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network. Simulation results and complexity analysis demonstrate the superiority of our proposed algorithm in terms of both accuracy and complexity, making it well-suited for time-critical live scenarios. We evaluate the performance of the proposed framework in a network with a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics. Comprehensive evaluations and simulations indicate that our proposed machine learning approach achieves a remarkable 52% higher accuracy in long-term predictions compared to traditional methods, without imposing additional processing load on the system.
{"title":"RACH Traffic Prediction in Massive Machine Type Communications","authors":"Hossein Mehri;Hani Mehrpouyan;Hao Chen","doi":"10.1109/TMLCN.2025.3542760","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3542760","url":null,"abstract":"Traffic pattern prediction has emerged as a promising approach for efficiently managing and mitigating the impacts of event-driven bursty traffic in massive machine-type communication (mMTC) networks. However, achieving accurate predictions of bursty traffic remains a non-trivial task due to the inherent randomness of events, and these challenges intensify within live network environments. Consequently, there is a compelling imperative to design a lightweight and agile framework capable of assimilating continuously collected data from the network and accurately forecasting bursty traffic in mMTC networks. This paper addresses these challenges by presenting a machine learning-based framework tailored for forecasting bursty traffic in multi-channel slotted ALOHA networks. The proposed machine learning network comprises long-term short-term memory (LSTM) and a DenseNet with feed-forward neural network (FFNN) layers, where the residual connections enhance the training ability of the machine learning network in capturing complicated patterns. Furthermore, we develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network. Simulation results and complexity analysis demonstrate the superiority of our proposed algorithm in terms of both accuracy and complexity, making it well-suited for time-critical live scenarios. We evaluate the performance of the proposed framework in a network with a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics. Comprehensive evaluations and simulations indicate that our proposed machine learning approach achieves a remarkable 52% higher accuracy in long-term predictions compared to traditional methods, without imposing additional processing load on the system.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"315-331"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480782","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}
In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as secondary users (SUs) to opportunistically utilize detected “spectrum holes”. Our overall framework consists of three main stages. Firstly, in the model training stage, we explore dataset generation in a multi-cell environment and train a machine learning (ML) model using the federated learning (FL) architecture. Unlike the existing studies on FL for wireless that presume datasets are readily available for training, we propose an end-to-end architecture that directly integrates wireless dataset generation, which involves capturing I/Q samples from over-the-air signals in a multi-cell environment, into the FL training process. To this purpose, we propose a multi-label classification problem for wideband spectrum sensing to detect multiple spectrum holes simultaneously based on the I/Q samples collected locally by the UAVs. In the traditional FL that employs federated averaging (FedAvg) as the aggregating method, each UAV is assigned an equal weight during model aggregation. However, due to the differences in wireless channels observed at each UAV in a multi-cell environment, the received signal powers and collected datasets at different UAV locations could be significantly different, which could degrade the FL performance using equal weights. To address this issue, we propose a proportional weighted federated averaging method (pwFedAvg) in which the aggregating weights are proportional to the received signal powers at each UAV, thereby integrating the intrinsic properties of wireless channels into the FL algorithm. Secondly, in the collaborative spectrum inference stage, we propose a collaborative spectrum fusion strategy that is compatible with the unmanned aircraft system traffic management (UTM) ecosystem. In particular, we improve the accuracy of spectrum sensing results by combining the multi-label classification results from the individual UAVs by performing spectrum fusion at a central server. Finally, in the spectrum scheduling stage, we leverage reinforcement learning (RL) solutions to dynamically allocate the detected spectrum holes to the secondary users. To evaluate the proposed methods, we establish a comprehensive simulation framework that generates a near-realistic synthetic dataset using MATLAB LTE toolbox by incorporating base station (BS) locations in a chosen area of interest, performing ray-tracing, and emulating the primary user’s channel usage in terms of I/Q samples. This evaluation methodology provides a flexible framework to generate large spectrum datasets that could be used for developing ML/AI-based spectrum management solutions for aerial devices.
