Pub Date : 2024-09-30DOI: 10.1109/JSAC.2024.3459021
Long Luo;Chi Zhang;Hongfang Yu;Zonghang Li;Gang Sun;Shouxi Luo
The hierarchical collaborative learning within Low Earth Orbit (LEO) satellite constellations, termed LEO-HCL, is gaining increasing popularity by integrating intra-orbit Inter-Satellite Links and orbital edge computing to alleviate the latency issues caused by intermittent satellite connectivity in satellite-ground training architectures. However, LEO-HCL systems are confronted with a triad of challenges: the variable topology induced by satellite mobility, limited onboard computing and communication resources, and stringent energy constraints. In response to these challenges, we propose an energy-efficient training algorithm called FedAAC, which adaptively optimizes both aggregation frequency and model compression ratio within the resource-constrained LEO network. We have conducted a theoretical analysis of model convergence and investigated the relationship between convergence, aggregation frequency, and model compression ratio. Building on this analysis, we offer an approximation algorithm that dynamically calculates the optimal aggregation frequency and compression ratio during the training process. Extensive simulations have demonstrated that FedAAC significantly outperforms existing methods, offering enhanced convergence speed and energy efficiency. Compared to prior solutions, FedAAC achieves a 60% reduction in energy consumption, a 70% decrease in training time, and a 52% lower communication overhead.
{"title":"Energy-Efficient Hierarchical Collaborative Learning Over LEO Satellite Constellations","authors":"Long Luo;Chi Zhang;Hongfang Yu;Zonghang Li;Gang Sun;Shouxi Luo","doi":"10.1109/JSAC.2024.3459021","DOIUrl":"10.1109/JSAC.2024.3459021","url":null,"abstract":"The hierarchical collaborative learning within Low Earth Orbit (LEO) satellite constellations, termed LEO-HCL, is gaining increasing popularity by integrating intra-orbit Inter-Satellite Links and orbital edge computing to alleviate the latency issues caused by intermittent satellite connectivity in satellite-ground training architectures. However, LEO-HCL systems are confronted with a triad of challenges: the variable topology induced by satellite mobility, limited onboard computing and communication resources, and stringent energy constraints. In response to these challenges, we propose an energy-efficient training algorithm called FedAAC, which adaptively optimizes both aggregation frequency and model compression ratio within the resource-constrained LEO network. We have conducted a theoretical analysis of model convergence and investigated the relationship between convergence, aggregation frequency, and model compression ratio. Building on this analysis, we offer an approximation algorithm that dynamically calculates the optimal aggregation frequency and compression ratio during the training process. Extensive simulations have demonstrated that FedAAC significantly outperforms existing methods, offering enhanced convergence speed and energy efficiency. Compared to prior solutions, FedAAC achieves a 60% reduction in energy consumption, a 70% decrease in training time, and a 52% lower communication overhead.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3366-3379"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142360309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1109/JSAC.2024.3460083
Su Yao;Yiying Lin;Mu Wang;Ke Xu;Mingwei Xu;Changqiao Xu;Hongke Zhang
With the rapid growth of low earth orbit (LEO) satellites, enabling LEO AI inference becomes a fast-increasing trend. However, due to resource heterogeneity, scheduling complexity, and fast movement, how to decide the place of executing each AI inference task is nontrivial in LEO systems. In this paper, we propose LEOEdge, an edge-assisted AI inference system for LEO satellites. We first introduce the adaptive modeling technologies that automatically generate the model for each satellite according to its computation resources. We then propose a layered scheduling optimization scheme to schedule the AI inference task in a distributed manner. LEOEdge also designs a seamless data transmission scheme to avoid transmission failure due to the LEO satellite movement. We conduct a series of simulation tests to validate the performance of the proposed LEOEdge, in terms of the neural network searching efficiency, average time execution latency, and delivery latency.
