IEEE Transactions on Machine Learning in Communications and Networking最新文献_第7页

Hierarchical Reinforcement Learning for Multi-Layer Multi-Service Non-Terrestrial Vehicular Edge Computing 多层多服务非地面车载边缘计算的分层强化学习

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-07-25 DOI: 10.1109/TMLCN.2024.3433620

Swapnil Sadashiv Shinde;Daniele Tarchi

{"title":"Hierarchical Reinforcement Learning for Multi-Layer Multi-Service Non-Terrestrial Vehicular Edge Computing","authors":"Swapnil Sadashiv Shinde;Daniele Tarchi","doi":"10.1109/TMLCN.2024.3433620","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3433620","url":null,"abstract":"Vehicular Edge Computing (VEC) represents a novel advancement within the Internet of Vehicles (IoV). Despite its implementation through Road Side Units (RSUs), VEC frequently falls short of satisfying the escalating demands of Vehicle Users (VUs) for new services, necessitating supplementary computational and communication resources. Non-Terrestrial Networks (NTN) with onboard Edge Computing (EC) facilities are gaining a central place in the 6G vision, allowing one to extend future services also to uncovered areas. This scenario, composed of a multitude of VUs, terrestrial and non-terrestrial nodes, and characterized by mobility and stringent requirements, brings in a very high complexity. Machine Learning (ML) represents a perfect tool for solving these types of problems. Integrated Terrestrial and Non-terrestrial (T-NT) EC, supported by innovative intelligent solutions enabled through ML technology, can boost the VEC capacity, coverage range, and resource utilization. Therefore, by exploring the integrated T-NT EC platforms, we design a multi-EC-enabled vehicular networking platform with a heterogeneous set of services. Next, we model the latency and energy requirements for processing the VU tasks through partial computation offloading operations. We aim to optimize the overall latency and energy requirements for processing the VU data by selecting the appropriate edge nodes and the offloading amount. The problem is defined as a multi-layer sequential decision-making problem through the Markov Decision Processes (MDP). The Hierarchical Reinforcement Learning (HRL) method, implemented through a Deep Q network, is used to optimize the network selection and offloading policies. Simulation results are compared with different benchmark methods to show performance gains in terms of overall cost requirements and reliability.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1045-1061"},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10609447","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964883","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}

引用次数: 0

Full-Duplex Millimeter Wave MIMO Channel Estimation: A Neural Network Approach 全双工毫米波多输入多输出信道估计：神经网络方法

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-07-24 DOI: 10.1109/TMLCN.2024.3432865

Mehdi Sattari;Hao Guo;Deniz Gündüz;Ashkan Panahi;Tommy Svensson

{"title":"Full-Duplex Millimeter Wave MIMO Channel Estimation: A Neural Network Approach","authors":"Mehdi Sattari;Hao Guo;Deniz Gündüz;Ashkan Panahi;Tommy Svensson","doi":"10.1109/TMLCN.2024.3432865","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3432865","url":null,"abstract":"Millimeter wave (mmWave) multiple-input-multi-output (MIMO) is now a reality with great potential for further improvement. We study full-duplex transmissions as an effective way to improve mmWave MIMO systems. Compared to half-duplex systems, full-duplex transmissions may offer higher data rates and lower latency. However, full-duplex transmission is hindered by self-interference (SI) at the receive antennas, and SI channel estimation becomes a crucial step to make the full-duplex systems feasible. In this paper, we address the problem of channel estimation in full-duplex mmWave MIMO systems using neural networks (NNs). Our approach involves sharing pilot resources between user equipments (UEs) and transmit antennas at the base station (BS), aiming to reduce the pilot overhead in full-duplex systems and to achieve a comparable level to that of a half-duplex system. Additionally, in the case of separate antenna configurations in a full-duplex BS, providing channel estimates of transmit antenna (TX) arrays to the downlink UEs poses another challenge, as the TX arrays are not capable of receiving pilot signals. To address this, we employ an NN to map the channel from the downlink UEs to the receive antenna (RX) arrays to the channel from the TX arrays to the downlink UEs. We further elaborate on how NNs perform the estimation with different architectures, (e.g., different numbers of hidden layers), the introduction of non-linear distortion (e.g., with a 1-bit analog-to-digital converter (ADC)), and different channel conditions (e.g., low-correlated and high-correlated channels). Our work provides novel insights into NN-based channel estimators.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1093-1108"},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10608175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994029","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}