{"title":"Federated Learning-Based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems","authors":"Sravan Reddy Chintareddy;Keenan Roach;Kenny Cheung;Morteza Hashemi","doi":"10.1109/TMLCN.2025.3540747","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3540747","url":null,"abstract":"In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as secondary users (SUs) to opportunistically utilize detected “spectrum holes”. Our overall framework consists of three main stages. Firstly, in the model training stage, we explore dataset generation in a multi-cell environment and train a machine learning (ML) model using the federated learning (FL) architecture. Unlike the existing studies on FL for wireless that presume datasets are readily available for training, we propose an end-to-end architecture that directly integrates wireless dataset generation, which involves capturing I/Q samples from over-the-air signals in a multi-cell environment, into the FL training process. To this purpose, we propose a multi-label classification problem for wideband spectrum sensing to detect multiple spectrum holes simultaneously based on the I/Q samples collected locally by the UAVs. In the traditional FL that employs federated averaging (FedAvg) as the aggregating method, each UAV is assigned an equal weight during model aggregation. However, due to the differences in wireless channels observed at each UAV in a multi-cell environment, the received signal powers and collected datasets at different UAV locations could be significantly different, which could degrade the FL performance using equal weights. To address this issue, we propose a proportional weighted federated averaging method (pwFedAvg) in which the aggregating weights are proportional to the received signal powers at each UAV, thereby integrating the intrinsic properties of wireless channels into the FL algorithm. Secondly, in the collaborative spectrum inference stage, we propose a collaborative spectrum fusion strategy that is compatible with the unmanned aircraft system traffic management (UTM) ecosystem. In particular, we improve the accuracy of spectrum sensing results by combining the multi-label classification results from the individual UAVs by performing spectrum fusion at a central server. Finally, in the spectrum scheduling stage, we leverage reinforcement learning (RL) solutions to dynamically allocate the detected spectrum holes to the secondary users. To evaluate the proposed methods, we establish a comprehensive simulation framework that generates a near-realistic synthetic dataset using MATLAB LTE toolbox by incorporating base station (BS) locations in a chosen area of interest, performing ray-tracing, and emulating the primary user’s channel usage in terms of I/Q samples. This evaluation methodology provides a flexible framework to generate large spectrum datasets that could be used for developing ML/AI-based spectrum management solutions for aerial devices.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"296-314"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480779","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}
Pub Date : 2025-02-03DOI: 10.1109/TMLCN.2025.3537967
Son Dinh-van;van-Linh Nguyen;Berna Bulut Cebecioglu;Antonino Masaracchia;Matthew D. Higgins
The next generation of wireless systems will leverage the millimeter-wave (mmWave) bands to meet the increasing traffic volume and high data rate requirements of emerging applications (e.g., ultra HD streaming, metaverse, and holographic telepresence). In this paper, we address the joint optimization of beamforming, power control, and interference management in multi-cell mmWave networks. We propose novel reinforcement learning algorithms, including a single-agent-based method (BPC-SA) for centralized settings and a multi-agent-based method (BPC-MA) for distributed settings. To tackle the high-variance rewards caused by narrow antenna beamwidths, we introduce a selective exploration method to guide the agent towards more intelligent exploration. Our proposed algorithms are well-suited for scenarios where beamforming vectors require control in either a discrete domain, such as a codebook, or in a continuous domain. Furthermore, they do not require channel state information, extensive feedback from user equipments, or any searching methods, thus reducing overhead and enhancing scalability. Numerical results demonstrate that selective exploration improves per-user spectral efficiency by up to 22.5% compared to scenarios without it. Additionally, our algorithms significantly outperform existing methods by 50% in terms of per-user spectral effciency and achieve 90% of the per-user spectral efficiency of the exhaustive search approach while requiring only 0.1% of its computational runtime.