{"title":"LEOEdge: A Satellite-Ground Cooperation Platform for the AI Inference in Large LEO Constellation","authors":"Su Yao;Yiying Lin;Mu Wang;Ke Xu;Mingwei Xu;Changqiao Xu;Hongke Zhang","doi":"10.1109/JSAC.2024.3460083","DOIUrl":"10.1109/JSAC.2024.3460083","url":null,"abstract":"With the rapid growth of low earth orbit (LEO) satellites, enabling LEO AI inference becomes a fast-increasing trend. However, due to resource heterogeneity, scheduling complexity, and fast movement, how to decide the place of executing each AI inference task is nontrivial in LEO systems. In this paper, we propose LEOEdge, an edge-assisted AI inference system for LEO satellites. We first introduce the adaptive modeling technologies that automatically generate the model for each satellite according to its computation resources. We then propose a layered scheduling optimization scheme to schedule the AI inference task in a distributed manner. LEOEdge also designs a seamless data transmission scheme to avoid transmission failure due to the LEO satellite movement. We conduct a series of simulation tests to validate the performance of the proposed LEOEdge, in terms of the neural network searching efficiency, average time execution latency, and delivery latency.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"36-50"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1109/JSAC.2024.3460034
Amr S. Matar;Xuemin Shen
This paper proposes a novel integrated aerial-terrestrial multi-operator network in which each operator deploys a number of unmanned aerial vehicle-base stations (UAV-BSs) besides the terrestrial macro base station (MBS), where each BS reuses the operator’s licensed band to provide downlink connectivity for UAV-user equipment (UAV-UE). In addition, the operators allow the UAV-UE, whose demand cannot be satisfied by the licensed band, to compete with others to obtain bandwidth resources from the unlicensed spectrum. Considering inter-cell and inter-operator interference in the licensed and unlicensed spectrum, the user association, power allocation, and dynamic spectrum sharing are jointly optimized to maximize the network throughput while ensuring the UAV-UEs’ data rate requirements. The formulated optimization problem, which is an NP-hard problem, is divided into two sequential subproblems. We propose a distributed iterative algorithm composed of a matching game, coalition game, and successive convex approximation technique to jointly solve the user association and power control subproblems in the licensed spectrum. Afterwards, we propose a three-layer auction framework to allocate the unlicensed spectrum dynamically between operators. Simulation results show that the proposed algorithms with the additional use of the unlicensed spectrum achieve 86.8% higher system throughput than that of only using the licensed spectrum.
{"title":"Joint Optimization of User Association, Power Control, and Dynamic Spectrum Sharing for Integrated Aerial-Terrestrial Network","authors":"Amr S. Matar;Xuemin Shen","doi":"10.1109/JSAC.2024.3460034","DOIUrl":"10.1109/JSAC.2024.3460034","url":null,"abstract":"This paper proposes a novel integrated aerial-terrestrial multi-operator network in which each operator deploys a number of unmanned aerial vehicle-base stations (UAV-BSs) besides the terrestrial macro base station (MBS), where each BS reuses the operator’s licensed band to provide downlink connectivity for UAV-user equipment (UAV-UE). In addition, the operators allow the UAV-UE, whose demand cannot be satisfied by the licensed band, to compete with others to obtain bandwidth resources from the unlicensed spectrum. Considering inter-cell and inter-operator interference in the licensed and unlicensed spectrum, the user association, power allocation, and dynamic spectrum sharing are jointly optimized to maximize the network throughput while ensuring the UAV-UEs’ data rate requirements. The formulated optimization problem, which is an NP-hard problem, is divided into two sequential subproblems. We propose a distributed iterative algorithm composed of a matching game, coalition game, and successive convex approximation technique to jointly solve the user association and power control subproblems in the licensed spectrum. Afterwards, we propose a three-layer auction framework to allocate the unlicensed spectrum dynamically between operators. Simulation results show that the proposed algorithms with the additional use of the unlicensed spectrum achieve 86.8% higher system throughput than that of only using the licensed spectrum.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"396-409"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The emerging space-terrestrial integrated network (STIN) assumes a pivotal role within the 6G vision, promising to deliver seamless global coverage and connectivity. Achieving advanced, high-reliability, and time-sensitive (TS) services in a resource-constrained and failure-prone space environment is critical, but also presents challenges. Existing space-terrestrial communication approaches either suffer from temporary link failures with unstable reliability, or intolerable service latency due to the extensive coverage and uneven traffic distribution. This paper presents FastTS, a heuristic resilient and performant scheduling strategy to achieve fault-tolerant and time-sensitive scheduling in futuristic STINs. First, we model the high-dynamic and failure-prone topology in space, and formulate the scheduling problem as a mixed non-linear problem with the objective of minimizing the average task completion time. To approach the optimal solution, joint time-variant routing and frame replication and elimination for reliability (FRER) redundancy under resource constraints are formally considered in our design. During the path-stable duration, FastTS prioritizes the multipath selection with higher redundancy scores, all while ensuring a bounded low latency for TS services based on time-sensitive networking (TSN) techniques. Specifically, our FastTS is divided into three phases: time-sensitive multipath generation (TMG), series-parallel redundancy scoring (SPRS), and SPRS-based time-variant routing (STR). Finally, simulation results show that FastTS exhibits outstanding performance improvements in terms of packet delay, scheduling success ratio, task completion time and packet loss rate, when compared to other state-of-the-art methods.
