Pub Date : 2024-09-18DOI: 10.1109/JSAC.2024.3423628
Yang Yang;Mingzhe Chen;Yufei Blankenship;Jemin Lee;Zabih Ghassemlooy;Julian Cheng;Shiwen Mao
This is Part II of the double-part Special Issue (SI) on Positioning and Sensing Over Wireless Networks. The two-part SI aims to bring cutting-edge and novel contributions on positioning and sensing over wireless networks for future and emerging applications. The accepted 51 papers are arranged into eight groups: 1) fundamental performance analysis and optimization; 2) positioning and sensing with cellular networks; 3) positioning and sensing with WiFi networks; 4) positioning and sensing with emerging communication technologies; 5) positioning and sensing applications; 6) cooperative positioning and sensing; 7) reconfigurable intelligent surfaces (RIS)-assisted positioning and sensing; and 8) privacy and security. The contributions made by the papers in Part II are summarized as follows, which correspond to the last four paper groups.
{"title":"Guest Editorial Positioning and Sensing Over Wireless Networks—Part II","authors":"Yang Yang;Mingzhe Chen;Yufei Blankenship;Jemin Lee;Zabih Ghassemlooy;Julian Cheng;Shiwen Mao","doi":"10.1109/JSAC.2024.3423628","DOIUrl":"10.1109/JSAC.2024.3423628","url":null,"abstract":"This is Part II of the double-part Special Issue (SI) on Positioning and Sensing Over Wireless Networks. The two-part SI aims to bring cutting-edge and novel contributions on positioning and sensing over wireless networks for future and emerging applications. The accepted 51 papers are arranged into eight groups: 1) fundamental performance analysis and optimization; 2) positioning and sensing with cellular networks; 3) positioning and sensing with WiFi networks; 4) positioning and sensing with emerging communication technologies; 5) positioning and sensing applications; 6) cooperative positioning and sensing; 7) reconfigurable intelligent surfaces (RIS)-assisted positioning and sensing; and 8) privacy and security. The contributions made by the papers in Part II are summarized as follows, which correspond to the last four paper groups.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2603-2607"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10683891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245747","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.2024.3447311
{"title":"IEEE Journal on Selected Areas in Communications Publication Information","authors":"","doi":"10.1109/JSAC.2024.3447311","DOIUrl":"10.1109/JSAC.2024.3447311","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":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10683988","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245749","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 : 2024-09-17DOI: 10.1109/JSAC.2024.3459092
Haithm M. Al-Gunid;Wang Xingfu;Ammar Hawbani;Yang Mingchuan;Mohammed A. M. Sultan;Hui Tian;Liqiang Zhao;Liang Zhao
With the rapid expansion of Internet of Everything (IoE) devices and the increasing demand for high-speed data and reliable communication services, particularly within 6G cellular networks (CNs), the design of efficient and robust CNs has become a critical research area. Consequently, enabling massive connections, optimizing network resource utilization, and achieving cost-effective network operation pose significant challenges. To this end, integrated space-ground cellular networks based on control- and user-plane separation (ISGCN-CUPS) architecture has been proposed as a promising solution. Furthermore, it becomes an integral aspect of the broader paradigm of integrated space-air-ground CNs (ISAGCNs). However, scalability poses an issue when increasing the number of connected cellular users, especially when conventional orthogonal multiple access (OMA) is utilized. To address this challenge, this paper introduces the non-orthogonal multiple access (NOMA)-enabled ISGCN-CUPS architecture. Subsequently, we provide an analytical model to analyze the scenarios of proposed architecture. Utilizing stochastic geometry, we derive closed-forms for coverage probabilities over control and data channels, by considering the propagation channel models for control and data channels, both with and without interference. Furthermore, total area spectral and energy efficiencies are computed. The proposed architecture demonstrates significant enhancements in terms of the key evaluation metrics compared to conventional and OMA-enabled ISGCN-CUPS architectures.