{"title":"Reinforcement Learning With Selective Exploration for Interference Management in mmWave Networks","authors":"Son Dinh-van;van-Linh Nguyen;Berna Bulut Cebecioglu;Antonino Masaracchia;Matthew D. Higgins","doi":"10.1109/TMLCN.2025.3537967","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3537967","url":null,"abstract":"The next generation of wireless systems will leverage the millimeter-wave (mmWave) bands to meet the increasing traffic volume and high data rate requirements of emerging applications (e.g., ultra HD streaming, metaverse, and holographic telepresence). In this paper, we address the joint optimization of beamforming, power control, and interference management in multi-cell mmWave networks. We propose novel reinforcement learning algorithms, including a single-agent-based method (BPC-SA) for centralized settings and a multi-agent-based method (BPC-MA) for distributed settings. To tackle the high-variance rewards caused by narrow antenna beamwidths, we introduce a selective exploration method to guide the agent towards more intelligent exploration. Our proposed algorithms are well-suited for scenarios where beamforming vectors require control in either a discrete domain, such as a codebook, or in a continuous domain. Furthermore, they do not require channel state information, extensive feedback from user equipments, or any searching methods, thus reducing overhead and enhancing scalability. Numerical results demonstrate that selective exploration improves per-user spectral efficiency by up to 22.5% compared to scenarios without it. Additionally, our algorithms significantly outperform existing methods by 50% in terms of per-user spectral effciency and achieve 90% of the per-user spectral efficiency of the exhaustive search approach while requiring only 0.1% of its computational runtime.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"280-295"},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10869481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422871","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}
Pub Date : 2025-01-27DOI: 10.1109/TMLCN.2025.3534754
Yangchen Li;Lingzhi Zhao;Tianle Wang;Lianghui Ding;Feng Yang
In this paper, we investigate federated learning (FL) efficiency improvement in practical edge computing systems, where edge workers have non-independent and identically distributed (non-IID) local data, as well as dynamic and heterogeneous computing and communication capabilities. We consider a general FL algorithm with configurable parameters, including the number of local iterations, mini-batch sizes, step sizes, aggregation weights, and quantization parameters, and provide a rigorous convergence analysis. We formulate a joint optimization problem for FL worker selection and algorithm parameter configuration to minimize the final test loss subject to time and energy constraints. The resulting problem is a complicated stochastic sequential decision-making problem with an implicit objective function and unknown transition probabilities. To address these challenges, we propose knowledge/model-driven single-agent and multi-agent deep reinforcement learning (DRL) frameworks. We transform the primal problem into a Markov decision process (MDP) for the single-agent DRL framework and a decentralized partially-observable Markov decision process (Dec-POMDP) for the multi-agent DRL framework. We develop efficient single-agent and multi-agent asynchronous advantage actor-critic (A3C) approaches to solve the MDP and Dec-POMDP, respectively. In both frameworks, we design a knowledge-based reward to facilitate effective DRL and propose a model-based stochastic policy to tackle the mixed discrete-continuous actions and large action spaces. To reduce the computational complexities of policy learning and execution, we introduce a segmented actor-critic architecture for the single-agent DRL and a distributed actor-critic architecture for the multi-agent DRL. Numerical results demonstrate the effectiveness and advantages of the proposed frameworks in enhancing FL efficiency.
{"title":"Knowledge- and Model-Driven Deep Reinforcement Learning for Efficient Federated Edge Learning: Single- and Multi-Agent Frameworks","authors":"Yangchen Li;Lingzhi Zhao;Tianle Wang;Lianghui Ding;Feng Yang","doi":"10.1109/TMLCN.2025.3534754","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3534754","url":null,"abstract":"In this paper, we investigate federated learning (FL) efficiency improvement in practical edge computing systems, where edge workers have non-independent and identically distributed (non-IID) local data, as well as dynamic and heterogeneous computing and communication capabilities. We consider a general FL algorithm with configurable parameters, including the number of local iterations, mini-batch sizes, step sizes, aggregation weights, and quantization parameters, and provide a rigorous convergence analysis. We formulate a joint optimization problem for FL worker selection and algorithm parameter configuration to minimize the final test loss subject to time and energy constraints. The resulting problem is a complicated stochastic sequential decision-making problem with an implicit objective function and unknown transition probabilities. To address these challenges, we propose knowledge/model-driven single-agent and multi-agent deep reinforcement learning (DRL) frameworks. We transform the primal problem into a Markov decision process (MDP) for the single-agent DRL framework and a decentralized partially-observable Markov decision process (Dec-POMDP) for the multi-agent DRL framework. We develop efficient single-agent and multi-agent asynchronous advantage actor-critic (A3C) approaches to solve the MDP and Dec-POMDP, respectively. In both frameworks, we design a knowledge-based reward to facilitate effective DRL and propose a model-based stochastic policy to tackle the mixed discrete-continuous actions and large action spaces. To reduce the computational complexities of policy learning and execution, we introduce a segmented actor-critic architecture for the single-agent DRL and a distributed actor-critic architecture for the multi-agent DRL. Numerical results demonstrate the effectiveness and advantages of the proposed frameworks in enhancing FL efficiency.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"332-352"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854500","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480780","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}