引用次数: 0

Intellicise Router Promotes Endogenous Intelligence in Communication Network Intellicise 路由器促进通信网络的内生智能化

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-07-24 DOI: 10.1109/TMLCN.2024.3432861

Qiyun Guo;Haotai Liang;Zhicheng Bao;Chen Dong;Xiaodong Xu;Zhongzheng Tang;Yue Bei

{"title":"Intellicise Router Promotes Endogenous Intelligence in Communication Network","authors":"Qiyun Guo;Haotai Liang;Zhicheng Bao;Chen Dong;Xiaodong Xu;Zhongzheng Tang;Yue Bei","doi":"10.1109/TMLCN.2024.3432861","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3432861","url":null,"abstract":"Endogenous intelligence has emerged as a crucial aspect of next-generation communication networks. This concept is closely intertwined with artificial intelligence (AI), with its primary components being data, algorithms, and computility. Data collection remains a critical concern that warrants focused attention. To address the challenge of data expansion and forwarding, the intellicise router is proposed. It extends the local dataset and continuously enhances the local model through a specifically crafted algorithm, which enhances AI performance, as exemplified by its application in image recognition tasks. Service capability is employed to gauge the router’s ability to provide services and the upper bounds are derived. To analyze the algorithm’s effectiveness, a category-increase model is developed to calculate the probability of categories rising under both equal and unequal probabilities of image communication categories. The numerical analysis results align with simulation results, affirming the validity of the category-increase model. To assess the performance of the intellicise router, a communication system is simulated. A comparative analysis of these experimental results demonstrates that the intellicise router can continuously improve its performance to provide better service.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1509-1526"},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10608170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397285","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}

引用次数: 0

Accelerating Fair Federated Learning: Adaptive Federated Adam 加速公平联合学习：自适应联合亚当

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-07-04 DOI: 10.1109/TMLCN.2024.3423648

Li Ju;Tianru Zhang;Salman Toor;Andreas Hellander

{"title":"Accelerating Fair Federated Learning: Adaptive Federated Adam","authors":"Li Ju;Tianru Zhang;Salman Toor;Andreas Hellander","doi":"10.1109/TMLCN.2024.3423648","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3423648","url":null,"abstract":"Federated learning is a distributed and privacy-preserving approach to train a statistical model collaboratively from decentralized data held by different parties. However, when the datasets are not independent and identically distributed, models trained by naive federated algorithms may be biased towards certain participants, and model performance across participants is non-uniform. This is known as the fairness problem in federated learning. In this paper, we formulate fairness-controlled federated learning as a dynamical multi-objective optimization problem to ensure the fairness and convergence with theoretical guarantee. To solve the problem efficiently, we study the convergence and bias of \u0000<monospace>Adam</monospace>\u0000 as the server optimizer in federated learning, and propose Adaptive Federated Adam (\u0000<monospace>AdaFedAdam</monospace>\u0000) to accelerate fair federated learning with alleviated bias. We validated the effectiveness, Pareto optimality and robustness of \u0000<monospace>AdaFedAdam</monospace>\u0000 with numerical experiments and show that \u0000<monospace>AdaFedAdam</monospace>\u0000 outperforms existing algorithms, providing better convergence and fairness properties of the federated scheme.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1017-1032"},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725563","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}

