Pub Date : 2024-11-29DOI: 10.1109/OJCOMS.2024.3509777
Fahri Wisnu Murti;Samad Ali;Matti Latva-Aho
Multi-access Edge Computing (MEC) can be implemented together with Open Radio Access Network (O-RAN) to offer low-cost deployment and bring services closer to end-users. In this paper, the joint orchestration of O-RAN and MEC using a Bayesian deep reinforcement learning (RL) framework is proposed. The framework jointly controls the O-RAN functional splits, O-RAN/MEC computing resource allocation, hosting locations, and data flow routing across geo-distributed platforms. The goal is to minimize the long-term total network operation cost and maximize MEC performance criterion while adapting to varying demands and resource availability. This orchestration problem is formulated as a Markov decision process (MDP). However, finding the exact model of the underlying O-RAN/MEC system is impractical since the system shares the same resources, serves heterogeneous demands, and its parameters have non-trivial relationships. Moreover, the formulated MDP results in a large state space with multidimensional discrete actions. To address these challenges, a model-free RL agent based on a combination of Double Deep Q-network (DDQN) with action branching is proposed. Furthermore, an efficient exploration-exploitation strategy under a Bayesian learning framework is leveraged to improve learning performance and expedite convergence. Trace-driven simulations are performed using an O-RAN-compliant model. The results show that our approach is data-efficient (i.e., converges significantly faster), increases the reward by 32% compared to its non-Bayesian version, and outperforms Deep Deterministic Policy Gradient by up to 41%.
{"title":"A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration","authors":"Fahri Wisnu Murti;Samad Ali;Matti Latva-Aho","doi":"10.1109/OJCOMS.2024.3509777","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3509777","url":null,"abstract":"Multi-access Edge Computing (MEC) can be implemented together with Open Radio Access Network (O-RAN) to offer low-cost deployment and bring services closer to end-users. In this paper, the joint orchestration of O-RAN and MEC using a Bayesian deep reinforcement learning (RL) framework is proposed. The framework jointly controls the O-RAN functional splits, O-RAN/MEC computing resource allocation, hosting locations, and data flow routing across geo-distributed platforms. The goal is to minimize the long-term total network operation cost and maximize MEC performance criterion while adapting to varying demands and resource availability. This orchestration problem is formulated as a Markov decision process (MDP). However, finding the exact model of the underlying O-RAN/MEC system is impractical since the system shares the same resources, serves heterogeneous demands, and its parameters have non-trivial relationships. Moreover, the formulated MDP results in a large state space with multidimensional discrete actions. To address these challenges, a model-free RL agent based on a combination of Double Deep Q-network (DDQN) with action branching is proposed. Furthermore, an efficient exploration-exploitation strategy under a Bayesian learning framework is leveraged to improve learning performance and expedite convergence. Trace-driven simulations are performed using an O-RAN-compliant model. The results show that our approach is data-efficient (i.e., converges significantly faster), increases the reward by 32% compared to its non-Bayesian version, and outperforms Deep Deterministic Policy Gradient by up to 41%.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7685-7700"},"PeriodicalIF":6.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10771978","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825903","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}
Recent advancements in low Earth orbit (LEO) satellite technology have facilitated a substantial increase in the number of Earth observation (EO) satellites launched. However, transmitting voluminous imagery generated by these EO satellites to the ground still faces the challenges of limited satellite resources and dynamic satellite networks. To address this problem, we propose SEC-DT, a S atellite E dge C omputing (SEC) enabled computation-aware dynamic D ata T ransmission framework for jointly optimizing the routing selection and in-orbit imagery compression adoption. Specifically, we formulate an online optimization problem for concurrently delivering data from multiple EO satellites in a single EO mission, aiming to minimize the overall transmission and computation latency while ensuring the decent quality of the final downloaded data. Then we cast the problem as a partially observable Markov decision process and adopt an augmented multi-agent reinforcement learning (MARL) algorithm to solve this intractable online decision problem. Considering the natural graph structure of the satellite network, we innovatively integrate the graph neural network (GNN) into the MARL algorithm to form a GNN-assisted MARL framework, wherein GNN can capture the enriched semantic information present in satellite topology to achieve the fusion of diverse environmental states, which is beneficial for improving the decision-making effectiveness of agents. Finally, we conduct extensive experiments and ablation studies in various settings based on real-world datasets of StarLink and SkySat constellations. The experimental results have demonstrated the scalability and excellent performance of our algorithm compared with other baseline schemes.