Pub Date : 2025-01-24DOI: 10.1109/TMLCN.2025.3534139
Shahrukh Khan Kasi;Fahd Ahmed Khan;Sabit Ekin;Ali Imran
The evolution of Open Radio Access Networks (O-RAN) presents an opportunity to enhance network performance by enabling dynamic orchestration of configuration and optimization parameters (COPs) through online learning methods. However, leveraging this potential requires overcoming the limitations of traditional cell-centric RAN architectures, which lack the necessary flexibility. On the other hand, despite their recent popularity, the practical deployment of online learning frameworks, such as Deep Reinforcement Learning (DRL)-based COP optimization solutions, remains limited due to their risk of deteriorating network performance during the exploration phase. In this article, we propose and analyze a novel risk-aware DRL framework for user-centric RAN (UC-RAN), which offers both the architectural flexibility and COP optimization to exploit this flexibility. We investigate and identify UC-RAN COPs that can be optimized via a soft actor-critic algorithm implementable as an O-RAN application (rApp) to jointly maximize latency satisfaction, reliability satisfaction, area spectral efficiency, and energy efficiency. We use the offline learning on UC-RAN to reliably accelerate DRL training, thus minimizing the risk of DRL deteriorating cellular network performance. Results show that our proposed solution approaches near-optimal performance in just a few hundred iterations with a decrease in risk score by a factor of ten.
{"title":"Risk-Aware Reinforcement Learning Framework for User-Centric O-RAN","authors":"Shahrukh Khan Kasi;Fahd Ahmed Khan;Sabit Ekin;Ali Imran","doi":"10.1109/TMLCN.2025.3534139","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3534139","url":null,"abstract":"The evolution of Open Radio Access Networks (O-RAN) presents an opportunity to enhance network performance by enabling dynamic orchestration of configuration and optimization parameters (COPs) through online learning methods. However, leveraging this potential requires overcoming the limitations of traditional cell-centric RAN architectures, which lack the necessary flexibility. On the other hand, despite their recent popularity, the practical deployment of online learning frameworks, such as Deep Reinforcement Learning (DRL)-based COP optimization solutions, remains limited due to their risk of deteriorating network performance during the exploration phase. In this article, we propose and analyze a novel risk-aware DRL framework for user-centric RAN (UC-RAN), which offers both the architectural flexibility and COP optimization to exploit this flexibility. We investigate and identify UC-RAN COPs that can be optimized via a soft actor-critic algorithm implementable as an O-RAN application (rApp) to jointly maximize latency satisfaction, reliability satisfaction, area spectral efficiency, and energy efficiency. We use the offline learning on UC-RAN to reliably accelerate DRL training, thus minimizing the risk of DRL deteriorating cellular network performance. Results show that our proposed solution approaches near-optimal performance in just a few hundred iterations with a decrease in risk score by a factor of ten.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"195-214"},"PeriodicalIF":0.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105944","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}
With the increasing deployment of 5G networks, the vulnerability to malicious interference, such as jamming attacks, has become a significant concern. Detecting such attacks is crucial to ensuring the reliability and security of 5G communication systems Specifically in CAVs. This paper proposes a robust jamming detection system addressing challenges posed by impairments, such as Carrier Frequency Offset (CFO) and channel effects. To improve overall detection performance, the proposed approach leverages deep ensemble learning techniques by fusing different features with different sensitivities from the RF domain and Physical layer namely, Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS) cross-correlations in the time and the frequency domain, the energy of the null subcarriers, and the PBCH Error Vector Magnitude (EVM). The ensemble module is optimized for the aggregation method and different learning parameters. Furthermore, to mitigate the false positive and false negative, a systematic approach, termed Temporal Epistemic Decision Aggregator (TEDA) is introduced, which elegantly navigates the time-accuracy tradeoff by seamlessly integrating temporal decisions, thereby enhancing decision reliability. The presented approach is also capable of detecting inter-cell/inter-sector interference, thereby enhancing situational awareness on 5G air interface and RF domain security. Results show that the presented approach achieves the Area Under Curve (AUC) of 0.98, outperforming other compared methods by at least 0.06 (a 6% improvement). The true positive and negative rates are reported as 93.5% and 91.9%, respectively, showcasing strong performance for scenarios with CFO and channel impairments and outperforming the other compared methods by at least 12%. An optimization problem is formulated and solved based on the level of uncertainty observed in the experimental set-up and the optimum TEDA configuration is derived for the target false-alarm and miss-detection probability. Ultimately, the performance of the entire architecture is confirmed through analysis of real 5G signals acquired from a practical testbed, showing strong agreement with the simulation results.