引用次数: 0

Learning on Bandwidth Constrained Multi-Source Data With MIMO-Inspired DPP MAP Inference 利用 MIMO 启发的 DPP MAP 推理学习带宽受限的多源数据

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-07-02 DOI: 10.1109/TMLCN.2024.3421907

Xiwen Chen;Huayu Li;Rahul Amin;Abolfazl Razi

{"title":"Learning on Bandwidth Constrained Multi-Source Data With MIMO-Inspired DPP MAP Inference","authors":"Xiwen Chen;Huayu Li;Rahul Amin;Abolfazl Razi","doi":"10.1109/TMLCN.2024.3421907","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3421907","url":null,"abstract":"Determinantal Point Process (DPP) is a powerful technique to enhance data diversity by promoting the repulsion of similar elements in the selected samples. Particularly, DPP-based Maximum A Posteriori (MAP) inference is used to identify subsets with the highest diversity. However, a commonly adopted presumption of all data samples being available at one point hinders its applicability to real-world scenarios where data samples are distributed across distinct sources with intermittent and bandwidth-limited connections. This paper proposes a distributed version of DPP inference to enhance multi-source data diversification under limited communication budgets. First, we convert the lower bound of the diversity-maximized distributed sample selection from matrix determinant optimization to a simpler form of the sum of individual terms. Next, a determinant-preserved sparse representation of selected samples is formed by the sink as a surrogate for collected samples and sent back to sources as lightweight messages to eliminate the need for raw data exchange. Our approach is inspired by the channel orthogonalization process of Multiple-Input Multiple-Output (MIMO) systems based on the Channel State Information (CSI). Extensive experiments verify the superiority of our scalable method over the most commonly used data selection methods, including GreeDi, Greedymax, random selection, and stratified sampling by a substantial gain of at least 12% reduction in Relative Diversity Error (RDE). This enhanced diversity translates to a substantial improvement in the performance of various downstream learning tasks, including multi-level classification (2%-4% gain in accuracy), object detection (2% gain in mAP), and multiple-instance learning (1.3% gain in AUC).","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1341-1356"},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10580972","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246439","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}

引用次数: 0

Fair Probabilistic Multi-Armed Bandit With Applications to Network Optimization 公平概率多臂匪徒与网络优化应用

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-07-01 DOI: 10.1109/TMLCN.2024.3421170

Zhiwu Guo;Chicheng Zhang;Ming Li;Marwan Krunz

{"title":"Fair Probabilistic Multi-Armed Bandit With Applications to Network Optimization","authors":"Zhiwu Guo;Chicheng Zhang;Ming Li;Marwan Krunz","doi":"10.1109/TMLCN.2024.3421170","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3421170","url":null,"abstract":"Online learning, particularly Multi-Armed Bandit (MAB) algorithms, has been extensively adopted in various real-world networking applications. In certain applications, such as fair heterogeneous networks coexistence, multiple links (individual arms) are selected in each round, and the throughputs (rewards) of these arms depend on the chosen set of links. Additionally, ensuring fairness among individual arms is a critical objective. However, existing MAB algorithms are unsuitable for these applications due to different models and assumptions. In this paper, we introduce a new fair probabilistic MAB (FP-MAB) problem aimed at either maximizing the minimum reward for all arms or maximizing the total reward while imposing a fairness constraint that guarantees a minimum selection fraction for each arm. In FP-MAB, the learning agent probabilistically selects a meta-arm, which is associated with one or multiple individual arms in each decision round. To address the FP-MAB problem, we propose two algorithms: Fair Probabilistic Explore-Then-Commit (FP-ETC) and Fair Probabilistic Optimism In the Face of Uncertainty (FP-OFU). We also introduce a novel concept of regret in the context of the max-min fairness objective. We analyze the performance of FP-ETC and FP-OFU in terms of the upper bound of average regret and average constraint violation. Simulation results demonstrate that FP-ETC and FP-OFU achieve lower regrets (or higher objective values) under the same fairness requirements compared to existing MAB algorithms.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"994-1016"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579843","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141618064","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}