{"title":"FastTS: Enabling Fault-Tolerant and Time-Sensitive Scheduling in Space-Terrestrial Integrated Networks","authors":"Guoyu Peng;Shuo Wang;Tao Huang;Fengtao Li;Kangzhe Zhao;Yudong Huang;Zehui Xiong","doi":"10.1109/JSAC.2024.3459008","DOIUrl":"10.1109/JSAC.2024.3459008","url":null,"abstract":"The emerging space-terrestrial integrated network (STIN) assumes a pivotal role within the 6G vision, promising to deliver seamless global coverage and connectivity. Achieving advanced, high-reliability, and time-sensitive (TS) services in a resource-constrained and failure-prone space environment is critical, but also presents challenges. Existing space-terrestrial communication approaches either suffer from temporary link failures with unstable reliability, or intolerable service latency due to the extensive coverage and uneven traffic distribution. This paper presents FastTS, a heuristic resilient and performant scheduling strategy to achieve fault-tolerant and time-sensitive scheduling in futuristic STINs. First, we model the high-dynamic and failure-prone topology in space, and formulate the scheduling problem as a mixed non-linear problem with the objective of minimizing the average task completion time. To approach the optimal solution, joint time-variant routing and frame replication and elimination for reliability (FRER) redundancy under resource constraints are formally considered in our design. During the path-stable duration, FastTS prioritizes the multipath selection with higher redundancy scores, all while ensuring a bounded low latency for TS services based on time-sensitive networking (TSN) techniques. Specifically, our FastTS is divided into three phases: time-sensitive multipath generation (TMG), series-parallel redundancy scoring (SPRS), and SPRS-based time-variant routing (STR). Finally, simulation results show that FastTS exhibits outstanding performance improvements in terms of packet delay, scheduling success ratio, task completion time and packet loss rate, when compared to other state-of-the-art methods.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3551-3565"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1109/JSAC.2024.3460067
Junjie Li;Liang Yang;Qingqing Wu;Xianfu Lei;Fuhui Zhou;Feng Shu;Xidong Mu;Yuanwei Liu;Pingzhi Fan
In this work, we investigate an active reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA)-enabled space-air-ground integrated network (SAGIN) with cognitive radio, leveraging the flexible deployment of an unmanned aerial vehicle (UAV) and the ubiquitous coverage of satellite networks. The UAV serves uplink and downlink users in the secondary network via NOMA and time division multiple access mechanisms, respectively, while satellites provide wireless backhaul for the UAV and primary users. We aim to maximize the weighted sum mean rate and energy efficiency for the secondary network by jointly the optimizing power allocation, the RIS reflection coefficients (RC), the user matching factors, and the UAV trajectory. We propose an alternating optimization framework based on the block coordinate ascent (BCA) technique, which decouples the problem into multiple variable blocks for alternating optimization until convergence. Moreover, we investigate the performance of energy-efficient active RIS with a sub-connected architecture, decoupling the RIS RC optimization into amplification factor and phase shift subproblems to be solved separately. Finally, simulation results validate the effectiveness of the proposed schemes, and demonstrate weakness of passive RIS and rationality and economics of sub-connected active RIS architecture.