{"title":"SEC-DT: Satellite Edge Computing Enabled Dynamic Data Transmission Based on GNN-Assisted MARL for Earth Observation Missions","authors":"Yuyang Xiao;Zhiwei Zhai;Shuai Yu;Zhenlong Xu;Lin Li;Fei Zhang;Lu Cao;Xu Chen","doi":"10.1109/OJCOMS.2024.3509440","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3509440","url":null,"abstract":"Recent advancements in low Earth orbit (LEO) satellite technology have facilitated a substantial increase in the number of Earth observation (EO) satellites launched. However, transmitting voluminous imagery generated by these EO satellites to the ground still faces the challenges of limited satellite resources and dynamic satellite networks. To address this problem, we propose SEC-DT, a \u0000<underline>S</u>\u0000atellite \u0000<underline>E</u>\u0000dge \u0000<underline>C</u>\u0000omputing (SEC) enabled computation-aware dynamic \u0000<underline>D</u>\u0000ata \u0000<underline>T</u>\u0000ransmission framework for jointly optimizing the routing selection and in-orbit imagery compression adoption. Specifically, we formulate an online optimization problem for concurrently delivering data from multiple EO satellites in a single EO mission, aiming to minimize the overall transmission and computation latency while ensuring the decent quality of the final downloaded data. Then we cast the problem as a partially observable Markov decision process and adopt an augmented multi-agent reinforcement learning (MARL) algorithm to solve this intractable online decision problem. Considering the natural graph structure of the satellite network, we innovatively integrate the graph neural network (GNN) into the MARL algorithm to form a GNN-assisted MARL framework, wherein GNN can capture the enriched semantic information present in satellite topology to achieve the fusion of diverse environmental states, which is beneficial for improving the decision-making effectiveness of agents. Finally, we conduct extensive experiments and ablation studies in various settings based on real-world datasets of StarLink and SkySat constellations. The experimental results have demonstrated the scalability and excellent performance of our algorithm compared with other baseline schemes.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"288-301"},"PeriodicalIF":6.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10771987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938350","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}
beyond diagonal reconfigurable intelligent surfaces (BD-RIS) generalizes and goes beyond conventional diagonal reconfigurable intelligent surfaces (D-RIS) by interconnecting elements to generate beyond diagonal scattering matrices, which significantly strengthen the wireless channels. In this work, we use BD-RIS for passive multiuser beamforming in multiuser multiple-input-single-output (MU-MISO) systems. Specifically, we design the scattering matrix of BD-RIS to either maximize the sum received signal power at the users following maximum ratio transmission (MRT), or to nullify the interference at the users following zero forcing (ZF). To control the BD-RIS circuit topology complexity, we present the scattering matrix designs for the single/group/fully-connected BD-RIS architectures. Furthermore, we investigate uniform/optimized power allocation and ZF precoding at the base station (BS). Numerical results show that BD-RIS improves the interference nulling capability and sum rate with fewer reflecting elements (REs) compared to D-RIS. In addition, at moderate to high signal to noise ratios (SNRs), passive interference nulling reduces the complexity at the BS by relaxing the need for precoding or water-filling power allocation design. Furthermore, the passive MRT with ZF precoding achieves a tight sum rate performance to the joint design considering MU-MISO scenarios with many REs while maintaining low computational complexity and simplifying the channel estimation.