{"title":"NOMA-Enabled Integrated Space-Ground Cellular Networks Architecture Relying on Control- and User-Plane Separation","authors":"Haithm M. Al-Gunid;Wang Xingfu;Ammar Hawbani;Yang Mingchuan;Mohammed A. M. Sultan;Hui Tian;Liqiang Zhao;Liang Zhao","doi":"10.1109/JSAC.2024.3459092","DOIUrl":"10.1109/JSAC.2024.3459092","url":null,"abstract":"With the rapid expansion of Internet of Everything (IoE) devices and the increasing demand for high-speed data and reliable communication services, particularly within 6G cellular networks (CNs), the design of efficient and robust CNs has become a critical research area. Consequently, enabling massive connections, optimizing network resource utilization, and achieving cost-effective network operation pose significant challenges. To this end, integrated space-ground cellular networks based on control- and user-plane separation (ISGCN-CUPS) architecture has been proposed as a promising solution. Furthermore, it becomes an integral aspect of the broader paradigm of integrated space-air-ground CNs (ISAGCNs). However, scalability poses an issue when increasing the number of connected cellular users, especially when conventional orthogonal multiple access (OMA) is utilized. To address this challenge, this paper introduces the non-orthogonal multiple access (NOMA)-enabled ISGCN-CUPS architecture. Subsequently, we provide an analytical model to analyze the scenarios of proposed architecture. Utilizing stochastic geometry, we derive closed-forms for coverage probabilities over control and data channels, by considering the propagation channel models for control and data channels, both with and without interference. Furthermore, total area spectral and energy efficiencies are computed. The proposed architecture demonstrates significant enhancements in terms of the key evaluation metrics compared to conventional and OMA-enabled ISGCN-CUPS architectures.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3690-3704"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142236512","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}
In the rapidly evolving domain of satellite communications, integrating advanced machine learning techniques, particularly split learning, is crucial for enhancing data processing and model training efficiency across satellites, space stations, and ground stations. Traditional ML approaches often face significant challenges within satellite networks due to constraints such as limited bandwidth and computational resources. To address this gap, we propose a novel framework for more efficient SL in satellite communications. Our approach, Dynamic Topology-Informed Pruning, namely DTIP, combines differential privacy with graph and model pruning to optimize graph neural networks for distributed learning. DTIP strategically applies differential privacy to raw graph data and prunes GNNs, thereby optimizing both model size and communication load across network tiers. Extensive experiments across diverse datasets demonstrate DTIP’s efficacy in enhancing privacy, accuracy, and computational efficiency. Specifically, on Amazon2M dataset, DTIP maintains an accuracy of 0.82 while achieving a 50% reduction in floating-point operations per second. Similarly, on ArXiv dataset, DTIP achieves an accuracy of 0.85 under comparable conditions. Our framework not only significantly improves the operational efficiency of satellite communications but also establishes a new benchmark in privacy-aware distributed learning, potentially revolutionizing data handling in space-based networks.
{"title":"An Efficient Privacy-Aware Split Learning Framework for Satellite Communications","authors":"Jianfei Sun;Cong Wu;Shahid Mumtaz;Junyi Tao;Mingsheng Cao;Mei Wang;Valerio Frascolla","doi":"10.1109/JSAC.2024.3459027","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3459027","url":null,"abstract":"In the rapidly evolving domain of satellite communications, integrating advanced machine learning techniques, particularly split learning, is crucial for enhancing data processing and model training efficiency across satellites, space stations, and ground stations. Traditional ML approaches often face significant challenges within satellite networks due to constraints such as limited bandwidth and computational resources. To address this gap, we propose a novel framework for more efficient SL in satellite communications. Our approach, Dynamic Topology-Informed Pruning, namely DTIP, combines differential privacy with graph and model pruning to optimize graph neural networks for distributed learning. DTIP strategically applies differential privacy to raw graph data and prunes GNNs, thereby optimizing both model size and communication load across network tiers. Extensive experiments across diverse datasets demonstrate DTIP’s efficacy in enhancing privacy, accuracy, and computational efficiency. Specifically, on Amazon2M dataset, DTIP maintains an accuracy of 0.82 while achieving a 50% reduction in floating-point operations per second. Similarly, on ArXiv dataset, DTIP achieves an accuracy of 0.85 under comparable conditions. Our framework not only significantly improves the operational efficiency of satellite communications but also establishes a new benchmark in privacy-aware distributed learning, potentially revolutionizing data handling in space-based networks.