This paper investigates joint device identification, channel estimation, and symbol detection for cooperative multi-satellite-enhanced random access, where orthogonal time-frequency space modulation with the large antenna array is utilized to combat the dynamics of the terrestrial-satellite links (TSLs). We introduce the generalized complex exponential basis expansion model to parameterize TSLs, thereby reducing the pilot overhead. By exploiting the block sparsity of the TSLs in the angular domain, a message passing algorithm is designed for initial channel estimation. Subsequently, we examine two cooperative modes to leverage the spatial diversity within satellite constellations: the centralized mode, where computations are performed at a high-power central server, and the distributed mode, where computations are offloaded to edge satellites with minimal signaling overhead. Specifically, in the centralized mode, device identification is achieved by aggregating backhaul information from edge satellites, and channel estimation and symbol detection are jointly enhanced through a structured approximate expectation propagation (AEP) algorithm. In the distributed mode, edge satellites share channel information and exchange soft information about data symbols, leading to a distributed version of AEP. The introduced basis expansion model for TSLs enables the efficient implementation of both centralized and distributed algorithms via fast Fourier transform. Simulation results demonstrate that proposed schemes significantly outperform conventional algorithms in terms of the activity error rate, the normalized mean squared error, and the symbol error rate. Notably, the distributed mode achieves performance comparable to the centralized mode with only two exchanges of soft information about data symbols within the constellation.
{"title":"Massive MIMO-OTFS-Based Random Access for Cooperative LEO Satellite Constellations","authors":"Boxiao Shen;Yongpeng Wu;Shiqi Gong;Heng Liu;Björn Ottersten;Wenjun Zhang","doi":"10.1109/JSAC.2024.3460064","DOIUrl":"10.1109/JSAC.2024.3460064","url":null,"abstract":"This paper investigates joint device identification, channel estimation, and symbol detection for cooperative multi-satellite-enhanced random access, where orthogonal time-frequency space modulation with the large antenna array is utilized to combat the dynamics of the terrestrial-satellite links (TSLs). We introduce the generalized complex exponential basis expansion model to parameterize TSLs, thereby reducing the pilot overhead. By exploiting the block sparsity of the TSLs in the angular domain, a message passing algorithm is designed for initial channel estimation. Subsequently, we examine two cooperative modes to leverage the spatial diversity within satellite constellations: the centralized mode, where computations are performed at a high-power central server, and the distributed mode, where computations are offloaded to edge satellites with minimal signaling overhead. Specifically, in the centralized mode, device identification is achieved by aggregating backhaul information from edge satellites, and channel estimation and symbol detection are jointly enhanced through a structured approximate expectation propagation (AEP) algorithm. In the distributed mode, edge satellites share channel information and exchange soft information about data symbols, leading to a distributed version of AEP. The introduced basis expansion model for TSLs enables the efficient implementation of both centralized and distributed algorithms via fast Fourier transform. Simulation results demonstrate that proposed schemes significantly outperform conventional algorithms in terms of the activity error rate, the normalized mean squared error, and the symbol error rate. Notably, the distributed mode achieves performance comparable to the centralized mode with only two exchanges of soft information about data symbols within the constellation.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"90-106"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231233","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.2024.3460053
Chengleyang Lei;Wei Feng;Peng Wei;Yunfei Chen;Ning Ge;Shiwen Mao
An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial base stations or mobile edge computing (MEC), the EIH would direct the operations of robots via sensing-communication-computing-control ( $textbf {SC}^{3}$ ) closed-loop orchestration. This paper aims to optimize the closed-loop control performance of multiple $textbf {SC}^{3}$ loops, with constraints on satellite-backhaul rate, computing capability, and on-board energy. Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources. We first derive the optimal splitting ratio of sensor data, and then recast the problem to a more tractable form. An iterative algorithm is finally proposed to provide a sub-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm. We also uncover the influence of $textbf {SC}^{3}$ parameters on closed-loop controls, highlighting more systematic understanding.