{"title":"Beyond Diagonal RIS: Passive Maximum Ratio Transmission and Interference Nulling Enabler","authors":"Hamad Yahya;Hongyu Li;Matteo Nerini;Bruno Clerckx;Mérouane Debbah","doi":"10.1109/OJCOMS.2024.3509220","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3509220","url":null,"abstract":"beyond diagonal reconfigurable intelligent surfaces (BD-RIS) generalizes and goes beyond conventional diagonal reconfigurable intelligent surfaces (D-RIS) by interconnecting elements to generate beyond diagonal scattering matrices, which significantly strengthen the wireless channels. In this work, we use BD-RIS for passive multiuser beamforming in multiuser multiple-input-single-output (MU-MISO) systems. Specifically, we design the scattering matrix of BD-RIS to either maximize the sum received signal power at the users following maximum ratio transmission (MRT), or to nullify the interference at the users following zero forcing (ZF). To control the BD-RIS circuit topology complexity, we present the scattering matrix designs for the single/group/fully-connected BD-RIS architectures. Furthermore, we investigate uniform/optimized power allocation and ZF precoding at the base station (BS). Numerical results show that BD-RIS improves the interference nulling capability and sum rate with fewer reflecting elements (REs) compared to D-RIS. In addition, at moderate to high signal to noise ratios (SNRs), passive interference nulling reduces the complexity at the BS by relaxing the need for precoding or water-filling power allocation design. Furthermore, the passive MRT with ZF precoding achieves a tight sum rate performance to the joint design considering MU-MISO scenarios with many REs while maintaining low computational complexity and simplifying the channel estimation.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7613-7627"},"PeriodicalIF":6.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10771739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we study a autonomous aerial vehicle (AAV) inspection system. In this system, the AAV flies to all inspection points in a certain area for patrol inspection, and the energy of inspection AAV is limited. Our goal is to optimize the communication quality of the AAV by planning the inspection sequence and flight trajectory, so as to ensure that the AAV can complete the inspection task and minimize the outage time subject to limited energy of the AAV. To solve this problem, we propose a hybrid algorithm, which consists of simulated annealing (SA) algorithm and Dueling Double Deep Q Network (D3QN) algorithm. The SA algorithm is used to obtain the inspection sequence of the AAV with the most energy saving. On this basis, the D3QN algorithm is used to optimize the flight trajectory of the energy-limited inspection AAV. To prove the effectiveness of the sub-optimal solution obtained by our proposed algorithm, we use several algorithms as a comparison. Numerical results show that the proposed algorithm is effective in optimizing the communication quality of the inspection AAV with limited energy, and its performance is improved by about 15%-50% compared with other benchmarks.
本文研究了一种自主飞行器（AAV）检测系统。在该系统中，AAV会飞到一定区域内的所有检查点进行巡逻检查，并且检查AAV的能量是有限的。我们的目标是通过规划AAV的检查顺序和飞行轨迹来优化AAV的通信质量，在AAV能量有限的情况下，确保AAV能够完成检查任务并最大限度地减少停机时间。为了解决这一问题，我们提出了一种混合算法，该算法由模拟退火（SA）算法和Dueling Double Deep Q Network （D3QN）算法组成。采用SA算法得到最节能的AAV检测序列。在此基础上，采用D3QN算法对能量受限型无人侦察机的飞行轨迹进行优化。为了证明本文算法得到的次优解的有效性，我们用几种算法进行了比较。数值结果表明，该算法在有限能量条件下能有效地优化检测AAV的通信质量，性能比其他基准测试提高15% ~ 50%左右。
{"title":"Optimization of Communication Quality for Energy-Limited Inspection AAV: A Hybrid Algorithm","authors":"Wei Wang;Jiangling Cao;Dingcheng Yang;Hao He;Zhihai Xu","doi":"10.1109/OJCOMS.2024.3507818","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3507818","url":null,"abstract":"In this paper, we study a autonomous aerial vehicle (AAV) inspection system. In this system, the AAV flies to all inspection points in a certain area for patrol inspection, and the energy of inspection AAV is limited. Our goal is to optimize the communication quality of the AAV by planning the inspection sequence and flight trajectory, so as to ensure that the AAV can complete the inspection task and minimize the outage time subject to limited energy of the AAV. To solve this problem, we propose a hybrid algorithm, which consists of simulated annealing (SA) algorithm and Dueling Double Deep Q Network (D3QN) algorithm. The SA algorithm is used to obtain the inspection sequence of the AAV with the most energy saving. On this basis, the D3QN algorithm is used to optimize the flight trajectory of the energy-limited inspection AAV. To prove the effectiveness of the sub-optimal solution obtained by our proposed algorithm, we use several algorithms as a comparison. Numerical results show that the proposed algorithm is effective in optimizing the communication quality of the inspection AAV with limited energy, and its performance is improved by about 15%-50% compared with other benchmarks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7900-7912"},"PeriodicalIF":6.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859018","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 : 2024-11-27DOI: 10.1109/OJCOMS.2024.3508463
José David Vega Sánchez;Henry Ramiro Carvajal Mora;Nathaly Verónica Orozco Garzón;F. Javier López-Martínez
