Pub Date : 2024-09-26DOI: 10.1109/TMLCN.2024.3469128
Dieter Coppens;Ben van Herbruggen;Adnan Shahid;Eli de Poorter
Indoor positioning using UWB technology has gained interest due to its centimeter-level accuracy potential. However, multipath effects and non-line-of-sight conditions cause ranging errors between anchors and tags. Existing approaches for mitigating these ranging errors rely on collecting large labeled datasets, making them impractical for real-world deployments. This paper proposes a novel self-supervised deep reinforcement learning approach that does not require labeled ground truth data. A reinforcement learning agent uses the channel impulse response as a state and predicts corrections to minimize the error between corrected and estimated ranges. The agent learns, self-supervised, by iteratively improving corrections that are generated by combining the predictability of trajectories with filtering and smoothening. Experiments on real-world UWB measurements demonstrate comparable performance to state-of-the-art supervised methods, overcoming data dependency and lack of generalizability limitations. This makes self-supervised deep reinforcement learning a promising solution for practical and scalable UWB-ranging error correction.
{"title":"Removing the Need for Ground Truth UWB Data Collection: Self-Supervised Ranging Error Correction Using Deep Reinforcement Learning","authors":"Dieter Coppens;Ben van Herbruggen;Adnan Shahid;Eli de Poorter","doi":"10.1109/TMLCN.2024.3469128","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3469128","url":null,"abstract":"Indoor positioning using UWB technology has gained interest due to its centimeter-level accuracy potential. However, multipath effects and non-line-of-sight conditions cause ranging errors between anchors and tags. Existing approaches for mitigating these ranging errors rely on collecting large labeled datasets, making them impractical for real-world deployments. This paper proposes a novel self-supervised deep reinforcement learning approach that does not require labeled ground truth data. A reinforcement learning agent uses the channel impulse response as a state and predicts corrections to minimize the error between corrected and estimated ranges. The agent learns, self-supervised, by iteratively improving corrections that are generated by combining the predictability of trajectories with filtering and smoothening. Experiments on real-world UWB measurements demonstrate comparable performance to state-of-the-art supervised methods, overcoming data dependency and lack of generalizability limitations. This makes self-supervised deep reinforcement learning a promising solution for practical and scalable UWB-ranging error correction.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1615-1627"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695458","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565484","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 : 2024-09-24DOI: 10.1109/TMLCN.2024.3467044
Giovanni Di Gennaro;Amedeo Buonanno;Gianmarco Romano;Stefano Buzzi;Francesco A. N. Palmieri
This paper addresses the problem of joint multiplexing of enhanced Mobile Broadband (eMBB) and massive Machine-Type Communications (mMTC) traffic in the same uplink time-frequency RG. Given the challenge posed by a potentially large number of users, it is essential to focus on a multiple access strategy that leverages artificial intelligence to adapt to specific channel conditions. An mMTC agent is developed through a Deep Reinforcement Learning (DRL) methodology for generating grant-free frequency hopping traffic in a decentralized manner, assuming the presence of underlying eMBB traffic dynamics. Within this DRL framework, a methodical comparison between two possible deep neural networks is conducted, using different generative models employed to ascertain their intrinsic capabilities in various application scenarios. The analysis conducted reveals that the Long Short-Term Memory network is particularly suitable for the required task, demonstrating a robustness that is consistently very close to potential upper-bounds, despite the latter requiring complete knowledge of the underlying statistics.