{"title":"Edge Information Hub: Orchestrating Satellites, UAVs, MEC, Sensing and Communications for 6G Closed-Loop Controls","authors":"Chengleyang Lei;Wei Feng;Peng Wei;Yunfei Chen;Ning Ge;Shiwen Mao","doi":"10.1109/JSAC.2024.3460053","DOIUrl":"10.1109/JSAC.2024.3460053","url":null,"abstract":"An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial base stations or mobile edge computing (MEC), the EIH would direct the operations of robots via sensing-communication-computing-control (\u0000<inline-formula> <tex-math>$textbf {SC}^{3}$ </tex-math></inline-formula>\u0000) closed-loop orchestration. This paper aims to optimize the closed-loop control performance of multiple \u0000<inline-formula> <tex-math>$textbf {SC}^{3}$ </tex-math></inline-formula>\u0000 loops, with constraints on satellite-backhaul rate, computing capability, and on-board energy. Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources. We first derive the optimal splitting ratio of sensor data, and then recast the problem to a more tractable form. An iterative algorithm is finally proposed to provide a sub-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm. We also uncover the influence of \u0000<inline-formula> <tex-math>$textbf {SC}^{3}$ </tex-math></inline-formula>\u0000 parameters on closed-loop controls, highlighting more systematic understanding.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"5-20"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10679984","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231235","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 : 2024-09-13DOI: 10.1109/JSAC.2024.3460079
Qunshu Wang;Shaoyong Guo;Celimuge Wu;Chengwen Xing;Nan Zhao;Dusit Niyato;George K. Karagiannidis
The combination of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and an unmanned aerial vehicle (UAV) can further improve channel quality and extend coverage. However, the high-quality air-to-ground link is more vulnerable to eavesdropping by adversaries. In this paper, we investigate STAR-RIS-assisted covert communication in UAV non-orthogonal multiple access (NOMA) networks with a warden Willie, where Alice intends to transmit the covert signal to a near user Bob under the cover of a far user Carol via STAR-RIS. We aim to maximize the covert transmission rate by jointly optimizing the active and passive beamforming as well as the UAV location. The error detection probability and optimal detection threshold for Willie are first derived to obtain an analytic solution for the minimum detection error probability. Then, an alternating optimization algorithm is proposed to maximize the covert transmission rate under the condition of guaranteeing the communication of Carol and satisfying the covertness constraint of Bob. Specifically, the nonconvex problem is decomposed into three sub-problems by block coordinate descent, which are then solved using semidefinite relaxation and successive convex approximation. Finally, simulation results are presented to demonstrate the effectiveness of the proposed covert communication scheme for STAR-RIS assisted UAV air-ground networks.
{"title":"STAR-RIS Aided Covert Communication in UAV Air-Ground Networks","authors":"Qunshu Wang;Shaoyong Guo;Celimuge Wu;Chengwen Xing;Nan Zhao;Dusit Niyato;George K. Karagiannidis","doi":"10.1109/JSAC.2024.3460079","DOIUrl":"10.1109/JSAC.2024.3460079","url":null,"abstract":"The combination of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and an unmanned aerial vehicle (UAV) can further improve channel quality and extend coverage. However, the high-quality air-to-ground link is more vulnerable to eavesdropping by adversaries. In this paper, we investigate STAR-RIS-assisted covert communication in UAV non-orthogonal multiple access (NOMA) networks with a warden Willie, where Alice intends to transmit the covert signal to a near user Bob under the cover of a far user Carol via STAR-RIS. We aim to maximize the covert transmission rate by jointly optimizing the active and passive beamforming as well as the UAV location. The error detection probability and optimal detection threshold for Willie are first derived to obtain an analytic solution for the minimum detection error probability. Then, an alternating optimization algorithm is proposed to maximize the covert transmission rate under the condition of guaranteeing the communication of Carol and satisfying the covertness constraint of Bob. Specifically, the nonconvex problem is decomposed into three sub-problems by block coordinate descent, which are then solved using semidefinite relaxation and successive convex approximation. Finally, simulation results are presented to demonstrate the effectiveness of the proposed covert communication scheme for STAR-RIS assisted UAV air-ground networks.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"245-259"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231239","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.2024.3460031
Hyung-Joo Moon;Chan-Byoung Chae
In this paper, we investigate a multi-user downlink scheduling and power allocation strategy for urban air mobility (UAM) within a 6G non-terrestrial network (NTN) framework that integrates satellite and ground networks. We consider a system model involving multiple ground stations (GSs) and a single satellite, addressing the sum rate maximization problem with link-association, power, elevation angle, and minimum quality-of-service constraints. The proposed method initially segregates satellite-serviced users to reduce interference among the remaining GS-serviced users, taking into account the locations and movements of those UAMs. Subsequently, using a graph-theoretical approach, we convert the GS link association problem into a minimum-cost maximum-flow problem. In this process, we employ an analytical method involving polynomial approximations or a numerical method using integral approximation through the sum of time-sampled parameters. We then address the non-convex power allocation problem for scheduled links through iterative algorithms. The proposed scheduling and power allocation algorithms effectively manage interference in multi-UAM and multi-GS environments, and their performance is validated through extensive simulation results. Our study provides a comprehensive framework and strategy for efficient downlink transmission in future UAM operations, paving the way for novel applications in 6G NTN.