Compact Ultramassive Antenna Array (CUMA) is a pioneering paradigm that leverages the flexibility of the Fluid Antenna System (FAS) to enable a simple multiple access scheme for massive connectivity without the need for precoding, power control at the base station or interference mitigation in each user’s equipment. In order to overcome the mathematical intricacy required to analyze their performance, we use an asymptotic matching approach to relax such complexity with remarkable accuracy. First, we analyze the performance of the CUMA network in terms of the outage probability (OP) and the ergodic rate (ER), deriving simple and highly accurate closed-form approximations of the channel statistics. Then, we evaluate the potential of the CUMA scheme to provide secure multi-user communications from a physical layer security perspective. Leveraging a tight approximation to the signal-to-interference-ratio (SIR) distribution, we derive closed-form expressions for the secrecy outage probability (SOP). We observe that the baseline CUMA (without side information processing) exhibits limited performance when eavesdroppers are equipped with a CUMA of the same type. To improve their secure performance, we suggest that a simple imperfect interference cancelation mechanism at the legitimate receiver may substantially increase the secrecy performance. Monte Carlo simulations validate our approximations and demonstrate their accuracy under different CUMA-based scenarios.
{"title":"Reliable and Secure Communications Through Compact Ultra-Massive Antenna Arrays","authors":"José David Vega Sánchez;Henry Ramiro Carvajal Mora;Nathaly Verónica Orozco Garzón;F. Javier López-Martínez","doi":"10.1109/OJCOMS.2024.3508463","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3508463","url":null,"abstract":"Compact Ultramassive Antenna Array (CUMA) is a pioneering paradigm that leverages the flexibility of the Fluid Antenna System (FAS) to enable a simple multiple access scheme for massive connectivity without the need for precoding, power control at the base station or interference mitigation in each user’s equipment. In order to overcome the mathematical intricacy required to analyze their performance, we use an asymptotic matching approach to relax such complexity with remarkable accuracy. First, we analyze the performance of the CUMA network in terms of the outage probability (OP) and the ergodic rate (ER), deriving simple and highly accurate closed-form approximations of the channel statistics. Then, we evaluate the potential of the CUMA scheme to provide secure multi-user communications from a physical layer security perspective. Leveraging a tight approximation to the signal-to-interference-ratio (SIR) distribution, we derive closed-form expressions for the secrecy outage probability (SOP). We observe that the baseline CUMA (without side information processing) exhibits limited performance when eavesdroppers are equipped with a CUMA of the same type. To improve their secure performance, we suggest that a simple imperfect interference cancelation mechanism at the legitimate receiver may substantially increase the secrecy performance. Monte Carlo simulations validate our approximations and demonstrate their accuracy under different CUMA-based scenarios.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7641-7652"},"PeriodicalIF":6.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770248","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825937","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 : 2024-11-27DOI: 10.1109/OJCOMS.2024.3507555
Elena Fernandez-Nino;Juan A. Fraire;Adriano Camps;Joan A. Ruiz-De-Azua
One of the space communications industry’s current focuses is developing high-throughput communication terminals for satellite-to-satellite communication links. Optical inter-satellite links provide high data rates, long-range, and robustness against interferences, and they do not require frequency licensing as radiofrequency communication systems. Nevertheless, as the uncertain location of the receiver is comprised in an area larger than that illuminated by the transmitting laser, the pointing accuracy is a critical element in the success of the optical link establishment and maintenance, requiring a pointing, acquisition, and tracking mechanism. The acquisition process is the most time-consuming of the pointing processes, limiting the time available to send data, especially in highly dynamic networks. This paper focuses on reducing the acquisition time by reducing the initial satellite position uncertainty. To this end, a hybrid system that combines RF and optical technologies in a single communication module is proposed. Whereas the control plane is managed via the RF link to exchange more precise global navigation data, the optical link corresponds to the data plane in which payload data is exchanged. The pointing between two satellites is simulated to analyze its behavior, considering the error of cumulative orbital propagation data and global navigation satellite system data. This work also analyzes the cumulative error produced by the propagation of the TLEs over time. Finally, the results show how a system that relies on the exchange of global navigation satellite system positioning data achieves up to 99.45% better-pointing accuracy than a system that bases positioning data on TLE propagation.