{"title":"Deep Fusion Intelligence: Enhancing 5G Security Against Over-the-Air Attacks","authors":"Mohammadreza Amini;Ghazal Asemian;Burak Kantarci;Cliff Ellement;Melike Erol-Kantarci","doi":"10.1109/TMLCN.2025.3533427","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3533427","url":null,"abstract":"With the increasing deployment of 5G networks, the vulnerability to malicious interference, such as jamming attacks, has become a significant concern. Detecting such attacks is crucial to ensuring the reliability and security of 5G communication systems Specifically in CAVs. This paper proposes a robust jamming detection system addressing challenges posed by impairments, such as Carrier Frequency Offset (CFO) and channel effects. To improve overall detection performance, the proposed approach leverages deep ensemble learning techniques by fusing different features with different sensitivities from the RF domain and Physical layer namely, Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS) cross-correlations in the time and the frequency domain, the energy of the null subcarriers, and the PBCH Error Vector Magnitude (EVM). The ensemble module is optimized for the aggregation method and different learning parameters. Furthermore, to mitigate the false positive and false negative, a systematic approach, termed Temporal Epistemic Decision Aggregator (TEDA) is introduced, which elegantly navigates the time-accuracy tradeoff by seamlessly integrating temporal decisions, thereby enhancing decision reliability. The presented approach is also capable of detecting inter-cell/inter-sector interference, thereby enhancing situational awareness on 5G air interface and RF domain security. Results show that the presented approach achieves the Area Under Curve (AUC) of 0.98, outperforming other compared methods by at least 0.06 (a 6% improvement). The true positive and negative rates are reported as 93.5% and 91.9%, respectively, showcasing strong performance for scenarios with CFO and channel impairments and outperforming the other compared methods by at least 12%. An optimization problem is formulated and solved based on the level of uncertainty observed in the experimental set-up and the optimum TEDA configuration is derived for the target false-alarm and miss-detection probability. Ultimately, the performance of the entire architecture is confirmed through analysis of real 5G signals acquired from a practical testbed, showing strong agreement with the simulation results.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"263-279"},"PeriodicalIF":0.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10851353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105946","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}
Semantic communications, a promising approach for agent-human and agent-agent interactions, typically operate at a feature level, lacking true semantic understanding. This paper explores understanding-level semantic communications (ULSC), transforming visual data into human-intelligible semantic content. We employ an image caption neural network (ICNN) to derive semantic representations from visual data, expressed as natural language descriptions. These are further refined using a pre-trained large language model (LLM) for importance quantification and semantic error correction. The subsequent semantic importance-aware communications (SIAC) aim to minimize semantic loss while respecting transmission delay constraints, exemplified through adaptive modulation and coding strategies. At the receiving end, LLM-based semantic error correction is utilized. If visual data recreation is desired, a pre-trained generative artificial intelligence (AI) model can regenerate it using the corrected descriptions. We assess semantic similarities between transmitted and recovered content, demonstrating ULSC’s superior ability to convey semantic understanding compared to feature-level semantic communications (FLSC). ULSC’s conversion of visual data to natural language facilitates various cognitive tasks, leveraging human knowledge bases. Additionally, this method enhances privacy, as neither original data nor features are directly transmitted.