{"title":"Decentralized Grant-Free mMTC Traffic Multiplexing With eMBB Data Through Deep Reinforcement Learning","authors":"Giovanni Di Gennaro;Amedeo Buonanno;Gianmarco Romano;Stefano Buzzi;Francesco A. N. Palmieri","doi":"10.1109/TMLCN.2024.3467044","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3467044","url":null,"abstract":"This paper addresses the problem of joint multiplexing of enhanced Mobile Broadband (eMBB) and massive Machine-Type Communications (mMTC) traffic in the same uplink time-frequency RG. Given the challenge posed by a potentially large number of users, it is essential to focus on a multiple access strategy that leverages artificial intelligence to adapt to specific channel conditions. An mMTC agent is developed through a Deep Reinforcement Learning (DRL) methodology for generating grant-free frequency hopping traffic in a decentralized manner, assuming the presence of underlying eMBB traffic dynamics. Within this DRL framework, a methodical comparison between two possible deep neural networks is conducted, using different generative models employed to ascertain their intrinsic capabilities in various application scenarios. The analysis conducted reveals that the Long Short-Term Memory network is particularly suitable for the required task, demonstrating a robustness that is consistently very close to potential upper-bounds, despite the latter requiring complete knowledge of the underlying statistics.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1440-1455"},"PeriodicalIF":0.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10689612","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368315","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}
To reduce the latency of Backpressure (BP) routing in wireless multi-hop networks, we propose to enhance the existing shortest path-biased BP (SP-BP) and sojourn time-based backlog metrics, since they introduce no additional time step-wise signaling overhead to the basic BP. Rather than relying on hop-distance, we introduce a new edge-weighted shortest path bias built on the scheduling duty cycle of wireless links, which can be predicted by a graph convolutional neural network based on the topology and traffic of wireless networks. Additionally, we tackle three long-standing challenges associated with SP-BP: optimal bias scaling, efficient bias maintenance, and integration of delay awareness. Our proposed solutions inherit the throughput optimality of the basic BP, as well as its practical advantages of low complexity and fully distributed implementation. Our approaches rely on common link features and introduces only a one-time constant overhead to previous SP-BP schemes, or a one-time overhead linear in the network size to the basic BP. Numerical experiments show that our solutions can effectively address the major drawbacks of slow startup, random walk, and the last packet problem in basic BP, improving the end-to-end delay of existing low-overhead BP algorithms under various settings of network traffic, interference, and mobility.
为了减少无线多跳网络中的Backpressure（BP）路由延迟，我们建议改进现有的基于最短路径的BP（SP-BP）和基于停留时间的积压指标，因为它们不会给基本BP带来额外的时间步进信号开销。我们不依赖于跳距，而是根据无线链路的调度占空比引入了一种新的边缘加权最短路径偏置，这种偏置可通过基于无线网络拓扑和流量的图卷积神经网络进行预测。此外，我们还解决了与 SP-BP 相关的三个长期难题：最优偏置缩放、高效偏置维护和延迟感知集成。我们提出的解决方案继承了基本 BP 的吞吐量最优性，以及低复杂性和完全分布式实施的实际优势。我们的方法依赖于常见的链路特征，与以前的 SP-BP 方案相比，只引入了一次性常量开销，与基本 BP 相比，只引入了与网络规模成线性关系的一次性开销。数值实验表明，我们的方案能有效解决基本 BP 的启动慢、随机漫步和最后一个数据包问题等主要缺点，在各种网络流量、干扰和移动性设置下改善现有低开销 BP 算法的端到端延迟。
{"title":"Biased Backpressure Routing Using Link Features and Graph Neural Networks","authors":"Zhongyuan Zhao;Bojan Radojičić;Gunjan Verma;Ananthram Swami;Santiago Segarra","doi":"10.1109/TMLCN.2024.3461711","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3461711","url":null,"abstract":"To reduce the latency of Backpressure (BP) routing in wireless multi-hop networks, we propose to enhance the existing shortest path-biased BP (SP-BP) and sojourn time-based backlog metrics, since they introduce no additional time step-wise signaling overhead to the basic BP. Rather than relying on hop-distance, we introduce a new edge-weighted shortest path bias built on the scheduling duty cycle of wireless links, which can be predicted by a graph convolutional neural network based on the topology and traffic of wireless networks. Additionally, we tackle three long-standing challenges associated with SP-BP: optimal bias scaling, efficient bias maintenance, and integration of delay awareness. Our proposed solutions inherit the throughput optimality of the basic BP, as well as its practical advantages of low complexity and fully distributed implementation. Our approaches rely on common link features and introduces only a one-time constant overhead to previous SP-BP schemes, or a one-time overhead linear in the network size to the basic BP. Numerical experiments show that our solutions can effectively address the major drawbacks of slow startup, random walk, and the last packet problem in basic BP, improving the end-to-end delay of existing low-overhead BP algorithms under various settings of network traffic, interference, and mobility.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1424-1439"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368430","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 : 2024-09-10DOI: 10.1109/TMLCN.2024.3457490