{"title":"RF-Assisted Uncertainty Cone Reduction in Free-Space Optical Inter-Satellite Links","authors":"Elena Fernandez-Nino;Juan A. Fraire;Adriano Camps;Joan A. Ruiz-De-Azua","doi":"10.1109/OJCOMS.2024.3507555","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3507555","url":null,"abstract":"One of the space communications industry’s current focuses is developing high-throughput communication terminals for satellite-to-satellite communication links. Optical inter-satellite links provide high data rates, long-range, and robustness against interferences, and they do not require frequency licensing as radiofrequency communication systems. Nevertheless, as the uncertain location of the receiver is comprised in an area larger than that illuminated by the transmitting laser, the pointing accuracy is a critical element in the success of the optical link establishment and maintenance, requiring a pointing, acquisition, and tracking mechanism. The acquisition process is the most time-consuming of the pointing processes, limiting the time available to send data, especially in highly dynamic networks. This paper focuses on reducing the acquisition time by reducing the initial satellite position uncertainty. To this end, a hybrid system that combines RF and optical technologies in a single communication module is proposed. Whereas the control plane is managed via the RF link to exchange more precise global navigation data, the optical link corresponds to the data plane in which payload data is exchanged. The pointing between two satellites is simulated to analyze its behavior, considering the error of cumulative orbital propagation data and global navigation satellite system data. This work also analyzes the cumulative error produced by the propagation of the TLEs over time. Finally, the results show how a system that relies on the exchange of global navigation satellite system positioning data achieves up to 99.45% better-pointing accuracy than a system that bases positioning data on TLE propagation.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7598-7612"},"PeriodicalIF":6.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10769552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825836","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 : 2024-11-27DOI: 10.1109/OJCOMS.2024.3507378
Seung-Hoon Park;Tahira Mahboob;Syed Tariq Shah;Mahmoud A. Shawky;Minseok Choi;Min Young Chung
Device-to–device (D2D) communications are based on direct signaling and data transmission within wireless devices. Furthermore, it facilitates mission-critical services for public safety (PS), vehicle-to–vehicle (V2V) or drones. These D2D applications need nationwide coverage across multiple operators. The existing roaming system, however, does not provide sufficient trust for D2D roaming scenarios since the direct link between devices is difficult to monitor in the core network side. Therefore, a novel framework to support D2D roaming is proposed inspired by blockchain-based trust systems. The framework consists of an authentication for D2D user equipment (UE) access, authorization to configure D2D service, and resource pool selection. Also, a dynamic D2D resource pool selection enabled by D2D class awareness is supported. Analytical results show that the proposed dynamic resource pool selection scheme improves capacity in the form of decoding performance.
{"title":"Blockchain-Assisted Dynamic Resource Pool Selection for D2D Roaming Scenarios","authors":"Seung-Hoon Park;Tahira Mahboob;Syed Tariq Shah;Mahmoud A. Shawky;Minseok Choi;Min Young Chung","doi":"10.1109/OJCOMS.2024.3507378","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3507378","url":null,"abstract":"Device-to–device (D2D) communications are based on direct signaling and data transmission within wireless devices. Furthermore, it facilitates mission-critical services for public safety (PS), vehicle-to–vehicle (V2V) or drones. These D2D applications need nationwide coverage across multiple operators. The existing roaming system, however, does not provide sufficient trust for D2D roaming scenarios since the direct link between devices is difficult to monitor in the core network side. Therefore, a novel framework to support D2D roaming is proposed inspired by blockchain-based trust systems. The framework consists of an authentication for D2D user equipment (UE) access, authorization to configure D2D service, and resource pool selection. Also, a dynamic D2D resource pool selection enabled by D2D class awareness is supported. Analytical results show that the proposed dynamic resource pool selection scheme improves capacity in the form of decoding performance.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7542-7551"},"PeriodicalIF":6.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10769550","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825771","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 : 2024-11-25DOI: 10.1109/OJCOMS.2024.3506214
Mumin Adam;Uthman Baroudi