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3355-3365"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142754265","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 space-terrestrial integrated network (STIN) is a pivotal architecture to support ubiquitous connectivity in the upcoming 6G era. Inter-operator resource and service sharing is a promising way to realize such a huge network, utilizing resources efficiently and reducing construction costs. Given the rationality of operators, the configuration of resources and services in STIN should focus on both the overall system performance and individual benefits of operators. Motivated by emerging symbiotic communication facilitating mutual benefits across different radio systems, we investigate the resource and service sharing in STIN from a symbiotic communication perspective in this paper. In particular, we consider a STIN consisting of a ground network operator (GNO) and a satellite network operator (SNO). Specifically, we aim to maximize the weighted sum rate (WSR) of the whole STIN by jointly optimizing the user association, resource allocation, and beamforming. Besides, we introduce a sharing coefficient to characterize the revenue of operators. Operators may suffer revenue loss when only focusing on maximizing the WSR. In pursuit of mutual benefits, we propose a mutual benefit constraint (MBC) to ensure that each operator obtains revenue gains. Then, we develop a centralized algorithm based on the successive convex approximation (SCA) method. Considering that the centralized algorithm is difficult to implement, we propose a distributed algorithm based on Lagrangian dual decomposition and the consensus alternating direction method of multipliers (ADMM). Finally, we provide extensive numerical simulations to demonstrate the effectiveness of the two proposed algorithms, and the distributed optimization algorithm can approach the performance of the centralized one. The results also reveal that the proposed MBCs can enable operators to achieve mutual benefits and realize a symbiotic resource and service sharing paradigm.
{"title":"Toward Symbiotic STIN Through Inter-Operator Resource and Service Sharing: Joint Orchestration of User Association and Radio Resources","authors":"Shizhao He;Jungang Ge;Ying-Chang Liang;Dusit Niyato","doi":"10.1109/JSAC.2024.3459042","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3459042","url":null,"abstract":"The space-terrestrial integrated network (STIN) is a pivotal architecture to support ubiquitous connectivity in the upcoming 6G era. Inter-operator resource and service sharing is a promising way to realize such a huge network, utilizing resources efficiently and reducing construction costs. Given the rationality of operators, the configuration of resources and services in STIN should focus on both the overall system performance and individual benefits of operators. Motivated by emerging symbiotic communication facilitating mutual benefits across different radio systems, we investigate the resource and service sharing in STIN from a symbiotic communication perspective in this paper. In particular, we consider a STIN consisting of a ground network operator (GNO) and a satellite network operator (SNO). Specifically, we aim to maximize the weighted sum rate (WSR) of the whole STIN by jointly optimizing the user association, resource allocation, and beamforming. Besides, we introduce a sharing coefficient to characterize the revenue of operators. Operators may suffer revenue loss when only focusing on maximizing the WSR. In pursuit of mutual benefits, we propose a mutual benefit constraint (MBC) to ensure that each operator obtains revenue gains. Then, we develop a centralized algorithm based on the successive convex approximation (SCA) method. Considering that the centralized algorithm is difficult to implement, we propose a distributed algorithm based on Lagrangian dual decomposition and the consensus alternating direction method of multipliers (ADMM). Finally, we provide extensive numerical simulations to demonstrate the effectiveness of the two proposed algorithms, and the distributed optimization algorithm can approach the performance of the centralized one. The results also reveal that the proposed MBCs can enable operators to achieve mutual benefits and realize a symbiotic resource and service sharing paradigm.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3674-3689"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713910","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 : 2024-09-16DOI: 10.1109/JSAC.2024.3459034
Jonathan Chamberlain;David Starobinski;Joel T. Johnson
Space-Air-Ground Integrated Networks will facilitate seamless user experiences across a variety of 6G applications. The deployment of these networks will necessitate new approaches to spectrum allocation. Spectrum access by passive microwave sensors for earth-based and space-based scientific applications represents a spectrum use application having unique attributes that motivate consideration of spectrum sharing between these “incumbents” and commercial users to ensure the most efficient utilization of available frequencies across applications. Toward this end, we propose an economic framework where incumbents have priority use, with a primary and secondary commercial tier underneath. For commercial users, the option to join the primary tier is based on a model of short term post-paid leases of spectrum, while the secondary tier is available to join at no cost. Using a joint game-theoretic and queuing-theoretic model, we find that for practical parameters the revenue maximizing equilibrium is: 1) stable in the Evolutionary Stable Strategy sense; 2) associated with the maximum priority upgrade fee customers are willing to pay; 3) associated with an equilibrium where all customers wish to join the priority class; and 4) socially optimal. We validate our findings leveraging trace data from satellite radiometers operating in the vicinity of Boston, Massachusetts.