{"title":"Semantic Importance-Aware Communications With Semantic Correction Using Large Language Models","authors":"Shuaishuai Guo;Yanhu Wang;Jia Ye;Anbang Zhang;Peng Zhang;Kun Xu","doi":"10.1109/TMLCN.2025.3530875","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3530875","url":null,"abstract":"Semantic communications, a promising approach for agent-human and agent-agent interactions, typically operate at a feature level, lacking true semantic understanding. This paper explores understanding-level semantic communications (ULSC), transforming visual data into human-intelligible semantic content. We employ an image caption neural network (ICNN) to derive semantic representations from visual data, expressed as natural language descriptions. These are further refined using a pre-trained large language model (LLM) for importance quantification and semantic error correction. The subsequent semantic importance-aware communications (SIAC) aim to minimize semantic loss while respecting transmission delay constraints, exemplified through adaptive modulation and coding strategies. At the receiving end, LLM-based semantic error correction is utilized. If visual data recreation is desired, a pre-trained generative artificial intelligence (AI) model can regenerate it using the corrected descriptions. We assess semantic similarities between transmitted and recovered content, demonstrating ULSC’s superior ability to convey semantic understanding compared to feature-level semantic communications (FLSC). ULSC’s conversion of visual data to natural language facilitates various cognitive tasks, leveraging human knowledge bases. Additionally, this method enhances privacy, as neither original data nor features are directly transmitted.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"232-245"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105945","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}
Pub Date : 2025-01-13DOI: 10.1109/TMLCN.2025.3528901
Xiyu Zhao;Qimei Cui;Weicai Li;Wei Ni;Ekram Hossain;Quan Z. Sheng;Xiaofeng Tao;Ping Zhang
Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). While FL is unaffected by personalized model training, in Ditto, PL depends on the outcome of the FL. However, the clients’ concern about their privacy and consequent perturbation of their local models can affect the convergence and (performance) fairness of PL. This paper presents PFL, called DP-Ditto, which is a non-trivial extension of Ditto under the protection of differential privacy (DP), and analyzes the trade-off among its privacy guarantee, model convergence, and performance distribution fairness. We also analyze the convergence upper bound of the personalized models under DP-Ditto and derive the optimal number of global aggregations given a privacy budget. Further, we analyze the performance fairness of the personalized models, and reveal the feasibility of optimizing DP-Ditto jointly for convergence and fairness. Experiments validate our analysis and demonstrate that DP-Ditto can surpass the DP-perturbed versions of the state-of-the-art PFL models, such as FedAMP, pFedMe, APPLE, and FedALA, by over 32.71% in fairness and 9.66% in accuracy.
{"title":"Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning","authors":"Xiyu Zhao;Qimei Cui;Weicai Li;Wei Ni;Ekram Hossain;Quan Z. Sheng;Xiaofeng Tao;Ping Zhang","doi":"10.1109/TMLCN.2025.3528901","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3528901","url":null,"abstract":"Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). While FL is unaffected by personalized model training, in Ditto, PL depends on the outcome of the FL. However, the clients’ concern about their privacy and consequent perturbation of their local models can affect the convergence and (performance) fairness of PL. This paper presents PFL, called DP-Ditto, which is a non-trivial extension of Ditto under the protection of differential privacy (DP), and analyzes the trade-off among its privacy guarantee, model convergence, and performance distribution fairness. We also analyze the convergence upper bound of the personalized models under DP-Ditto and derive the optimal number of global aggregations given a privacy budget. Further, we analyze the performance fairness of the personalized models, and reveal the feasibility of optimizing DP-Ditto jointly for convergence and fairness. Experiments validate our analysis and demonstrate that DP-Ditto can surpass the DP-perturbed versions of the state-of-the-art PFL models, such as FedAMP, pFedMe, APPLE, and FedALA, by over 32.71% in fairness and 9.66% in accuracy.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"246-262"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838599","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105948","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}
Pub Date : 2025-01-09DOI: 10.1109/TMLCN.2025.3527919
Mahsa Raeiszadeh;Amin Ebrahimzadeh;Roch H. Glitho;Johan Eker;Raquel A. F. Mini
The complexity and dynamicity of microservice architectures in cloud environments present substantial challenges to the reliability and availability of the services built on these architectures. Therefore, effective anomaly detection is crucial to prevent impending failures and resolve them promptly. Distributed data analysis techniques based on machine learning (ML) have recently gained attention in detecting anomalies in microservice systems. ML-based anomaly detection techniques mostly require centralized data collection and processing, which may raise scalability and computational issues in practice. In this paper, we propose an Asynchronous Real-Time Federated Learning (ART-FL) approach for anomaly detection in cloud-based microservice systems. In our approach, edge clients perform real-time learning with continuous streaming local data. At the edge clients, we model intra-service behaviors and inter-service dependencies in multi-source distributed data based on a Span Causal Graph (SCG) representation and train a model through a combination of Graph Neural Network (GNN) and Positive and Unlabeled (PU) learning. Our FL approach updates the global model in an asynchronous manner to achieve accurate and efficient anomaly detection, addressing computational overhead across diverse edge clients, including those that experience delays. Our trace-driven evaluations indicate that the proposed method outperforms the state-of-the-art anomaly detection methods by 4% in terms of $F_{1}$ -score while meeting the given time efficiency and scalability requirements.