{"title":"Active RIS-Aided NOMA-Enabled Space- Air-Ground Integrated Networks With Cognitive Radio","authors":"Junjie Li;Liang Yang;Qingqing Wu;Xianfu Lei;Fuhui Zhou;Feng Shu;Xidong Mu;Yuanwei Liu;Pingzhi Fan","doi":"10.1109/JSAC.2024.3460067","DOIUrl":"10.1109/JSAC.2024.3460067","url":null,"abstract":"In this work, we investigate an active reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA)-enabled space-air-ground integrated network (SAGIN) with cognitive radio, leveraging the flexible deployment of an unmanned aerial vehicle (UAV) and the ubiquitous coverage of satellite networks. The UAV serves uplink and downlink users in the secondary network via NOMA and time division multiple access mechanisms, respectively, while satellites provide wireless backhaul for the UAV and primary users. We aim to maximize the weighted sum mean rate and energy efficiency for the secondary network by jointly the optimizing power allocation, the RIS reflection coefficients (RC), the user matching factors, and the UAV trajectory. We propose an alternating optimization framework based on the block coordinate ascent (BCA) technique, which decouples the problem into multiple variable blocks for alternating optimization until convergence. Moreover, we investigate the performance of energy-efficient active RIS with a sub-connected architecture, decoupling the RIS RC optimization into amplification factor and phase shift subproblems to be solved separately. Finally, simulation results validate the effectiveness of the proposed schemes, and demonstrate weakness of passive RIS and rationality and economics of sub-connected active RIS architecture.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"314-333"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1109/JSAC.2024.3460060
Yu Liu;Ming Chen;Cunhua Pan;Tantao Gong;Jinhong Yuan;Jiangzhou Wang
Orthogonal time frequency space (OTFS) modulation, a delay-Doppler (DD) domain communication scheme exhibiting strong robustness against the Doppler shifts, has the potentials to be employed in LEO satellite communications. However, the performance comparison with the orthogonal frequency division multiplexing (OFDM) modulation and the resource allocation scheme for multiuser OTFS-based LEO satellite communication system have rarely been investigated. In this paper, we conduct a performance comparison under various channel conditions between the OTFS and OFDM modulations, encompassing evaluations of sum-rate and bit error ratio (BER). Additionally, we investigate the joint optimal allocation of power and delay-Doppler resource blocks aiming at maximizing sum-rate for multiuser downlink OTFS-based LEO satellite communication systems. Unlike the conventional modulations relying on complex input-output relations within the Time-Frequency (TF) domain, the OTFS modulation exploits both time and frequency diversities, i.e., delay and Doppler shifts remain constant during a OTFS frame, which facilitates a DD domain input-output simple relation for our investigation. We transform the resulting non-convex and combinatorial optimization problem into an equivalent difference of convex problem by decoupling the conditional constraints, and solve the transformed problem via penalty convex-concave procedure algorithm. Simulation results demonstrate that the OTFS modulation is robust to carrier frequency offsets (CFO) caused by high-mobility of LEO satellites, and has superior performance to the OFDM modulation. Moreover, numerical results indicate that our proposed resource allocation scheme has higher sum-rate than existing schemes for the OTFS modulation, such as delay divided multiple access and Doppler divided multiple access, especially in the high signal-to-noise ratio (SNR) regime.
{"title":"OTFS Versus OFDM: Which is Superior in Multiuser LEO Satellite Communications","authors":"Yu Liu;Ming Chen;Cunhua Pan;Tantao Gong;Jinhong Yuan;Jiangzhou Wang","doi":"10.1109/JSAC.2024.3460060","DOIUrl":"10.1109/JSAC.2024.3460060","url":null,"abstract":"Orthogonal time frequency space (OTFS) modulation, a delay-Doppler (DD) domain communication scheme exhibiting strong robustness against the Doppler shifts, has the potentials to be employed in LEO satellite communications. However, the performance comparison with the orthogonal frequency division multiplexing (OFDM) modulation and the resource allocation scheme for multiuser OTFS-based LEO satellite communication system have rarely been investigated. In this paper, we conduct a performance comparison under various channel conditions between the OTFS and OFDM modulations, encompassing evaluations of sum-rate and bit error ratio (BER). Additionally, we investigate the joint optimal allocation of power and delay-Doppler resource blocks aiming at maximizing sum-rate for multiuser downlink OTFS-based LEO satellite communication systems. Unlike the conventional modulations relying on complex input-output relations within the Time-Frequency (TF) domain, the OTFS modulation exploits both time and frequency diversities, i.e., delay and Doppler shifts remain constant during a OTFS frame, which facilitates a DD domain input-output simple relation for our investigation. We transform the resulting non-convex and combinatorial optimization problem into an equivalent difference of convex problem by decoupling the conditional constraints, and solve the transformed problem via penalty convex-concave procedure algorithm. Simulation results demonstrate that the OTFS modulation is robust to carrier frequency offsets (CFO) caused by high-mobility of LEO satellites, and has superior performance to the OFDM modulation. Moreover, numerical results indicate that our proposed resource allocation scheme has higher sum-rate than existing schemes for the OTFS modulation, such as delay divided multiple access and Doppler divided multiple access, especially in the high signal-to-noise ratio (SNR) regime.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"139-155"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1109/JSAC.2024.3460066