{"title":"Cooperative Ground-Satellite Scheduling and Power Allocation for Urban Air Mobility Networks","authors":"Hyung-Joo Moon;Chan-Byoung Chae","doi":"10.1109/JSAC.2024.3460031","DOIUrl":"10.1109/JSAC.2024.3460031","url":null,"abstract":"In this paper, we investigate a multi-user downlink scheduling and power allocation strategy for urban air mobility (UAM) within a 6G non-terrestrial network (NTN) framework that integrates satellite and ground networks. We consider a system model involving multiple ground stations (GSs) and a single satellite, addressing the sum rate maximization problem with link-association, power, elevation angle, and minimum quality-of-service constraints. The proposed method initially segregates satellite-serviced users to reduce interference among the remaining GS-serviced users, taking into account the locations and movements of those UAMs. Subsequently, using a graph-theoretical approach, we convert the GS link association problem into a minimum-cost maximum-flow problem. In this process, we employ an analytical method involving polynomial approximations or a numerical method using integral approximation through the sum of time-sampled parameters. We then address the non-convex power allocation problem for scheduled links through iterative algorithms. The proposed scheduling and power allocation algorithms effectively manage interference in multi-UAM and multi-GS environments, and their performance is validated through extensive simulation results. Our study provides a comprehensive framework and strategy for efficient downlink transmission in future UAM operations, paving the way for novel applications in 6G NTN.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"218-233"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231225","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}
Low Earth orbit (LEO) satellite communications (SATCOM) with ubiquitous global connectivity is deemed a pivotal catalyst in advancing wireless communication systems for 5G and beyond. LEO SATCOM excels in delivering versatile information services across expansive areas, facilitating both unicast and multicast transmissions via high-speed broadband capability. Nonetheless, given the broadband coverage of LEO SATCOM, traffic demand distribution within the service area is non-uniform, and the time/frequency/power resources available at LEO satellites remain significantly limited. Motivated by these challenges, we propose a rate-matching framework for non-orthogonal unicast and multicast (NOUM) transmission. Our approach aims to minimize the difference between offered rates and traffic demands for both unicast and multicast messages. By multiplexing unicast and multicast transmissions over the same radio resource, rate-splitting multiple access (RSMA) is employed to manage interference between unicast and multicast streams, as well as inter-user interference under imperfect channel state information at the LEO satellite. To address the formulated problem’s non-smoothness and non-convexity, the common rate is approximated using the LogSumExp technique. Thereafter, we represent the common rate portion as the ratio of the approximated function, converting the problem into an unconstrained form. A generalized power iteration (GPI)-based algorithm, coined GPI-RS-NOUM, is proposed upon this reformulation. Through comprehensive numerical analysis across diverse simulation setups, we demonstrate that the proposed framework outperforms various benchmarks for LEO SATCOM with uneven traffic demands.