The rapid advancement of Internet of Things (IoT) technology has transformed the digital landscape, enabling unprecedented connectivity between devices, people, and services. Traditionally, IoT-generated data was processed through centralized, cloud-based machine learning (ML) systems, raising significant privacy, security, and network bandwidth concerns. Federated Learning (FL) presents a viable alternative by transmitting only model parameters while preserving local data privacy. Despite the growing body of research, there remains a gap in comprehensive studies on FL-enabled IoT systems. This review provides an in-depth examination of the integration of FL with IoT, highlighting how FL enhances the efficiency, robustness, and adaptability of IoT systems. The paper introduces the foundational principles of FL, followed by an exploration of its key benefits in decentralized IoT applications. It presents a comparative analysis of FL-IoT architectures using quantitative metrics and proposes a taxonomy that clarifies the complexities and variations in FL-enabled IoT systems. The challenges of deploying FL in IoT environments are discussed, along with current trends and solutions aimed at overcoming these hurdles. Furthermore, the review explores the integration of FL with emerging technologies, including foundational models (FMs), green and sustainable 6th-generation (6G) IoT networks, and deep reinforcement learning (DRL), emphasizing their role in enhancing FL’s efficiency and resilience. It also covers FL frameworks and benchmarks, providing a valuable resource for researchers and practitioners in the field The article concludes by identifying promising research directions that are expected to drive future advancements in this dynamic and expanding field.
{"title":"Federated Learning for IoT: Applications, Trends, Taxonomy, Challenges, Current Solutions, and Future Directions","authors":"Mumin Adam;Uthman Baroudi","doi":"10.1109/OJCOMS.2024.3506214","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3506214","url":null,"abstract":"The rapid advancement of Internet of Things (IoT) technology has transformed the digital landscape, enabling unprecedented connectivity between devices, people, and services. Traditionally, IoT-generated data was processed through centralized, cloud-based machine learning (ML) systems, raising significant privacy, security, and network bandwidth concerns. Federated Learning (FL) presents a viable alternative by transmitting only model parameters while preserving local data privacy. Despite the growing body of research, there remains a gap in comprehensive studies on FL-enabled IoT systems. This review provides an in-depth examination of the integration of FL with IoT, highlighting how FL enhances the efficiency, robustness, and adaptability of IoT systems. The paper introduces the foundational principles of FL, followed by an exploration of its key benefits in decentralized IoT applications. It presents a comparative analysis of FL-IoT architectures using quantitative metrics and proposes a taxonomy that clarifies the complexities and variations in FL-enabled IoT systems. The challenges of deploying FL in IoT environments are discussed, along with current trends and solutions aimed at overcoming these hurdles. Furthermore, the review explores the integration of FL with emerging technologies, including foundational models (FMs), green and sustainable 6th-generation (6G) IoT networks, and deep reinforcement learning (DRL), emphasizing their role in enhancing FL’s efficiency and resilience. It also covers FL frameworks and benchmarks, providing a valuable resource for researchers and practitioners in the field The article concludes by identifying promising research directions that are expected to drive future advancements in this dynamic and expanding field.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7842-7877"},"PeriodicalIF":6.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858735","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 : 2024-11-25DOI: 10.1109/OJCOMS.2024.3506481
Alexander James Fernandes;Ioannis N. Psaromiligkos
Channel estimation is one of the main challenges for reconfigurable intelligent surface (RIS) assisted communication systems with passive reflective elements due to the high number of parameters to estimate. In this paper, we consider channel estimation for a MIMO FD RIS-assisted wireless communication system and use tensor (multidimensional array) signal modelling techniques to estimate all channel state information (CSI) involving the self-interference, direct-path, and the RIS assisted channel links. We model the received signal as a tensor composed of two CANDECOMP/PARAFAC (CP) decomposition terms for the non-RIS and the RIS assisted links. Based on this model we extend the alternating least squares algorithm to jointly estimate all channels, then derive the corresponding Cramér-Rao Bounds (CRB). Numerical results show that compared to recent previous works which estimate the non-RIS and RIS links during separate training stages, our method provides a more accurate estimate by efficiently using all pilots transmitted throughout the full training duration without turning the RIS off when comparing the same number of total pilots transmitted. For a sufficient number of transmitted pilots, the proposed method’s accuracy comes close to the CRB for the RIS channels and attains the CRB for the direct-path and self-interference channels.