Mario Minardi;Youssouf Drif;Thang X. Vu;Symeon Chatzinotas
Network slicing (NS) is one of the key techniques to manage logical and functionally separated networks on a common infrastructure, in a dynamic manner. As the complexity of virtualizing a full infrastructure required unprecedented effort, the initial idea of combining satellite and terrestrial networks has not been fully implemented in 5G yet. 6G networks are expected to further bring NS to a substrate network that is more heterogeneous, due to the full integration between terrestrial and satellite networks. NS describes the process of accommodating virtual networks, typically composed of nodes and links with the respective requirements, into the main infrastructure. This is an NP-Hard problem, typically also known as Virtual Network Embedding (VNE). Existing VNE solutions are designed per use-case and lack flexibility, adaptation and traffic-awareness, especially in such dynamic satellite environment. In this work, we investigate the VNE implementation to integrated satellite-terrestrial networks and propose a novel flexible framework, named Slice-Aware VNE for Satellite-Terrestrial (SAST-VNE), which 1) operates based on traffic prioritization; 2) jointly optimizes the load-balancing and the migration cost when network congestion occurs; and 3) provides a near-optimal solution. We compare SAST-VNE to existing well-known near-optimal VNE algorithms such as VINEYard and CEVNE and the shortest-path SN-VNE solution for satellite networks. The simulations showed that SAST-VNE reduces the migration costs between 10% and 40% during satellite handovers while maintaining the network load under control. Furthermore, when congestion occurs, SAST-VNE proved to be flexible in matching the priority of the slice, i.e., tolerated latency, with the time complexity and optimality of the solution.
{"title":"SAST-VNE: A Flexible Framework for Network Slicing in 6G Integrated Satellite-Terrestrial Networks","authors":"Mario Minardi;Youssouf Drif;Thang X. Vu;Symeon Chatzinotas","doi":"10.1109/JSAC.2024.3460066","DOIUrl":"10.1109/JSAC.2024.3460066","url":null,"abstract":"Network slicing (NS) is one of the key techniques to manage logical and functionally separated networks on a common infrastructure, in a dynamic manner. As the complexity of virtualizing a full infrastructure required unprecedented effort, the initial idea of combining satellite and terrestrial networks has not been fully implemented in 5G yet. 6G networks are expected to further bring NS to a substrate network that is more heterogeneous, due to the full integration between terrestrial and satellite networks. NS describes the process of accommodating virtual networks, typically composed of nodes and links with the respective requirements, into the main infrastructure. This is an NP-Hard problem, typically also known as Virtual Network Embedding (VNE). Existing VNE solutions are designed per use-case and lack flexibility, adaptation and traffic-awareness, especially in such dynamic satellite environment. In this work, we investigate the VNE implementation to integrated satellite-terrestrial networks and propose a novel flexible framework, named Slice-Aware VNE for Satellite-Terrestrial (SAST-VNE), which 1) operates based on traffic prioritization; 2) jointly optimizes the load-balancing and the migration cost when network congestion occurs; and 3) provides a near-optimal solution. We compare SAST-VNE to existing well-known near-optimal VNE algorithms such as VINEYard and CEVNE and the shortest-path SN-VNE solution for satellite networks. The simulations showed that SAST-VNE reduces the migration costs between 10% and 40% during satellite handovers while maintaining the network load under control. Furthermore, when congestion occurs, SAST-VNE proved to be flexible in matching the priority of the slice, i.e., tolerated latency, with the time complexity and optimality of the solution.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"234-244"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10685080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275438","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 : 2024-09-18DOI: 10.1109/JSAC.2024.3447313
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/JSAC.2024.3447313","DOIUrl":"10.1109/JSAC.2024.3447313","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10683990","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245899","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 : 2024-09-18DOI: 10.1109/JSAC.2024.3447371
{"title":"IEEE Open Access Publishing","authors":"","doi":"10.1109/JSAC.2024.3447371","DOIUrl":"10.1109/JSAC.2024.3447371","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2986-2986"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10683991","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245746","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 : 2024-09-18DOI: 10.1109/JSAC.2024.3447369
{"title":"TechRxiv: Share Your Preprint Research With the World!","authors":"","doi":"10.1109/JSAC.2024.3447369","DOIUrl":"10.1109/JSAC.2024.3447369","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2985-2985"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10683992","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245748","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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