{"title":"Rate-Splitting for Joint Unicast and Multicast Transmission in LEO Satellite Networks With Non-Uniform Traffic Demand","authors":"Jaehyup Seong;Juha Park;Dong-Hyun Jung;Jeonghun Park;Wonjae Shin","doi":"10.1109/JSAC.2024.3460073","DOIUrl":"10.1109/JSAC.2024.3460073","url":null,"abstract":"Low Earth orbit (LEO) satellite communications (SATCOM) with ubiquitous global connectivity is deemed a pivotal catalyst in advancing wireless communication systems for 5G and beyond. LEO SATCOM excels in delivering versatile information services across expansive areas, facilitating both unicast and multicast transmissions via high-speed broadband capability. Nonetheless, given the broadband coverage of LEO SATCOM, traffic demand distribution within the service area is non-uniform, and the time/frequency/power resources available at LEO satellites remain significantly limited. Motivated by these challenges, we propose a rate-matching framework for non-orthogonal unicast and multicast (NOUM) transmission. Our approach aims to minimize the difference between offered rates and traffic demands for both unicast and multicast messages. By multiplexing unicast and multicast transmissions over the same radio resource, rate-splitting multiple access (RSMA) is employed to manage interference between unicast and multicast streams, as well as inter-user interference under imperfect channel state information at the LEO satellite. To address the formulated problem’s non-smoothness and non-convexity, the common rate is approximated using the LogSumExp technique. Thereafter, we represent the common rate portion as the ratio of the approximated function, converting the problem into an unconstrained form. A generalized power iteration (GPI)-based algorithm, coined GPI-RS-NOUM, is proposed upon this reformulation. Through comprehensive numerical analysis across diverse simulation setups, we demonstrate that the proposed framework outperforms various benchmarks for LEO SATCOM with uneven traffic demands.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"122-138"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231226","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 : 2024-09-12DOI: 10.1109/JSAC.2024.3459035
Ming Tao;Xueqiang Li;Jie Feng;Dapeng Lan;Jun Du;Celimuge Wu
In the paradigm of ubiquitous edge computing, with those advantages, e.g., high mobility, fast response, flexibility and controllability, and low cost of use, Unmanned Aerial Vehicles (UAVs) could be used not only as relays to assist with data collection, but also as computing power nodes to process uncomplicated computational workloads from ground users. Especially, UAVs could be employed to provide alternative computing power resources in field, lake, post-disaster and other complex regional environments. In this paper, to address the issue of computing power scheduling in UAVs empowered aerial computing systems, a scenario where multiple UAVs from the same departure station cooperatively fly over hovering points and achieve the data collection and computation in a decentralized manner is investigated. Nevertheless, due to limited onboard battery capacities of UAVs and diverse service requests of ground users, it is necessary to optimize energy efficiency and service fairness for improving mission execution capabilities of UAVs and the quality of service (QoS) experienced by ground users, and a joint optimization problem of energy efficiency and service fairness is formulated. Through considering complex coupling associations among the departure station, flight paths and hovering points of UAVs, the problem is investigated from the trajectory planning of UAVs and the location planning for both the departure station and hovering points. Proving investigations to be Markov decision processes (MDP), multi-agent cooperation approaches are proposed as promising solutions, and simulation results have been shown to demonstrate that the performance achieved by the proposal outperforms that achieved by schemes commonly used in literatures.