Mostafa Ibrahim;Arsalan Ahmad;Sabit Ekin;Peter LoPresti;Serhat Altunc;Obadiah Kegege;John F. O'Hara
Radiofrequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate prediction of dynamic ground-to-satellite FSO link availability is critical for routing decisions in low-earth orbit constellations. In this paper, we propose a system leveraging ubiquitous RF links to proactively forecast FSO link degradation prior to signal drops below threshold levels. This enables pre-calculation of rerouting to maximally maintain high data rate FSO links throughout the duration of weather effects. We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. Through the simulation of a dense lower earth orbit (LEO) satellite constellation, we demonstrate the efficacy of our approach in a simulated satellite network, highlighting the balance between predictive accuracy and prediction duration. An emulated cloud attenuation model is proposed to provide insight into the temporal profiles of RF signals and their correlation to FSO channel dynamics. Our investigation sheds light on the trade-offs between prediction horizon and accuracy arising from RF beacon numbers and proximity.
{"title":"Anticipating Optical Availability in Hybrid RF/FSO Links Using RF Beacons and Deep Learning","authors":"Mostafa Ibrahim;Arsalan Ahmad;Sabit Ekin;Peter LoPresti;Serhat Altunc;Obadiah Kegege;John F. O'Hara","doi":"10.1109/TMLCN.2024.3457490","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3457490","url":null,"abstract":"Radiofrequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate prediction of dynamic ground-to-satellite FSO link availability is critical for routing decisions in low-earth orbit constellations. In this paper, we propose a system leveraging ubiquitous RF links to proactively forecast FSO link degradation prior to signal drops below threshold levels. This enables pre-calculation of rerouting to maximally maintain high data rate FSO links throughout the duration of weather effects. We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. Through the simulation of a dense lower earth orbit (LEO) satellite constellation, we demonstrate the efficacy of our approach in a simulated satellite network, highlighting the balance between predictive accuracy and prediction duration. An emulated cloud attenuation model is proposed to provide insight into the temporal profiles of RF signals and their correlation to FSO channel dynamics. Our investigation sheds light on the trade-offs between prediction horizon and accuracy arising from RF beacon numbers and proximity.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1369-1388"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10672517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246523","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}
The increasing proliferation of industrial internet of things (IIoT) devices requires the development of efficient radio resource allocation techniques to optimize spectrum utilization. In densely populated IIoT networks, the interference that results from simultaneously scheduling multiple IIoT devices over the same radio resource blocks (RRBs) severely degrades a network’s achievable capacity. This paper investigates an interference management problem for IIoT networks that considers both finite blocklength (FBL)-coded transmission and signal distortions induced by hardware impairments (HWIs) arising from practical, low-complexity radio-frequency front ends. We use the rate-splitting multiple access (RSMA) scheme to effectively schedule multiple IIoT devices in a cluster over the same RRB(s). To enhance the system’s achievable capacity, a joint clustering and transmit power allocation (PA) problem is formulated. To tackle the optimization problem’s inherent computational intractability due to its non-convex structure, a two-step distributed clustering and power management (DCPM) framework is proposed. First, the DCPM framework obtains a set of clustered devices for each access point by employing a greedy clustering algorithm while maximizing the clustered devices’ signal-to-interference-plus-noise ratio. Then, the DCPM framework employs a multi-agent deep reinforcement learning (DRL) framework to optimize transmit PA among the clustered devices. The proposed DRL algorithm learns a suitable transmit PA policy that does not require precise information about instantaneous signal distortions. Our simulation results demonstrate that our proposed DCPM framework adapts seamlessly to varying channel conditions and outperforms several benchmark schemes with and without HWI-induced signal distortions.
