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}
Pub Date : 2024-12-25DOI: 10.1109/TMLCN.2024.3522872
Mehdi Letafati;Samad Ali;Matti Latva-Aho
In this paper, conditional denoising diffusion probabilistic models (CDiffs) are proposed to enhance the data transmission and reconstruction over wireless channels. The underlying mechanism of diffusion models is to decompose the data generation process over the so-called “denoising” steps. Inspired by this, the key idea is to leverage the generative prior of diffusion models in learning a “noisy-to-clean” transformation of the information signal to help enhance data reconstruction. The proposed scheme could be beneficial for communication scenarios in which a prior knowledge of the information content is available, e.g., in multimedia transmission. Hence, instead of employing complicated channel codes that reduce the information rate, one can exploit diffusion priors for reliable data reconstruction, especially under extreme channel conditions due to low signal-to-noise ratio (SNR), or hardware-impaired communications. The proposed CDiff-assisted receiver is tailored for the scenario of wireless image transmission using MNIST dataset. Our numerical results highlight the reconstruction performance of our scheme compared to the conventional digital communication, as well as the deep neural network (DNN)-based benchmark. It is also shown that more than 10 dB improvement in the reconstruction could be achieved in low SNR regimes, without the need to reduce the information rate for error correction.
{"title":"Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications","authors":"Mehdi Letafati;Samad Ali;Matti Latva-Aho","doi":"10.1109/TMLCN.2024.3522872","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3522872","url":null,"abstract":"In this paper, conditional denoising diffusion probabilistic models (CDiffs) are proposed to enhance the data transmission and reconstruction over wireless channels. The underlying mechanism of diffusion models is to decompose the data generation process over the so-called “denoising” steps. Inspired by this, the key idea is to leverage the generative prior of diffusion models in learning a “noisy-to-clean” transformation of the information signal to help enhance data reconstruction. The proposed scheme could be beneficial for communication scenarios in which a prior knowledge of the information content is available, e.g., in multimedia transmission. Hence, instead of employing complicated channel codes that reduce the information rate, one can exploit diffusion priors for reliable data reconstruction, especially under extreme channel conditions due to low signal-to-noise ratio (SNR), or hardware-impaired communications. The proposed CDiff-assisted receiver is tailored for the scenario of wireless image transmission using MNIST dataset. Our numerical results highlight the reconstruction performance of our scheme compared to the conventional digital communication, as well as the deep neural network (DNN)-based benchmark. It is also shown that more than 10 dB improvement in the reconstruction could be achieved in low SNR regimes, without the need to reduce the information rate for error correction.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"133-146"},"PeriodicalIF":0.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912491","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 : 2024-12-23DOI: 10.1109/TMLCN.2024.3521876
Danish Rizvi;David Boyle
Unmanned Aerial Vehicles (UAVs) are increasingly used as aerial base stations to provide ad hoc communications infrastructure. Building upon prior research efforts which consider either static nodes, 2D trajectories or single UAV systems, this paper focuses on the use of multiple UAVs for providing wireless communication to mobile users in the absence of terrestrial communications infrastructure. In particular, we jointly optimize UAV 3D trajectory and NOMA power allocation to maximize system throughput. Firstly, a weighted K-means-based clustering algorithm establishes UAV-user associations at regular intervals. Then the efficacy of training a novel Shared Deep Q-Network (SDQN) with action masking is explored. Unlike training each UAV separately using DQN, the SDQN reduces training time by using the experiences of multiple UAVs instead of a single agent. We also show that SDQN can be used to train a multi-agent system with differing action spaces. Simulation results confirm that: 1) training a shared DQN outperforms a conventional DQN in terms of maximum system throughput (+20%) and training time (-10%); 2) it can converge for agents with different action spaces, yielding a 9% increase in throughput compared to Mutual DQN algorithm; and 3) combining NOMA with an SDQN architecture enables the network to achieve a better sum rate compared with existing baseline schemes.
