Pub Date : 2024-09-13DOI: 10.1109/JSAC.2024.3460050
Jun Li;Shuping Dang;Xuan Chen;Miaowen Wen;Marco Di Renzo;Huseyin Arslan
Unmanned aerial vehicles (UAVs) serve as flexible aerial platforms, enriching air-ground communication networks in various ways. To support massive connectivity within limited time-frequency blocks, non-orthogonal multiple access (NOMA) is proposed to be integrated into UAV networks. However, a common issue associated with almost all NOMA schemes is the susceptibility to inter-user interference (IUI). Therefore, in this paper, we propose a multi-UAV cooperative system aided by NOMA with index modulation (IM), termed MCU-NOMA-IM, to improve the performance of air-ground networks by mitigating IUI and also avoiding the successive interference cancellation (SIC) decoding method that is prone to error floors. With MCU-NOMA-IM, the information bits pertaining to multiple UAVs are mapped into multiple dimensions, including the modulated symbols, subcarrier indices, and energy allocation patterns. To fully investigate the performance of MCU-NOMA-IM on air-ground networks, we consider scenarios in the presence of three and four UAVs and derive upper-bounds for the bit error rates (BERs). In addition, we propose a multi-clustered-UAV cooperative system aided by NOMA with IM (MCCU-NOMA-IM), which groups closely located UAVs into several clusters to reduce the requirement for time resources. Simulation results demonstrate that both MCU-NOMA-IM and MCCU-NOMA-IM greatly outperform cooperative NOMA and non-cooperative NOMA-IM schemes, especially for distant UAVs when the signal-to-noise ratio is sufficiently high. Also, we show that the derived BER upper bounds are asymptotically tight.
{"title":"Cooperative Non-Orthogonal Multiple Access With Index Modulation for Air-Ground Multi-UAV Networks","authors":"Jun Li;Shuping Dang;Xuan Chen;Miaowen Wen;Marco Di Renzo;Huseyin Arslan","doi":"10.1109/JSAC.2024.3460050","DOIUrl":"10.1109/JSAC.2024.3460050","url":null,"abstract":"Unmanned aerial vehicles (UAVs) serve as flexible aerial platforms, enriching air-ground communication networks in various ways. To support massive connectivity within limited time-frequency blocks, non-orthogonal multiple access (NOMA) is proposed to be integrated into UAV networks. However, a common issue associated with almost all NOMA schemes is the susceptibility to inter-user interference (IUI). Therefore, in this paper, we propose a multi-UAV cooperative system aided by NOMA with index modulation (IM), termed MCU-NOMA-IM, to improve the performance of air-ground networks by mitigating IUI and also avoiding the successive interference cancellation (SIC) decoding method that is prone to error floors. With MCU-NOMA-IM, the information bits pertaining to multiple UAVs are mapped into multiple dimensions, including the modulated symbols, subcarrier indices, and energy allocation patterns. To fully investigate the performance of MCU-NOMA-IM on air-ground networks, we consider scenarios in the presence of three and four UAVs and derive upper-bounds for the bit error rates (BERs). In addition, we propose a multi-clustered-UAV cooperative system aided by NOMA with IM (MCCU-NOMA-IM), which groups closely located UAVs into several clusters to reduce the requirement for time resources. Simulation results demonstrate that both MCU-NOMA-IM and MCCU-NOMA-IM greatly outperform cooperative NOMA and non-cooperative NOMA-IM schemes, especially for distant UAVs when the signal-to-noise ratio is sufficiently high. Also, we show that the derived BER upper bounds are asymptotically tight.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"171-185"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231221","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}
Satellite communication (SatCom) is regarded as a key enabler for bridging connectivity and capacity gaps in sixth-generation (6G) networks. However, the proliferation of Low Earth Orbit (LEO) satellites raises significant intersystem interference risks with Geostationary Earth Orbit (GEO) systems. This paper introduces a cooperative multi-satellite multi-reconfigurable intelligent surface (RIS) transmission framework to mitigate such interference while enhancing LEO SatCom performance. Specifically, cooperative beamforming is designed under a non-coherent cell-free paradigm, considering both adaptive and max ratio (MR) precoding, as well as statistical and two-timescale channel state information (CSI), aiming to synthesize the advantages of cell-free and RIS into SatCom in a practical way. Firstly, an alternating optimization (AO)-based design leveraging statistical CSI with adaptive precoding is proposed. Then, we propose a power allocation algorithm under MR precoding with given RIS phase shifts obtained from the former, along with a direct two-stage design bypassing prior results. Additionally, we extend derived closed-form expressions and proposed algorithms to exploit two-timescale CSI. Numerical results demonstrate the impact of intersystem interference mitigation constraints, compare the performance of proposed algorithms, draw insights into the effects of transmit power, interference threshold, and Rician factors, validate SatCom performance enhancements achieved by RISs, and discuss the advantages of multi-satellite cooperation.