{"title":"RSMA-Enabled Interference Management for Industrial Internet of Things Networks With Finite Blocklength Coding and Hardware Impairments","authors":"Nahed Belhadj Mohamed;Md. Zoheb Hassan;Georges Kaddoum","doi":"10.1109/TMLCN.2024.3455268","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3455268","url":null,"abstract":"The increasing proliferation of industrial internet of things (IIoT) devices requires the development of efficient radio resource allocation techniques to optimize spectrum utilization. In densely populated IIoT networks, the interference that results from simultaneously scheduling multiple IIoT devices over the same radio resource blocks (RRBs) severely degrades a network’s achievable capacity. This paper investigates an interference management problem for IIoT networks that considers both finite blocklength (FBL)-coded transmission and signal distortions induced by hardware impairments (HWIs) arising from practical, low-complexity radio-frequency front ends. We use the rate-splitting multiple access (RSMA) scheme to effectively schedule multiple IIoT devices in a cluster over the same RRB(s). To enhance the system’s achievable capacity, a joint clustering and transmit power allocation (PA) problem is formulated. To tackle the optimization problem’s inherent computational intractability due to its non-convex structure, a two-step distributed clustering and power management (DCPM) framework is proposed. First, the DCPM framework obtains a set of clustered devices for each access point by employing a greedy clustering algorithm while maximizing the clustered devices’ signal-to-interference-plus-noise ratio. Then, the DCPM framework employs a multi-agent deep reinforcement learning (DRL) framework to optimize transmit PA among the clustered devices. The proposed DRL algorithm learns a suitable transmit PA policy that does not require precise information about instantaneous signal distortions. Our simulation results demonstrate that our proposed DCPM framework adapts seamlessly to varying channel conditions and outperforms several benchmark schemes with and without HWI-induced signal distortions.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1319-1340"},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246544","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 advancement of communication technology, there is a higher demand for high-precision and high-generalization channel path loss models as it is fundamental to communication systems. For traditional stochastic and deterministic models, it is difficult to strike a balance between prediction accuracy and generalizability. This paper proposes a novel deep learning-based path loss prediction model using satellite images. In order to efficiently extract environment features from satellite images, residual structure, attention mechanism, and spatial pyramid pooling layer are developed in the network based on expert knowledge. Using a convolutional network activation visualization method, the interpretability of the proposed model is improved. Finally, the proposed model achieves a prediction accuracy with a root mean square error of 5.05 dB, demonstrating an improvement of 3.07 dB over a reference empirical propagation model.
{"title":"Channel Path Loss Prediction Using Satellite Images: A Deep Learning Approach","authors":"Chenlong Wang;Bo Ai;Ruisi He;Mi Yang;Shun Zhou;Long Yu;Yuxin Zhang;Zhicheng Qiu;Zhangdui Zhong;Jianhua Fan","doi":"10.1109/TMLCN.2024.3454019","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3454019","url":null,"abstract":"With the advancement of communication technology, there is a higher demand for high-precision and high-generalization channel path loss models as it is fundamental to communication systems. For traditional stochastic and deterministic models, it is difficult to strike a balance between prediction accuracy and generalizability. This paper proposes a novel deep learning-based path loss prediction model using satellite images. In order to efficiently extract environment features from satellite images, residual structure, attention mechanism, and spatial pyramid pooling layer are developed in the network based on expert knowledge. Using a convolutional network activation visualization method, the interpretability of the proposed model is improved. Finally, the proposed model achieves a prediction accuracy with a root mean square error of 5.05 dB, demonstrating an improvement of 3.07 dB over a reference empirical propagation model.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1357-1368"},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663692","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246437","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}
This paper studies an unmanned aerial vehicle (UAV)-enabled communication network, in which the UAV acts as an air relay serving multiple ground users (GUs) to jointly construct an accurate radio map or channel knowledge maps (CKM) through a federated learning (FL) algorithm. Radio map or CKM is a site-specific database that contains detailed channel-related information for specific locations. This information includes channel power gains, shadowing, interference, and angles of arrival (AoA) and departure (AoD), all of which are crucial for enabling environment-aware wireless communications. Because the wireless communication network has limited resource blocks (RBs), only a subset of users can be selected to transmit the model parameters at each iteration. Since the FL training process requires multiple transmission model parameters, the energy limitation