引用次数: 0

Sample-Efficient Multi-Agent DQNs for Scalable Multi-Domain 5G+ Inter-Slice Orchestration 面向可扩展多域 5G+ 片间协调的样本高效多代理 DQN

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-28 DOI: 10.1109/TMLCN.2024.3420268

Pavlos Doanis;Thrasyvoulos Spyropoulos

{"title":"Sample-Efficient Multi-Agent DQNs for Scalable Multi-Domain 5G+ Inter-Slice Orchestration","authors":"Pavlos Doanis;Thrasyvoulos Spyropoulos","doi":"10.1109/TMLCN.2024.3420268","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3420268","url":null,"abstract":"Data-driven network slicing has been recently explored as a major driver for beyond 5G networks. Nevertheless, we are still a long way before such solutions are practically applicable in real problems. Most solutions addressing the problem of dynamically placing virtual network function chains (“slices”) on top of a physical topology still face one or more of the following hurdles: (i) they focus on simple slicing setups (e.g. single domain, single slice, simple VNF chains and performance metrics); (ii) solutions based on modern reinforcement learning theory have to deal with astronomically high action spaces, when considering multi-VNF, multi-domain, multi-slice problems; (iii) the training of the algorithms is not particularly data-efficient, which can hinder their practical application given the scarce(r) availability of cellular network related data (as opposed to standard machine learning problems). To this end, we attempt to tackle all the above shortcomings in one common framework. For (i), we propose a generic, queuing network based model that captures the inter-slice orchestration setting, supporting complex VNF chain topologies and end-to-end performance metrics. For (ii), we explore multi-agent DQN algorithms that can reduce action space complexity by orders of magnitude compared to standard DQN. For (iii), we investigate two mechanisms to store to and select from the experience replay buffer, in order to speed up the training of DQN agents. The proposed scheme was validated to outperform both vanilla DQN (by orders of magnitude faster convergence) and static heuristics (\u0000<inline-formula> <tex-math>$3times $ </tex-math></inline-formula>\u0000 cost improvement).","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"956-977"},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141618103","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}

引用次数: 0

Learning End-to-End Hybrid Precoding for Multi-User mmWave Mobile System With GNNs 利用 GNN 为多用户毫米波移动系统学习端到端混合精确编码

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-28 DOI: 10.1109/TMLCN.2024.3420269

Ruiming Wang;Chenyang Yang;Shengqian Han;Jiajun Wu;Shuangfeng Han;Xiaoyun Wang

{"title":"Learning End-to-End Hybrid Precoding for Multi-User mmWave Mobile System With GNNs","authors":"Ruiming Wang;Chenyang Yang;Shengqian Han;Jiajun Wu;Shuangfeng Han;Xiaoyun Wang","doi":"10.1109/TMLCN.2024.3420269","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3420269","url":null,"abstract":"Hybrid precoding is an efficient technique for achieving high rates at a low cost in millimeter wave (mmWave) multi-antenna systems. Many research efforts have explored the use of deep learning to optimize hybrid precoding, particularly in static channel scenarios. However, in mobile communication systems, the performance of mmWave communication severely degrades due to the channel aging effect. Furthermore, the learned precoding policy should be adaptable to dynamic environments, such as variations in the number of active users, to avoid the need for re-training. In this paper, resorting to the proactive optimization approach, we propose an end-to-end learning method to learn the downlink multi-user analog and digital hybrid precoders directly from the received uplink sounding reference signals, without explicit channel estimation and prediction. We take into account the frame structure used in practical cellular systems and design a parallel proactive optimization network (P-PONet) to concurrently learn hybrid precoding for multiple downlink subframes. The P-PONet consists of several graph neural networks, which enable the generalizability across different system scales. Simulation results show that the proposed P-PONet outperforms existing methods in terms of sum-rate performance and sounding overhead, and is generalizable to various system configurations.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"978-993"},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141618063","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}