{"title":"Asynchronous Real-Time Federated Learning for Anomaly Detection in Microservice Cloud Applications","authors":"Mahsa Raeiszadeh;Amin Ebrahimzadeh;Roch H. Glitho;Johan Eker;Raquel A. F. Mini","doi":"10.1109/TMLCN.2025.3527919","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3527919","url":null,"abstract":"The complexity and dynamicity of microservice architectures in cloud environments present substantial challenges to the reliability and availability of the services built on these architectures. Therefore, effective anomaly detection is crucial to prevent impending failures and resolve them promptly. Distributed data analysis techniques based on machine learning (ML) have recently gained attention in detecting anomalies in microservice systems. ML-based anomaly detection techniques mostly require centralized data collection and processing, which may raise scalability and computational issues in practice. In this paper, we propose an Asynchronous Real-Time Federated Learning (ART-FL) approach for anomaly detection in cloud-based microservice systems. In our approach, edge clients perform real-time learning with continuous streaming local data. At the edge clients, we model intra-service behaviors and inter-service dependencies in multi-source distributed data based on a Span Causal Graph (SCG) representation and train a model through a combination of Graph Neural Network (GNN) and Positive and Unlabeled (PU) learning. Our FL approach updates the global model in an asynchronous manner to achieve accurate and efficient anomaly detection, addressing computational overhead across diverse edge clients, including those that experience delays. Our trace-driven evaluations indicate that the proposed method outperforms the state-of-the-art anomaly detection methods by 4% in terms of <inline-formula> <tex-math>$F_{1}$ </tex-math></inline-formula>-score while meeting the given time efficiency and scalability requirements.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"176-194"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10835399","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993443","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}
Pub Date : 2025-01-06DOI: 10.1109/TMLCN.2025.3526551
Selim F. Yilmaz;Burak Hasircioğlu;Li Qiao;Denız Gündüz
We consider collaborative inference at the wireless edge, where each client’s model is trained independently on its local dataset. Clients are queried in parallel to make an accurate decision collaboratively. In addition to maximizing the inference accuracy, we also want to ensure the privacy of local models. To this end, we leverage the superposition property of the multiple access channel to implement bandwidth-efficient multi-user inference methods. We propose different methods for ensemble and multi-view classification that exploit over-the-air computation (OAC). We show that these schemes perform better than their orthogonal counterparts with statistically significant differences while using fewer resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed OAC approach to multi-user inference, and perform an ablation study to demonstrate the effectiveness of our design choices. We share the source code of the framework publicly on Github to facilitate further research and reproducibility.
{"title":"Private Collaborative Edge Inference via Over-the-Air Computation","authors":"Selim F. Yilmaz;Burak Hasircioğlu;Li Qiao;Denız Gündüz","doi":"10.1109/TMLCN.2025.3526551","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3526551","url":null,"abstract":"We consider collaborative inference at the wireless edge, where each client’s model is trained independently on its local dataset. Clients are queried in parallel to make an accurate decision collaboratively. In addition to maximizing the inference accuracy, we also want to ensure the privacy of local models. To this end, we leverage the superposition property of the multiple access channel to implement bandwidth-efficient multi-user inference methods. We propose different methods for ensemble and multi-view classification that exploit over-the-air computation (OAC). We show that these schemes perform better than their orthogonal counterparts with statistically significant differences while using fewer resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed OAC approach to multi-user inference, and perform an ablation study to demonstrate the effectiveness of our design choices. We share the source code of the framework publicly on Github to facilitate further research and reproducibility.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"215-231"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105947","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}
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