of the wireless device will seriously affect the quality of the FL result. In this sense, the energy consumption and resource allocation have a significance to the final FL training result. We formulate an optimization problem by jointly considering user selection, wireless resource allocation, and UAV deployment, with the goal of minimizing the computation energy and wireless transmission energy. To solve the problem, we first propose a probabilistic user selection mechanism to reduce the total number of FL iterations, whereby the users who have a larger impact on the global model in each iteration are more likely to be selected. Then the convex optimization technique is utilized to optimize bandwidth allocation. Furthermore, to further save communication transmission energy, we use deep reinforcement learning (DRL) to optimize the deployment location of the UAV. The DRL-based method enables the UAV to learn from its interaction with the environment and ascertain the most energy-efficient deployment locations through an evaluation of energy consumption during the training process. Finally, the simulation results show that our proposed algorithm can reduce the total energy consumption by nearly 38%, compared to the standard FL algorithm.
{"title":"Energy Minimization for Federated Learning Based Radio Map Construction","authors":"Fahui Wu;Yunfei Gao;Lin Xiao;Dingcheng Yang;Jiangbin Lyu","doi":"10.1109/TMLCN.2024.3453212","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3453212","url":null,"abstract":"This paper studies an unmanned aerial vehicle (UAV)-enabled communication network, in which the UAV acts as an air relay serving multiple ground users (GUs) to jointly construct an accurate radio map or channel knowledge maps (CKM) through a federated learning (FL) algorithm. Radio map or CKM is a site-specific database that contains detailed channel-related information for specific locations. This information includes channel power gains, shadowing, interference, and angles of arrival (AoA) and departure (AoD), all of which are crucial for enabling environment-aware wireless communications. Because the wireless communication network has limited resource blocks (RBs), only a subset of users can be selected to transmit the model parameters at each iteration. Since the FL training process requires multiple transmission model parameters, the energy limitation of the wireless device will seriously affect the quality of the FL result. In this sense, the energy consumption and resource allocation have a significance to the final FL training result. We formulate an optimization problem by jointly considering user selection, wireless resource allocation, and UAV deployment, with the goal of minimizing the computation energy and wireless transmission energy. To solve the problem, we first propose a probabilistic user selection mechanism to reduce the total number of FL iterations, whereby the users who have a larger impact on the global model in each iteration are more likely to be selected. Then the convex optimization technique is utilized to optimize bandwidth allocation. Furthermore, to further save communication transmission energy, we use deep reinforcement learning (DRL) to optimize the deployment location of the UAV. The DRL-based method enables the UAV to learn from its interaction with the environment and ascertain the most energy-efficient deployment locations through an evaluation of energy consumption during the training process. Finally, the simulation results show that our proposed algorithm can reduce the total energy consumption by nearly 38%, compared to the standard FL algorithm.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1248-1264"},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10662910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169609","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 : 2024-08-29DOI: 10.1109/TMLCN.2024.3452057
Pranav S. Page;Anand S. Siyote;Vivek S. Borkar;Gaurav S. Kasbekar
The Internet of Things (IoT) is emerging as a critical technology to connect resource-constrained devices such as sensors and actuators as well as appliances to the Internet. In this paper, a novel methodology for node cardinality estimation in wireless networks such as the IoT and Radio-Frequency Identification (RFID) systems is proposed, which uses the Privileged Feature Distillation (PFD) technique and works using a neural network with a teacher-student model. This paper is the first to use the powerful PFD technique for node cardinality estimation in wireless networks. The teacher is trained using both privileged and regular features, and the student is trained with predictions from the teacher and regular features. Node cardinality estimation algorithms based on the PFD technique are proposed for homogeneous wireless networks as well as heterogeneous wireless networks with $T geq 2$ types of nodes. Extensive simulations, using a synthetic dataset as well as a real dataset, are used to show that the proposed PFD based algorithms for homogeneous as well as heterogeneous networks achieve much lower mean squared errors (MSEs) in the computed node cardinality estimates than state-of-the-art protocols proposed in prior work. In particular, our simulation results for the real dataset show that our proposed PFD based technique for homogeneous (respectively, heterogeneous) networks achieves a MSE that is 92.35% (respectively, 94.08%) lower on average than that achieved by the Simple RFID Counting (SRCs) protocol (respectively, T-SRCs protocol) proposed in prior work while taking the same number of time slots to execute.