{"title":"Multi-Agent Cooperation for Computing Power Scheduling in UAVs Empowered Aerial Computing Systems","authors":"Ming Tao;Xueqiang Li;Jie Feng;Dapeng Lan;Jun Du;Celimuge Wu","doi":"10.1109/JSAC.2024.3459035","DOIUrl":"10.1109/JSAC.2024.3459035","url":null,"abstract":"In the paradigm of ubiquitous edge computing, with those advantages, e.g., high mobility, fast response, flexibility and controllability, and low cost of use, Unmanned Aerial Vehicles (UAVs) could be used not only as relays to assist with data collection, but also as computing power nodes to process uncomplicated computational workloads from ground users. Especially, UAVs could be employed to provide alternative computing power resources in field, lake, post-disaster and other complex regional environments. In this paper, to address the issue of computing power scheduling in UAVs empowered aerial computing systems, a scenario where multiple UAVs from the same departure station cooperatively fly over hovering points and achieve the data collection and computation in a decentralized manner is investigated. Nevertheless, due to limited onboard battery capacities of UAVs and diverse service requests of ground users, it is necessary to optimize energy efficiency and service fairness for improving mission execution capabilities of UAVs and the quality of service (QoS) experienced by ground users, and a joint optimization problem of energy efficiency and service fairness is formulated. Through considering complex coupling associations among the departure station, flight paths and hovering points of UAVs, the problem is investigated from the trajectory planning of UAVs and the location planning for both the departure station and hovering points. Proving investigations to be Markov decision processes (MDP), multi-agent cooperation approaches are proposed as promising solutions, and simulation results have been shown to demonstrate that the performance achieved by the proposal outperforms that achieved by schemes commonly used in literatures.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3521-3535"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174700","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 : 2024-09-12DOI: 10.1109/JSAC.2024.3459079
Xiangdong Zheng;Yuxin Wu;Lisheng Fan;Xianfu Lei;Rose Qingyang Hu;George K. Karagiannidis
In this paper, we investigate a sensing-enabled integrated space-air-ground (SAG) data collection network, in which an unmanned aerial vehicle (UAV) can not only work singly to sense data from multiple targets but also collaborate with a low-earth orbit (LEO) satellite to collect communication data from multiple users. Since the coverage of the UAV is much smaller than that of the LEO satellite, we first determine the set of usable users and targets for the UAV by analyzing the signal-to-noise ratios between the UAV and the users and targets. Based on this, we pose an optimization problem designed to maximize the total amount of data collected in the network while satisfying the constraints of UAV energy consumption, memory capacity, and minimum amount of sensor data per target. Moreover, considering that the network consists of three layers and the UAV has dual functions of communication and sensing, this problem is solved by jointly optimizing the scheduling of the users’ data upload scheme, the UAV trajectory, and the allocation of communication and sensing time. However, the formulated problem is a mixed integer nonlinear programming (MINLP) problem, so it is difficult to find the optimal solution. Therefore, we further design an alternating iterative optimization algorithm (AIOA) framework to find an appropriate solution. Specifically, we alternately optimize the UAV trajectory, time allocation strategy, and data upload schedule in each iteration. Finally, simulation experiments validate the effectiveness of the AIOA and its superiority over other benchmarks in terms of the amount of data collected.
{"title":"Dual-Functional UAV-Empowered Space-Air-Ground Networks: Joint Communication and Sensing","authors":"Xiangdong Zheng;Yuxin Wu;Lisheng Fan;Xianfu Lei;Rose Qingyang Hu;George K. Karagiannidis","doi":"10.1109/JSAC.2024.3459079","DOIUrl":"10.1109/JSAC.2024.3459079","url":null,"abstract":"In this paper, we investigate a sensing-enabled integrated space-air-ground (SAG) data collection network, in which an unmanned aerial vehicle (UAV) can not only work singly to sense data from multiple targets but also collaborate with a low-earth orbit (LEO) satellite to collect communication data from multiple users. Since the coverage of the UAV is much smaller than that of the LEO satellite, we first determine the set of usable users and targets for the UAV by analyzing the signal-to-noise ratios between the UAV and the users and targets. Based on this, we pose an optimization problem designed to maximize the total amount of data collected in the network while satisfying the constraints of UAV energy consumption, memory capacity, and minimum amount of sensor data per target. Moreover, considering that the network consists of three layers and the UAV has dual functions of communication and sensing, this problem is solved by jointly optimizing the scheduling of the users’ data upload scheme, the UAV trajectory, and the allocation of communication and sensing time. However, the formulated problem is a mixed integer nonlinear programming (MINLP) problem, so it is difficult to find the optimal solution. Therefore, we further design an alternating iterative optimization algorithm (AIOA) framework to find an appropriate solution. Specifically, we alternately optimize the UAV trajectory, time allocation strategy, and data upload schedule in each iteration. Finally, simulation experiments validate the effectiveness of the AIOA and its superiority over other benchmarks in terms of the amount of data collected.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3412-3427"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174757","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 : 2024-09-12DOI: 10.1109/JSAC.2024.3459074