{"title":"Multi-Agent Reinforcement Learning With Action Masking for UAV-Enabled Mobile Communications","authors":"Danish Rizvi;David Boyle","doi":"10.1109/TMLCN.2024.3521876","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3521876","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) are increasingly used as aerial base stations to provide ad hoc communications infrastructure. Building upon prior research efforts which consider either static nodes, 2D trajectories or single UAV systems, this paper focuses on the use of multiple UAVs for providing wireless communication to mobile users in the absence of terrestrial communications infrastructure. In particular, we jointly optimize UAV 3D trajectory and NOMA power allocation to maximize system throughput. Firstly, a weighted K-means-based clustering algorithm establishes UAV-user associations at regular intervals. Then the efficacy of training a novel Shared Deep Q-Network (SDQN) with action masking is explored. Unlike training each UAV separately using DQN, the SDQN reduces training time by using the experiences of multiple UAVs instead of a single agent. We also show that SDQN can be used to train a multi-agent system with differing action spaces. Simulation results confirm that: 1) training a shared DQN outperforms a conventional DQN in terms of maximum system throughput (+20%) and training time (-10%); 2) it can converge for agents with different action spaces, yielding a 9% increase in throughput compared to Mutual DQN algorithm; and 3) combining NOMA with an SDQN architecture enables the network to achieve a better sum rate compared with existing baseline schemes.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"117-132"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10812765","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905826","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 : 2024-12-16DOI: 10.1109/TMLCN.2024.3517619
Zhanwei Yu;Yi Zhao;Xiaoli Chu;Di Yuan
Passively cooled base stations (PCBSs) have emerged to deliver better cost and energy efficiency. However, passive cooling necessitates intelligent thermal control via traffic management, i.e., the instantaneous data traffic or throughput of a PCBS directly impacts its thermal performance. This is particularly challenging for outdoor deployment of PCBSs because the heat dissipation efficiency is uncertain and fluctuates over time. What is more, the PCBSs are interference-coupled in multi-cell scenarios. Thus, a higher-throughput PCBS leads to higher interference to the other PCBSs, which, in turn, would require more resource consumption to meet their respective throughput targets. In this paper, we address online decision-making for maximizing the total downlink throughput for a multi-PCBS system subject to constraints related on operating temperature. We demonstrate that a reinforcement learning (RL) approach, specifically soft actor-critic (SAC), can successfully perform throughput maximization while keeping the PCBSs cool, by adapting the throughput to time-varying heat dissipation conditions. Furthermore, we design a denial and reward mechanism that effectively mitigates the risk of overheating during the exploration phase of RL. Simulation results show that our approach achieves up to 88.6% of the global optimum. This is very promising, as our approach operates without prior knowledge of future heat dissipation efficiency, which is required by the global optimum.
{"title":"Online Learning for Intelligent Thermal Management of Interference-Coupled and Passively Cooled Base Stations","authors":"Zhanwei Yu;Yi Zhao;Xiaoli Chu;Di Yuan","doi":"10.1109/TMLCN.2024.3517619","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3517619","url":null,"abstract":"Passively cooled base stations (PCBSs) have emerged to deliver better cost and energy efficiency. However, passive cooling necessitates intelligent thermal control via traffic management, i.e., the instantaneous data traffic or throughput of a PCBS directly impacts its thermal performance. This is particularly challenging for outdoor deployment of PCBSs because the heat dissipation efficiency is uncertain and fluctuates over time. What is more, the PCBSs are interference-coupled in multi-cell scenarios. Thus, a higher-throughput PCBS leads to higher interference to the other PCBSs, which, in turn, would require more resource consumption to meet their respective throughput targets. In this paper, we address online decision-making for maximizing the total downlink throughput for a multi-PCBS system subject to constraints related on operating temperature. We demonstrate that a reinforcement learning (RL) approach, specifically soft actor-critic (SAC), can successfully perform throughput maximization while keeping the PCBSs cool, by adapting the throughput to time-varying heat dissipation conditions. Furthermore, we design a denial and reward mechanism that effectively mitigates the risk of overheating during the exploration phase of RL. Simulation results show that our approach achieves up to 88.6% of the global optimum. This is very promising, as our approach operates without prior knowledge of future heat dissipation efficiency, which is required by the global optimum.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"64-79"},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10802970","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858862","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 : 2024-12-16DOI: 10.1109/TMLCN.2024.3517613
Aleksandar Pasquini;Rajesh Vasa;Irini Logothetis;Hassan Habibi Gharakheili;Alexander Chambers;Minh Tran
Machine Learning (ML)-based techniques are increasingly used for network management tasks, such as intrusion detection, application identification, or asset management. Recent studies show that neural network-based traffic analysis can achieve performance comparable to human feature-engineered ML pipelines. However, neural networks provide this performance at a higher computational cost and complexity, due to high-throughput traffic conditions necessitating specialized hardware for real-time operations. This paper presents lightweight models for encoding characteristics of Internet-of-Things (IoT) network packets; 1) we present two strategies to encode packets (regardless of their size, encryption, and protocol) to integer vectors: a shallow lightweight neural network and compression. With a public dataset containing about 8 million packets emitted by 22 IoT device types, we show the encoded packets can form complete (up to 80%) and homogeneous (up to 89%) clusters; 2) we demonstrate the efficacy of our generated encodings in the downstream classification task and quantify their computing costs. We train three multi-class models to predict the IoT class given network packets and show our models can achieve the same levels of accuracy (94%) as deep neural network embeddings but with computing costs up to 10 times lower; 3) we examine how the amount of packet data (headers and payload) can affect the prediction quality. We demonstrate how the choice of Internet Protocol (IP) payloads strikes a balance between prediction accuracy (99%) and cost. Along with the cost-efficacy of models, this capability can result in rapid and accurate predictions, meeting the requirements of network operators.