{"title":"Node Cardinality Estimation in the Internet of Things Using Privileged Feature Distillation","authors":"Pranav S. Page;Anand S. Siyote;Vivek S. Borkar;Gaurav S. Kasbekar","doi":"10.1109/TMLCN.2024.3452057","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3452057","url":null,"abstract":"The Internet of Things (IoT) is emerging as a critical technology to connect resource-constrained devices such as sensors and actuators as well as appliances to the Internet. In this paper, a novel methodology for node cardinality estimation in wireless networks such as the IoT and Radio-Frequency Identification (RFID) systems is proposed, which uses the Privileged Feature Distillation (PFD) technique and works using a neural network with a teacher-student model. This paper is the first to use the powerful PFD technique for node cardinality estimation in wireless networks. The teacher is trained using both privileged and regular features, and the student is trained with predictions from the teacher and regular features. Node cardinality estimation algorithms based on the PFD technique are proposed for homogeneous wireless networks as well as heterogeneous wireless networks with \u0000<inline-formula> <tex-math>$T geq 2$ </tex-math></inline-formula>\u0000 types of nodes. Extensive simulations, using a synthetic dataset as well as a real dataset, are used to show that the proposed PFD based algorithms for homogeneous as well as heterogeneous networks achieve much lower mean squared errors (MSEs) in the computed node cardinality estimates than state-of-the-art protocols proposed in prior work. In particular, our simulation results for the real dataset show that our proposed PFD based technique for homogeneous (respectively, heterogeneous) networks achieves a MSE that is 92.35% (respectively, 94.08%) lower on average than that achieved by the Simple RFID Counting (SRCs) protocol (respectively, T-SRCs protocol) proposed in prior work while taking the same number of time slots to execute.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1229-1247"},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142152132","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 : 2024-08-28DOI: 10.1109/TMLCN.2024.3450829
Maria Raftopoulou;José Mairton B. da Silva;Remco Litjens;H. Vincent Poor;Piet van Mieghem
Federated learning is an effective method to train a machine learning model without requiring to aggregate the potentially sensitive data of agents in a central server. However, the limited communication bandwidth, the hardware of the agents and a potential application-specific latency requirement impact how many and which agents can participate in the learning process at each communication round. In this paper, we propose a selection metric characterizing each agent’s importance with respect to both the learning process and the resource efficiency of its wireless communication channel. Leveraging this importance metric, we formulate a general agent selection optimization problem, which can be adapted to different environments with latency or resource-oriented constraints. Considering an example wireless environment with latency constraints, the agent selection problem reduces to the 0/1 Knapsack problem, which we solve with a fully polynomial approximation. We then evaluate the agent selection policy in different scenarios, using extensive simulations for an example task of object classification of European traffic signs. The results indicate that agent selection policies which consider both learning and channel aspects provide benefits in terms of the attainable global model accuracy and/or the time needed to achieve a targeted accuracy level. However, in scenarios where agents have a limited number of data samples or where the latency requirement is very stringent, a pure learning-based agent selection policy is shown to be more beneficial during the early or late stages of the learning process.
