Pub Date : 2024-10-04DOI: 10.1109/TMLCN.2024.3474639
Jakob Hoydis;Fayçal Aït Aoudia;Sebastian Cammerer;Florian Euchner;Merlin Nimier-David;Stephan Ten Brink;Alexander Keller
Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and environment-specific channel impulse responses (CIRs). While acquiring accurate scene geometries is now relatively straightforward, determining material characteristics requires precise calibration using channel measurements. We therefore introduce a novel gradient-based calibration method, complemented by differentiable parametrizations of material properties, scattering and antenna patterns. Our method seamlessly integrates with differentiable ray tracers that enable the computation of derivatives of CIRs with respect to these parameters. Essentially, we approach field computation as a large computational graph wherein parameters are trainable akin to weights of a neural network (NN). We have validated our method using both synthetic data and real-world indoor channel measurements, employing a distributed multiple-input multiple-output (MIMO) channel sounder.
{"title":"Learning Radio Environments by Differentiable Ray Tracing","authors":"Jakob Hoydis;Fayçal Aït Aoudia;Sebastian Cammerer;Florian Euchner;Merlin Nimier-David;Stephan Ten Brink;Alexander Keller","doi":"10.1109/TMLCN.2024.3474639","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3474639","url":null,"abstract":"Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and environment-specific channel impulse responses (CIRs). While acquiring accurate scene geometries is now relatively straightforward, determining material characteristics requires precise calibration using channel measurements. We therefore introduce a novel gradient-based calibration method, complemented by differentiable parametrizations of material properties, scattering and antenna patterns. Our method seamlessly integrates with differentiable ray tracers that enable the computation of derivatives of CIRs with respect to these parameters. Essentially, we approach field computation as a large computational graph wherein parameters are trainable akin to weights of a neural network (NN). We have validated our method using both synthetic data and real-world indoor channel measurements, employing a distributed multiple-input multiple-output (MIMO) channel sounder.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1527-1539"},"PeriodicalIF":0.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442991","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}
Network slicing is a pivotal paradigm in wireless networks enabling customized services to users and applications. Yet, intelligent jamming attacks threaten the performance of network slicing. In this paper, we focus on the security aspect of network slicing over a deep transfer reinforcement learning (DTRL) enabled scenario. We first demonstrate how a deep reinforcement learning (DRL)-enabled jamming attack exposes potential risks. In particular, the attacker can intelligently jam resource blocks (RBs) reserved for slices by monitoring transmission signals and perturbing the assigned resources. Then, we propose a DRL-driven mitigation model to mitigate the intelligent attacker. Specifically, the defense mechanism generates interference on unallocated RBs where another antenna is used for transmitting powerful signals. This causes the jammer to consider these RBs as allocated RBs and generate interference for those instead of the allocated RBs. The analysis revealed that the intelligent DRL-enabled jamming attack caused a significant 50% degradation in network throughput and 60% increase in latency in comparison with the no-attack scenario. However, with the implemented mitigation measures, we observed 80% improvement in network throughput and 70% reduction in latency in comparison to the under-attack scenario.