{"title":"Robust and Lightweight Modeling of IoT Network Behaviors From Raw Traffic Packets","authors":"Aleksandar Pasquini;Rajesh Vasa;Irini Logothetis;Hassan Habibi Gharakheili;Alexander Chambers;Minh Tran","doi":"10.1109/TMLCN.2024.3517613","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3517613","url":null,"abstract":"Machine Learning (ML)-based techniques are increasingly used for network management tasks, such as intrusion detection, application identification, or asset management. Recent studies show that neural network-based traffic analysis can achieve performance comparable to human feature-engineered ML pipelines. However, neural networks provide this performance at a higher computational cost and complexity, due to high-throughput traffic conditions necessitating specialized hardware for real-time operations. This paper presents lightweight models for encoding characteristics of Internet-of-Things (IoT) network packets; 1) we present two strategies to encode packets (regardless of their size, encryption, and protocol) to integer vectors: a shallow lightweight neural network and compression. With a public dataset containing about 8 million packets emitted by 22 IoT device types, we show the encoded packets can form complete (up to 80%) and homogeneous (up to 89%) clusters; 2) we demonstrate the efficacy of our generated encodings in the downstream classification task and quantify their computing costs. We train three multi-class models to predict the IoT class given network packets and show our models can achieve the same levels of accuracy (94%) as deep neural network embeddings but with computing costs up to 10 times lower; 3) we examine how the amount of packet data (headers and payload) can affect the prediction quality. We demonstrate how the choice of Internet Protocol (IP) payloads strikes a balance between prediction accuracy (99%) and cost. Along with the cost-efficacy of models, this capability can result in rapid and accurate predictions, meeting the requirements of network operators.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"98-116"},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10802939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890343","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 : 2024-12-11DOI: 10.1109/TMLCN.2024.3515913
Zhizhou He;Fabien Héliot;Yi Ma
Reconfigurable Intelligent Surfaces (RIS) can enhance system performance at the cost of increased complexity in multi-user MIMO systems. The beamforming options scale with the number of antennas at the base station/RIS. Existing methods for solving this problem tend to use computationally intensive iterative methods that are non-scalable for large RIS-aided MIMO systems. We propose here a novel self-supervised contrastive learning neural network (NN) architecture to optimize the sum spectral efficiency through joint active and passive beamforming design in multi-user RIS-aided MIMO systems. Our scheme utilizes contrastive learning to capture the channel features from augmented channel data and then can be trained to perform beamforming with only 1% of labeled data. The labels are derived through a closed-form optimization algorithm, leveraging a sequential fractional programming approach. Leveraging the proposed self-supervised design helps to greatly reduce the computational complexity during the training phase. Moreover, our proposed model can operate under various noise levels by using data augmentation methods while maintaining a robust out-of-distribution performance under various propagation environments and different signal-to-noise ratios (SNR)s. During training, our proposed network only needs 10% of labeled data to converge when compared to supervised learning. Our trained NN can then achieve performance which is only $~7%$ and $~2.5%$ away from mathematical upper bound and fully supervised learning, respectively, with far less computational complexity.
{"title":"Self-Supervised Contrastive Learning for Joint Active and Passive Beamforming in RIS-Assisted MU-MIMO Systems","authors":"Zhizhou He;Fabien Héliot;Yi Ma","doi":"10.1109/TMLCN.2024.3515913","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3515913","url":null,"abstract":"Reconfigurable Intelligent Surfaces (RIS) can enhance system performance at the cost of increased complexity in multi-user MIMO systems. The beamforming options scale with the number of antennas at the base station/RIS. Existing methods for solving this problem tend to use computationally intensive iterative methods that are non-scalable for large RIS-aided MIMO systems. We propose here a novel self-supervised contrastive learning neural network (NN) architecture to optimize the sum spectral efficiency through joint active and passive beamforming design in multi-user RIS-aided MIMO systems. Our scheme utilizes contrastive learning to capture the channel features from augmented channel data and then can be trained to perform beamforming with only 1% of labeled data. The labels are derived through a closed-form optimization algorithm, leveraging a sequential fractional programming approach. Leveraging the proposed self-supervised design helps to greatly reduce the computational complexity during the training phase. Moreover, our proposed model can operate under various noise levels by using data augmentation methods while maintaining a robust out-of-distribution performance under various propagation environments and different signal-to-noise ratios (SNR)s. During training, our proposed network only needs 10% of labeled data to converge when compared to supervised learning. Our trained NN can then achieve performance which is only \u0000<inline-formula> <tex-math>$~7%$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$~2.5%$ </tex-math></inline-formula>\u0000 away from mathematical upper bound and fully supervised learning, respectively, with far less computational complexity.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"147-162"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10793234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912511","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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