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Pub Date : 2024-08-26DOI: 10.1109/TMLCN.2024.3449834
Marc Jean;Murat Yuksel;Xun Gong
The increasing demand for gigabit-per-second speeds and higher wireless node density is driving the need for spatial reuse and the utilization of higher frequencies above the legacy sub-6 GHz bands. Since these super-6 GHz bands experience high path loss, directional beamforming has been the main method of access to the large amount of bandwidth available at these higher frequencies. Hence, the programming of wireless beams with specific directions is emerging as a requirement for software-defined radio (SDR) platforms. To address this need, we introduce an affordable millimeter-wave (mmWave) testbed. Using a multi-threaded software architecture, the testbed allows for the convenient programming of mmWave beam directions using a high-level programming language, while also providing access to machine learning (ML) libraries as well as SDR methods traditionally deployed in Universal Software Radio Peripheral (USRP) devices. To showcase the potential of the testbed, we tackle the Angle-of-Arrival (AoA) detection problem using reinforcement learning (RL) methods on the receiver side. AoA detection and direction finding is a crucial need for the emerging use of super-6 GHz spectra. We design and implement Q-learning, Double Q-learning, and Deep Q-learning algorithms that passively inspect the Received Signal Strength (RSS) of the mmWave beam and autonomously determine the predicted AoA. The results indicate the feasibility of programming directionality of the wireless beams via ML-based methods as well as solving difficult problems pertaining to emerging directional wireless systems.
对每秒千兆位速度和更高无线节点密度的需求不断增长，推动了对空间重用和利用传统 6 GHz 以下频段以上更高频率的需求。由于这些超 6 GHz 频段的路径损耗较高，定向波束成形一直是利用这些较高频率的大量带宽的主要方法。因此，软件定义无线电（SDR）平台需要对特定方向的无线波束进行编程。为了满足这一需求，我们推出了一种经济实惠的毫米波（mmWave）测试平台。该测试平台采用多线程软件架构，可使用高级编程语言方便地对毫米波波束方向进行编程，同时还可访问机器学习（ML）库以及传统上部署在通用软件无线电外设（USRP）设备中的 SDR 方法。为了展示该测试平台的潜力，我们在接收端使用强化学习（RL）方法解决了到达角（AoA）检测问题。AoA检测和测向是超6 GHz频谱新兴应用的关键需求。我们设计并实施了 Q-learning、Double Q-learning 和 Deep Q-learning 算法，这些算法可被动检测毫米波波束的接收信号强度 (RSS)，并自主确定预测的 AoA。研究结果表明，通过基于 ML 的方法对无线波束的方向性进行编程以及解决与新兴定向无线系统相关的难题是可行的。
{"title":"ML-Enabled Millimeter-Wave Software-Defined Radio With Programmable Directionality","authors":"Marc Jean;Murat Yuksel;Xun Gong","doi":"10.1109/TMLCN.2024.3449834","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3449834","url":null,"abstract":"The increasing demand for gigabit-per-second speeds and higher wireless node density is driving the need for spatial reuse and the utilization of higher frequencies above the legacy sub-6 GHz bands. Since these super-6 GHz bands experience high path loss, directional beamforming has been the main method of access to the large amount of bandwidth available at these higher frequencies. Hence, the programming of wireless beams with specific directions is emerging as a requirement for software-defined radio (SDR) platforms. To address this need, we introduce an affordable millimeter-wave (mmWave) testbed. Using a multi-threaded software architecture, the testbed allows for the convenient programming of mmWave beam directions using a high-level programming language, while also providing access to machine learning (ML) libraries as well as SDR methods traditionally deployed in Universal Software Radio Peripheral (USRP) devices. To showcase the potential of the testbed, we tackle the Angle-of-Arrival (AoA) detection problem using reinforcement learning (RL) methods on the receiver side. AoA detection and direction finding is a crucial need for the emerging use of super-6 GHz spectra. We design and implement Q-learning, Double Q-learning, and Deep Q-learning algorithms that passively inspect the Received Signal Strength (RSS) of the mmWave beam and autonomously determine the predicted AoA. The results indicate the feasibility of programming directionality of the wireless beams via ML-based methods as well as solving difficult problems pertaining to emerging directional wireless systems.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1159-1177"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123044","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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