{"title":"Facilitating Spectrum Sharing With Passive Satellite Incumbents","authors":"Jonathan Chamberlain;David Starobinski;Joel T. Johnson","doi":"10.1109/JSAC.2024.3459034","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3459034","url":null,"abstract":"Space-Air-Ground Integrated Networks will facilitate seamless user experiences across a variety of 6G applications. The deployment of these networks will necessitate new approaches to spectrum allocation. Spectrum access by passive microwave sensors for earth-based and space-based scientific applications represents a spectrum use application having unique attributes that motivate consideration of spectrum sharing between these “incumbents” and commercial users to ensure the most efficient utilization of available frequencies across applications. Toward this end, we propose an economic framework where incumbents have priority use, with a primary and secondary commercial tier underneath. For commercial users, the option to join the primary tier is based on a model of short term post-paid leases of spectrum, while the secondary tier is available to join at no cost. Using a joint game-theoretic and queuing-theoretic model, we find that for practical parameters the revenue maximizing equilibrium is: 1) stable in the Evolutionary Stable Strategy sense; 2) associated with the maximum priority upgrade fee customers are willing to pay; 3) associated with an equilibrium where all customers wish to join the priority class; and 4) socially optimal. We validate our findings leveraging trace data from satellite radiometers operating in the vicinity of Boston, Massachusetts.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3719-3733"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713940","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 : 2024-09-16DOI: 10.1109/JSAC.2024.3460086
Yue Cai;Peng Cheng;Zhuo Chen;Wei Xiang;Branka Vucetic;Yonghui Li
Space-Air-Ground integrated network (SAGIN) is a crucial component of the 6G, enabling global and seamless communication coverage. This multi-layered communication system integrates space, air, and terrestrial segments, each with computational capability, and also serves as a ubiquitous computing platform. An efficient task offloading and resource allocation scheme is key in SAGIN to maximize resource utilization efficiency, meeting the stringent quality of service (QoS) requirements for different service types. In this paper, we introduce a dynamic SAGIN model featuring diverse antenna configurations, two timescale types, different channel models for each segment, and dual service types. We formulate a problem of sequential decision-making task offloading and resource allocation. Our proposed solution is an innovative online approach referred to as graphic deep reinforcement learning (GDRL). This approach utilizes a graph neural network (GNN)-based feature extraction network to identify the inherent dependencies within the graphical structure of the states. We design an action mapping network with an encoding scheme for end-to-end generation of task offloading and resource allocation decisions. Additionally, we incorporate meta-learning into GDRL to swiftly adapt to rapid changes in key parameters of the SAGIN environment, significantly reducing online deployment complexity. Simulation results validate that our proposed GDRL significantly outperforms state-of-the-art DRL approaches by achieving the highest reward and lowest overall latency.