Pinjun Zheng;Xing Liu;Tareq Y. Al-Naffouri
Low Earth orbit (LEO) satellites and reconfigurable intelligent surfaces (RISs) have recently drawn significant attention as two transformative technologies, and the synergy between them emerges as a promising paradigm for providing cross-environment communication and positioning services. This paper investigates an integrated terrestrial and non-terrestrial wireless network that leverages LEO satellites and RISs to achieve simultaneous tracking of the three-dimensional (3D) position, 3D velocity, and 3D orientation of user equipment (UE). To address inherent challenges including nonlinear observation function, constrained UE state, and unknown observation statistics, we develop a Riemannian manifold-based unscented Kalman filter (UKF) method. This method propagates statistics over nonlinear functions using generated sigma points and maintains state constraints through projection onto the defined manifold space. Additionally, by employing Fisher information matrices (FIMs) of the sigma points, a belief assignment principle is proposed to approximate the unknown observation covariance matrix, thereby ensuring accurate measurement updates in the UKF procedure. Numerical results demonstrate a substantial enhancement in tracking accuracy facilitated by RIS integration, despite urban signal reception challenges from LEO satellites. In addition, extensive simulations underscore the superior performance of the proposed tracking method and FIM-based belief assignment over the adopted benchmarks. Furthermore, the robustness of the proposed UKF is verified across various uncertainty levels.
{"title":"LEO- and RIS-Empowered User Tracking: A Riemannian Manifold Approach","authors":"Pinjun Zheng;Xing Liu;Tareq Y. Al-Naffouri","doi":"10.1109/JSAC.2024.3459074","DOIUrl":"10.1109/JSAC.2024.3459074","url":null,"abstract":"Low Earth orbit (LEO) satellites and reconfigurable intelligent surfaces (RISs) have recently drawn significant attention as two transformative technologies, and the synergy between them emerges as a promising paradigm for providing cross-environment communication and positioning services. This paper investigates an integrated terrestrial and non-terrestrial wireless network that leverages LEO satellites and RISs to achieve simultaneous tracking of the three-dimensional (3D) position, 3D velocity, and 3D orientation of user equipment (UE). To address inherent challenges including nonlinear observation function, constrained UE state, and unknown observation statistics, we develop a Riemannian manifold-based unscented Kalman filter (UKF) method. This method propagates statistics over nonlinear functions using generated sigma points and maintains state constraints through projection onto the defined manifold space. Additionally, by employing Fisher information matrices (FIMs) of the sigma points, a belief assignment principle is proposed to approximate the unknown observation covariance matrix, thereby ensuring accurate measurement updates in the UKF procedure. Numerical results demonstrate a substantial enhancement in tracking accuracy facilitated by RIS integration, despite urban signal reception challenges from LEO satellites. In addition, extensive simulations underscore the superior performance of the proposed tracking method and FIM-based belief assignment over the adopted benchmarks. Furthermore, the robustness of the proposed UKF is verified across various uncertainty levels.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3445-3461"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174760","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 : 2024-09-12DOI: 10.1109/JSAC.2024.3459055
Fengsheng Wei;Gang Feng;Shuang Qin;Youkun Peng;Yijing Liu
Unmanned aerial vehicle (UAV) has been recognized as a key supplement for terrestrial networks to meet the stringent requirements of the forthcoming 6G networks. However, a significant challenge lies in providing differentiated services through a common UAV network, without the need to deploy individual networks for each service type. In this paper, we consider the problem of joint network slicing and UAV deployment under dynamic wireless environments as well as the uncertain traffic demands. To overcome the challenges posed by the network dynamics, we propose an intelligent hierarchical UAV slicing framework that operates at two different time-scales. At the large time-scale, the problem of inter-slice resource slicing and UAV deployment is formulated as a mixed integer nonlinear program, and a decomposition technique is applied to resolve it. At the small time-scale, the problem of intra-slice resource adjustment is modeled as a stochastic game and a distributed learning algorithm is proposed to find its Nash Equilibrium. Simulation results demonstrate that the proposed framework is lightweight and outperforms a number of known benchmark algorithms in terms of system utility, throughput and transmission delay.