{"title":"Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing","authors":"Shavbo Salehi;Hao Zhou;Medhat Elsayed;Majid Bavand;Raimundas Gaigalas;Yigit Ozcan;Melike Erol-Kantarci","doi":"10.1109/TMLCN.2024.3470760","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3470760","url":null,"abstract":"Network slicing is a pivotal paradigm in wireless networks enabling customized services to users and applications. Yet, intelligent jamming attacks threaten the performance of network slicing. In this paper, we focus on the security aspect of network slicing over a deep transfer reinforcement learning (DTRL) enabled scenario. We first demonstrate how a deep reinforcement learning (DRL)-enabled jamming attack exposes potential risks. In particular, the attacker can intelligently jam resource blocks (RBs) reserved for slices by monitoring transmission signals and perturbing the assigned resources. Then, we propose a DRL-driven mitigation model to mitigate the intelligent attacker. Specifically, the defense mechanism generates interference on unallocated RBs where another antenna is used for transmitting powerful signals. This causes the jammer to consider these RBs as allocated RBs and generate interference for those instead of the allocated RBs. The analysis revealed that the intelligent DRL-enabled jamming attack caused a significant 50% degradation in network throughput and 60% increase in latency in comparison with the no-attack scenario. However, with the implemented mitigation measures, we observed 80% improvement in network throughput and 70% reduction in latency in comparison to the under-attack scenario.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1492-1508"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10699421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the 6G wireless era, the strategical deployment of Virtual Network Functions (VNFs) within a network infrastructure that optimizes resource utilization while fulfilling performance criteria is critical for successfully implementing the Network Function Virtualization (NFV) paradigm across the Edge-to-Cloud continuum. This is especially prominent when resource fragmentation –where available resources become isolated and underutilized– becomes an issue due to the frequent reallocations of VNFs. However, traditional optimization methods often struggle to deal with the dynamic and complex nature of the VNF placement problem when fragmentation is considered. This study proposes a novel online VNF placement approach for Edge/Cloud infrastructures that utilizes Deep Reinforcement Learning (DRL) and Reward Constrained Policy Optimization (RCPO) to address this problem. We combine DRL’s adaptability with RCPO’s constraint incorporation capabilities to ensure that the learned policies satisfy the performance and resource constraints while minimizing resource fragmentation. Specifically, the VNF placement problem is first formulated as an offline-constrained optimization problem, and then we devise an online solver using Neural Combinatorial Optimization (NCO). Our method incorporates a metric called Resource Fragmentation Degree (RFD) to quantify fragmentation in the network. Using this metric and RCPO, our NCO agent is trained to make intelligent placement decisions that reduce fragmentation and optimize resource utilization. An error correction heuristic complements the robustness of the proposed framework. Through extensive testing in a simulated environment, the proposed approach is shown to outperform state-of-the-art VNF placement techniques when it comes to minimizing resource fragmentation under constraint satisfaction guarantees.
{"title":"Optimizing Resource Fragmentation in Virtual Network Function Placement Using Deep Reinforcement Learning","authors":"Ramy Mohamed;Marios Avgeris;Aris Leivadeas;Ioannis Lambadaris","doi":"10.1109/TMLCN.2024.3469131","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3469131","url":null,"abstract":"In the 6G wireless era, the strategical deployment of Virtual Network Functions (VNFs) within a network infrastructure that optimizes resource utilization while fulfilling performance criteria is critical for successfully implementing the Network Function Virtualization (NFV) paradigm across the Edge-to-Cloud continuum. This is especially prominent when resource fragmentation –where available resources become isolated and underutilized– becomes an issue due to the frequent reallocations of VNFs. However, traditional optimization methods often struggle to deal with the dynamic and complex nature of the VNF placement problem when fragmentation is considered. This study proposes a novel online VNF placement approach for Edge/Cloud infrastructures that utilizes Deep Reinforcement Learning (DRL) and Reward Constrained Policy Optimization (RCPO) to address this problem. We combine DRL’s adaptability with RCPO’s constraint incorporation capabilities to ensure that the learned policies satisfy the performance and resource constraints while minimizing resource fragmentation. Specifically, the VNF placement problem is first formulated as an offline-constrained optimization problem, and then we devise an online solver using Neural Combinatorial Optimization (NCO). Our method incorporates a metric called Resource Fragmentation Degree (RFD) to quantify fragmentation in the network. Using this metric and RCPO, our NCO agent is trained to make intelligent placement decisions that reduce fragmentation and optimize resource utilization. An error correction heuristic complements the robustness of the proposed framework. Through extensive testing in a simulated environment, the proposed approach is shown to outperform state-of-the-art VNF placement techniques when it comes to minimizing resource fragmentation under constraint satisfaction guarantees.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1475-1491"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695455","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397282","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 : 2024-09-26DOI: 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}
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