{"title":"Graphic Deep Reinforcement Learning for Dynamic Resource Allocation in Space-Air-Ground Integrated Networks","authors":"Yue Cai;Peng Cheng;Zhuo Chen;Wei Xiang;Branka Vucetic;Yonghui Li","doi":"10.1109/JSAC.2024.3460086","DOIUrl":"10.1109/JSAC.2024.3460086","url":null,"abstract":"Space-Air-Ground integrated network (SAGIN) is a crucial component of the 6G, enabling global and seamless communication coverage. This multi-layered communication system integrates space, air, and terrestrial segments, each with computational capability, and also serves as a ubiquitous computing platform. An efficient task offloading and resource allocation scheme is key in SAGIN to maximize resource utilization efficiency, meeting the stringent quality of service (QoS) requirements for different service types. In this paper, we introduce a dynamic SAGIN model featuring diverse antenna configurations, two timescale types, different channel models for each segment, and dual service types. We formulate a problem of sequential decision-making task offloading and resource allocation. Our proposed solution is an innovative online approach referred to as graphic deep reinforcement learning (GDRL). This approach utilizes a graph neural network (GNN)-based feature extraction network to identify the inherent dependencies within the graphical structure of the states. We design an action mapping network with an encoding scheme for end-to-end generation of task offloading and resource allocation decisions. Additionally, we incorporate meta-learning into GDRL to swiftly adapt to rapid changes in key parameters of the SAGIN environment, significantly reducing online deployment complexity. Simulation results validate that our proposed GDRL significantly outperforms state-of-the-art DRL approaches by achieving the highest reward and lowest overall latency.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"334-349"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142236521","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 future sixth-generation (6G) of wireless networks is expected to surpass its predecessors by offering ubiquitous coverage through integrated air-ground deployments in both communication and computing domains. In such networks, aerial platforms, such as unmanned aerial vehicles (UAVs), conduct artificial intelligence (AI) computations based on multi-modal data to support diverse applications including surveillance and environment construction. However, these multi-domain inference and content generation tasks require large AI models, demanding powerful computing capabilities and finely tuned inference models trained on rich datasets, thus posing significant challenges for UAVs. To tackle this problem, we propose an integrated air-ground edge-cloud model framework, in which UAVs serve as edge nodes for data collection and small model computation. Through wireless channels, UAVs collaborate with ground cloud servers providing large model computation and model updating for edge UAVs. With limited wireless communication bandwidth, the proposed framework faces the challenge of information exchange scheduling between the edge UAVs and the cloud server. To tackle this, we present joint task allocation, transmission resource allocation, transmission data quantization design, and edge model update design to enhance the inference accuracy of the integrated air-ground edge-cloud model evolution framework by mean average precision (mAP) maximization. A closed-form lower bound on the mAP of the proposed framework is derived based on the mAP of the edge model and mAP of the cloud model, and the solution to the mAP maximization problem is optimized accordingly. Simulations, based on results from vision-based classification experiments, consistently demonstrate that the mAP of the proposed integrated air-ground edge-cloud model evolution framework outperforms both a centralized cloud model framework and a distributed edge model framework across various communication bandwidths and data sizes.
{"title":"Large Models for Aerial Edges: An Edge-Cloud Model Evolution and Communication Paradigm","authors":"Shuhang Zhang;Qingyu Liu;Ke Chen;Boya Di;Hongliang Zhang;Wenhan Yang;Dusit Niyato;Zhu Han;H. Vincent Poor","doi":"10.1109/JSAC.2024.3460078","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3460078","url":null,"abstract":"The future sixth-generation (6G) of wireless networks is expected to surpass its predecessors by offering ubiquitous coverage through integrated air-ground deployments in both communication and computing domains. In such networks, aerial platforms, such as unmanned aerial vehicles (UAVs), conduct artificial intelligence (AI) computations based on multi-modal data to support diverse applications including surveillance and environment construction. However, these multi-domain inference and content generation tasks require large AI models, demanding powerful computing capabilities and finely tuned inference models trained on rich datasets, thus posing significant challenges for UAVs. To tackle this problem, we propose an integrated air-ground edge-cloud model framework, in which UAVs serve as edge nodes for data collection and small model computation. Through wireless channels, UAVs collaborate with ground cloud servers providing large model computation and model updating for edge UAVs. With limited wireless communication bandwidth, the proposed framework faces the challenge of information exchange