引用次数: 0

Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming 学习大规模多输入多输出波束成形的高能效发射机配置

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-27 DOI: 10.1109/TMLCN.2024.3419728

Hamed Hojatian;Zoubeir Mlika;Jérémy Nadal;Jean-François Frigon;François Leduc-Primeau

{"title":"Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming","authors":"Hamed Hojatian;Zoubeir Mlika;Jérémy Nadal;Jean-François Frigon;François Leduc-Primeau","doi":"10.1109/TMLCN.2024.3419728","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3419728","url":null,"abstract":"Hybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency (EE) of massive multiple-input multiple-output (mMIMO) systems. However, the transmitter architecture may contain several parameters that need to be optimized, such as the power allocated to the antennas and the connections between the antennas and the radio frequency chains. Therefore, finding the optimal transmitter architecture requires solving a non-convex mixed integer problem in a large search space. In this paper, we consider the problem of maximizing the EE of fully digital precoder (FDP) and HBF transmitters. First, we propose an energy model for different beamforming structures. Then, based on the proposed energy model, we develop a self-supervised learning (SSL) method to maximize the EE by designing the transmitter configuration for FDP and HBF. The proposed deep neural networks can provide different trade-offs between spectral efficiency and energy consumption while adapting to different numbers of active users. Finally, towards obtaining a system that can be trained using in-the-field measurements, we investigate the ability of the model to be trained exclusively using imperfect channel state information (CSI), both for the input to the deep learning model and for the calculation of the loss function. Simulation results show that the proposed solutions can outperform conventional methods in terms of EE while being trained with imperfect CSI. Furthermore, we show that the proposed solutions are less complex and more robust to noise than conventional methods.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"939-955"},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10574840","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141618104","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}

引用次数: 0

GenAI-Based Models for NGSO Satellites Interference Detection 基于 GenAI 的 NGSO 卫星干扰探测模型

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-25 DOI: 10.1109/TMLCN.2024.3418933

Almoatssimbillah Saifaldawla;Flor Ortiz;Eva Lagunas;Abuzar B. M. Adam;Symeon Chatzinotas

{"title":"GenAI-Based Models for NGSO Satellites Interference Detection","authors":"Almoatssimbillah Saifaldawla;Flor Ortiz;Eva Lagunas;Abuzar B. M. Adam;Symeon Chatzinotas","doi":"10.1109/TMLCN.2024.3418933","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3418933","url":null,"abstract":"Recent advancements in satellite communications have highlighted the challenge of interference detection, especially with the new generation of non-geostationary orbit satellites (NGSOs) that share the same frequency bands as legacy geostationary orbit satellites (GSOs). Despite existing radio regulations during the filing stage, this heightened congestion in the spectrum is likely to lead to instances of interference during real-time operations. This paper addresses the NGSO-to-GSO interference problem by proposing advanced artificial intelligence (AI) models to detect interference events. In particular, we focus on the downlink interference case, where signals from low-Earth orbit satellites (LEOs) potentially impact the signals received at the GSO ground stations (GGSs). In addition to the widely used autoencoder-based models (AEs), we design, develop, and train two generative AI-based models (GenAI), which are a variational autoencoder (VAE) and a transformer-based interference detector (TrID). These models generate samples of the expected GSO signal, whose error with respect to the input signal is used to flag interference. Actual satellite positions, trajectories, and realistic system parameters are used to emulate the interference scenarios and validate the proposed models. Numerical evaluation reveals that the models exhibit higher accuracy for detecting interference in the time-domain signal representations compared to the frequency-domain representations. Furthermore, the results demonstrate that TrID significantly outperforms the other models as well as the traditional energy detector (ED) approach, showing an increase of up to 31.23% in interference detection accuracy, offering an innovative and efficient solution to a pressing challenge in satellite communications.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"904-924"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500330","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}

引用次数: 0