{"title":"Tensor Signal Modeling and Channel Estimation for Reconfigurable Intelligent Surface-Assisted Full-Duplex MIMO","authors":"Alexander James Fernandes;Ioannis N. Psaromiligkos","doi":"10.1109/OJCOMS.2024.3506481","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3506481","url":null,"abstract":"Channel estimation is one of the main challenges for reconfigurable intelligent surface (RIS) assisted communication systems with passive reflective elements due to the high number of parameters to estimate. In this paper, we consider channel estimation for a MIMO FD RIS-assisted wireless communication system and use tensor (multidimensional array) signal modelling techniques to estimate all channel state information (CSI) involving the self-interference, direct-path, and the RIS assisted channel links. We model the received signal as a tensor composed of two CANDECOMP/PARAFAC (CP) decomposition terms for the non-RIS and the RIS assisted links. Based on this model we extend the alternating least squares algorithm to jointly estimate all channels, then derive the corresponding Cramér-Rao Bounds (CRB). Numerical results show that compared to recent previous works which estimate the non-RIS and RIS links during separate training stages, our method provides a more accurate estimate by efficiently using all pilots transmitted throughout the full training duration without turning the RIS off when comparing the same number of total pilots transmitted. For a sufficient number of transmitted pilots, the proposed method’s accuracy comes close to the CRB for the RIS channels and attains the CRB for the direct-path and self-interference channels.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7668-7684"},"PeriodicalIF":6.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767385","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825838","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 : 2024-11-25DOI: 10.1109/OJCOMS.2024.3506033
Prasoon Raghuwanshi;Onel Luis Alcaraz López;Neelesh B. Mehta;Hirley Alves;Matti Latva-Aho
Efficient Random Access (RA) is critical for enabling reliable communication in Industrial Internet of Things (IIoT) networks. Herein, we propose a deep reinforcement learning-based distributed RA scheme, entitled Neural Network-Based Bandit (NNBB), for the IIoT alarm scenario. In such a scenario, devices may detect a common critical event, and the goal is to ensure the alarm information is delivered successfully from at least one device. The proposed NNBB scheme is implemented at each device, where it trains itself online and establishes implicit inter-device coordination to achieve the common goal. We devise a procedure for acquiring a valuable context for NNBB, which then uses a deep neural network to process this context and let devices determine their action. Each possible transmission pattern, i.e., transmit channel(s) allocation, constitutes a feasible action. Our simulation results show that as the number of devices in the network increases, so does the performance gain of the NNBB compared to the Multi-Armed Bandit (MAB) RA benchmark. For instance, NNBB experiences a 7% success rate drop when there are four channels and the number of devices increases from 10 to 60, while MAB faces a 25% drop.
{"title":"Neural Network-Based Bandit: A Medium Access Control for the IIoT Alarm Scenario","authors":"Prasoon Raghuwanshi;Onel Luis Alcaraz López;Neelesh B. Mehta;Hirley Alves;Matti Latva-Aho","doi":"10.1109/OJCOMS.2024.3506033","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3506033","url":null,"abstract":"Efficient Random Access (RA) is critical for enabling reliable communication in Industrial Internet of Things (IIoT) networks. Herein, we propose a deep reinforcement learning-based distributed RA scheme, entitled Neural Network-Based Bandit (NNBB), for the IIoT alarm scenario. In such a scenario, devices may detect a common critical event, and the goal is to ensure the alarm information is delivered successfully from at least one device. The proposed NNBB scheme is implemented at each device, where it trains itself online and establishes implicit inter-device coordination to achieve the common goal. We devise a procedure for acquiring a valuable context for NNBB, which then uses a deep neural network to process this context and let devices determine their action. Each possible transmission pattern, i.e., transmit channel(s) allocation, constitutes a feasible action. Our simulation results show that as the number of devices in the network increases, so does the performance gain of the NNBB compared to the Multi-Armed Bandit (MAB) RA benchmark. For instance, NNBB experiences a 7% success rate drop when there are four channels and the number of devices increases from 10 to 60, while MAB faces a 25% drop.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7511-7524"},"PeriodicalIF":6.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777748","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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