scheduling between the edge UAVs and the cloud server. To tackle this, we present joint task allocation, transmission resource allocation, transmission data quantization design, and edge model update design to enhance the inference accuracy of the integrated air-ground edge-cloud model evolution framework by mean average precision (mAP) maximization. A closed-form lower bound on the mAP of the proposed framework is derived based on the mAP of the edge model and mAP of the cloud model, and the solution to the mAP maximization problem is optimized accordingly. Simulations, based on results from vision-based classification experiments, consistently demonstrate that the mAP of the proposed integrated air-ground edge-cloud model evolution framework outperforms both a centralized cloud model framework and a distributed edge model framework across various communication bandwidths and data sizes.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"21-35"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859288","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 : 2024-09-16DOI: 10.1109/JSAC.2024.3460059
Chen Zhang;Yusha Liu;Jie Hu;Kun Yang
Satellite Internet of things (S-IoT) aims to provide globally covered network services. In this paper, we conceive an uplink grant-free random access scheme for S-IoT network, where ground devices transmit data packets to the low Earth orbit (LEO) satellite, reducing signaling cost and making efficient use of spectrum resources by employing the non-orthogonal multiple access scheme. The impact of high operational speed of the LEO satellite is also taken into account. We further propose an iterative Gaussian approximated message passing-aided sparse Bayesian learning (GAMP-SBL) algorithm to address the joint channel estimation (CE), active user identification (UID) and data detection (DD) problem, where the three steps interacts with each other during the iterative process. Simulation results have demonstrated that our proposed joint receiver design outperforms the existing AMP-based schemes in terms of bit error rate (BER), convergence speed, as well as false alarm rate (FAR).
{"title":"Joint User Identification, Channel Estimation, and Data Detection for Grant-Free NOMA in LEO Satellite Communications","authors":"Chen Zhang;Yusha Liu;Jie Hu;Kun Yang","doi":"10.1109/JSAC.2024.3460059","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3460059","url":null,"abstract":"Satellite Internet of things (S-IoT) aims to provide globally covered network services. In this paper, we conceive an uplink grant-free random access scheme for S-IoT network, where ground devices transmit data packets to the low Earth orbit (LEO) satellite, reducing signaling cost and making efficient use of spectrum resources by employing the non-orthogonal multiple access scheme. The impact of high operational speed of the LEO satellite is also taken into account. We further propose an iterative Gaussian approximated message passing-aided sparse Bayesian learning (GAMP-SBL) algorithm to address the joint channel estimation (CE), active user identification (UID) and data detection (DD) problem, where the three steps interacts with each other during the iterative process. Simulation results have demonstrated that our proposed joint receiver design outperforms the existing AMP-based schemes in terms of bit error rate (BER), convergence speed, as well as false alarm rate (FAR).","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"107-121"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859289","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 : 2024-09-13DOI: 10.1109/JSAC.2024.3460084
Minghui Wu;Zhen Gao;Zhaocheng Wang;Dusit Niyato;George K. Karagiannidis;Sheng Chen
Near-space airship-borne communication network is recognized to be an indispensable component of the future integrated ground-air-space network thanks to airships’ advantage of long-term residency at stratospheric altitudes, but it urgently needs reliable and efficient Airship-to-X link. To improve the transmission efficiency and capacity, this paper proposes to integrate semantic communication with massive multiple-input multiple-output (MIMO) technology. Specifically, we propose a deep joint semantic coding and beamforming (JSCBF) scheme for airship-based massive MIMO image transmission network in space, in which semantics from both source and channel are fused to jointly design the semantic coding and physical layer beamforming. First, we design two semantic extraction networks to extract semantics from image source and channel state information, respectively. Then, we propose a semantic fusion network that can fuse these semantics into complex-valued semantic features for subsequent physical-layer transmission. To efficiently transmit the fused semantic features at the physical layer, we then propose the hybrid data and model-driven semantic-aware beamforming networks. At the receiver, a semantic decoding network is designed to reconstruct the transmitted images. Finally, we perform end-to-end deep learning to jointly train all the modules, using the image reconstruction quality at the receivers as a metric. The proposed deep JSCBF scheme fully combines the efficient source compressibility and robust error correction capability of semantic communication with the high spectral efficiency of massive MIMO, achieving a significant performance improvement over existing approaches.
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