{"title":"Hierarchical Network Slicing for UAV-Assisted Wireless Networks With Deployment Optimization","authors":"Fengsheng Wei;Gang Feng;Shuang Qin;Youkun Peng;Yijing Liu","doi":"10.1109/JSAC.2024.3459055","DOIUrl":"10.1109/JSAC.2024.3459055","url":null,"abstract":"Unmanned aerial vehicle (UAV) has been recognized as a key supplement for terrestrial networks to meet the stringent requirements of the forthcoming 6G networks. However, a significant challenge lies in providing differentiated services through a common UAV network, without the need to deploy individual networks for each service type. In this paper, we consider the problem of joint network slicing and UAV deployment under dynamic wireless environments as well as the uncertain traffic demands. To overcome the challenges posed by the network dynamics, we propose an intelligent hierarchical UAV slicing framework that operates at two different time-scales. At the large time-scale, the problem of inter-slice resource slicing and UAV deployment is formulated as a mixed integer nonlinear program, and a decomposition technique is applied to resolve it. At the small time-scale, the problem of intra-slice resource adjustment is modeled as a stochastic game and a distributed learning algorithm is proposed to find its Nash Equilibrium. Simulation results demonstrate that the proposed framework is lightweight and outperforms a number of known benchmark algorithms in terms of system utility, throughput and transmission delay.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3705-3718"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174714","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 : 2024-09-12DOI: 10.1109/JSAC.2024.3459039
Yinuo Zhao;Chi Harold Liu;Tianjiao Yi;Guozheng Li;Dapeng Wu
The integrated ground-air-space (GAS) communications system can enhance post-disaster rescue and management efforts when traditional networks fail, by navigating unmanned ground vehicles (UGVs) and unmanned arieal vehicles (UAVs) to collaboratively collect sufficient data from point-of-interests (PoIs) in a timely manner. In this paper, we consider the GAS vehicular crowdsensing (VCS) campaign, where UGVs dispatch and callback UAVs periodically across multiple stops in the workzone, to maximize the total collected amount of data, geographic fairness while minimizing the energy consumption simultaneously. Specifically, we propose an energy-efficient, go-directed hierarchical multi-agent deep reinforcement learning (MADRL) method with discrete diffusion models called “gMADRL-VCS”, to optimize the high-level goal-conditioned navigation policies of UGVs, and the low-level long-term sensing strategies of UAVs. Extensive experimental results on two real-world datasets in Roma, Italy, and Hong Kong SAR, China show that gMADRL-VCS outperforms baselines in terms of energy efficiency, data collection ratio, energy consumption, and UAV-UGV cooperation factor.
{"title":"Energy-Efficient Ground-Air-Space Vehicular Crowdsensing by Hierarchical Multi-Agent Deep Reinforcement Learning With Diffusion Models","authors":"Yinuo Zhao;Chi Harold Liu;Tianjiao Yi;Guozheng Li;Dapeng Wu","doi":"10.1109/JSAC.2024.3459039","DOIUrl":"10.1109/JSAC.2024.3459039","url":null,"abstract":"The integrated ground-air-space (GAS) communications system can enhance post-disaster rescue and management efforts when traditional networks fail, by navigating unmanned ground vehicles (UGVs) and unmanned arieal vehicles (UAVs) to collaboratively collect sufficient data from point-of-interests (PoIs) in a timely manner. In this paper, we consider the GAS vehicular crowdsensing (VCS) campaign, where UGVs dispatch and callback UAVs periodically across multiple stops in the workzone, to maximize the total collected amount of data, geographic fairness while minimizing the energy consumption simultaneously. Specifically, we propose an energy-efficient, go-directed hierarchical multi-agent deep reinforcement learning (MADRL) method with discrete diffusion models called “gMADRL-VCS”, to optimize the high-level goal-conditioned navigation policies of UGVs, and the low-level long-term sensing strategies of UAVs. Extensive experimental results on two real-world datasets in Roma, Italy, and Hong Kong SAR, China show that gMADRL-VCS outperforms baselines in terms of energy efficiency, data collection ratio, energy consumption, and UAV-UGV cooperation factor.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3566-3580"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174769","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：