{"title":"Cooperative Multi-Satellite and Multi-RIS Beamforming: Enhancing LEO SatCom and Mitigating LEO-GEO Intersystem Interference","authors":"Ziyuan Zheng;Wenpeng Jing;Zhaoming Lu;Qingqing Wu;Haijun Zhang;David Gesbert","doi":"10.1109/JSAC.2024.3460068","DOIUrl":"10.1109/JSAC.2024.3460068","url":null,"abstract":"Satellite communication (SatCom) is regarded as a key enabler for bridging connectivity and capacity gaps in sixth-generation (6G) networks. However, the proliferation of Low Earth Orbit (LEO) satellites raises significant intersystem interference risks with Geostationary Earth Orbit (GEO) systems. This paper introduces a cooperative multi-satellite multi-reconfigurable intelligent surface (RIS) transmission framework to mitigate such interference while enhancing LEO SatCom performance. Specifically, cooperative beamforming is designed under a non-coherent cell-free paradigm, considering both adaptive and max ratio (MR) precoding, as well as statistical and two-timescale channel state information (CSI), aiming to synthesize the advantages of cell-free and RIS into SatCom in a practical way. Firstly, an alternating optimization (AO)-based design leveraging statistical CSI with adaptive precoding is proposed. Then, we propose a power allocation algorithm under MR precoding with given RIS phase shifts obtained from the former, along with a direct two-stage design bypassing prior results. Additionally, we extend derived closed-form expressions and proposed algorithms to exploit two-timescale CSI. Numerical results demonstrate the impact of intersystem interference mitigation constraints, compare the performance of proposed algorithms, draw insights into the effects of transmit power, interference threshold, and Rician factors, validate SatCom performance enhancements achieved by RISs, and discuss the advantages of multi-satellite cooperation.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"279-296"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231231","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}
With the advantages of seamless coverage and ubiquitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services for future Connected Automated Vehicles (CAVs) to satisfy onboard collaborative viewing, traffic sensing, and metaverse entertainments in remote areas. However, the heterogeneous caching hardware, communication environments, and frequent network dynamics make the optimization of content caching policy highly complicated. Firstly, considering all LEO satellites as caching satellites can lead to content duplication and radio interference, causing storage waste and NTN transmission quality deterioration. Secondly, how to provide customized QoS by intra-layer and inter-layer cooperative caching in such complicated environments remains an open issue. Thus, we propose a Delay-Motivated Ant Colony Optimization (DM-ACO) scheme to select caching LEO satellites with reduced system propagation delay. Then, the Multi-Agent Deep Reinforcement Learning-based Hierarchical Caching and Asynchronous Updating (MADRL-HCAU) strategy is designed to manage the caching capacity of LEO satellites and UAVs, providing customized services for CAVs and dispensing the peak traffic. Simulation results illustrate that the proposed scheme can not only effectively accelerate the caching refreshing and content downloading process but also significantly reduce the packet drop and improve the cache hit ratio.
{"title":"On a Hierarchical Content Caching and Asynchronous Updating Scheme for Non-Terrestrial Network-Assisted Connected Automated Vehicles","authors":"Bomin Mao;Yangbo Liu;Hongzhi Guo;Yijie Xun;Jiadai Wang;Jiajia Liu;Nei Kato","doi":"10.1109/JSAC.2024.3460063","DOIUrl":"10.1109/JSAC.2024.3460063","url":null,"abstract":"With the advantages of seamless coverage and ubiquitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services for future Connected Automated Vehicles (CAVs) to satisfy onboard collaborative viewing, traffic sensing, and metaverse entertainments in remote areas. However, the heterogeneous caching hardware, communication environments, and frequent network dynamics make the optimization of content caching policy highly complicated. Firstly, considering all LEO satellites as caching satellites can lead to content duplication and radio interference, causing storage waste and NTN transmission quality deterioration. Secondly, how to provide customized QoS by intra-layer and inter-layer cooperative caching in such complicated environments remains an open issue. Thus, we propose a Delay-Motivated Ant Colony Optimization (DM-ACO) scheme to select caching LEO satellites with reduced system propagation delay. Then, the Multi-Agent Deep Reinforcement Learning-based Hierarchical Caching and Asynchronous Updating (MADRL-HCAU) strategy is designed to manage the caching capacity of LEO satellites and UAVs, providing customized services for CAVs and dispensing the peak traffic. Simulation results illustrate that the proposed scheme can not only effectively accelerate the caching refreshing and content downloading process but also significantly reduce the packet drop and improve the cache hit ratio.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"64-74"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231234","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 this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) network, where multiple UAVs with caching and computation functionalities are deployed to satisfy the heterogeneous content and service requests from the user equipments (UEs). In order to comprehensively characterize the capability of our considered network in satisfying the UEs’ requests, we define the weighted sum of the content cache hit ratio and the service delay shrinkage ratio as the average quality-of-experience (QoE) of our network and adopt it as the performance metric. Through analysis, we show how the average QoE of our network is dependent on the content cache and service placement decisions at the UAVs, as well as the computation task offloading decisions at the UEs, thus enabling us to formulate an average QoE maximization problem, subject to practical constraints on the UAVs’ caching and computation capabilities. To solve this NP-hard problem, we decompose it into two sub-problems, namely, the content cache and service placement optimization sub-problem and the task offloading optimization sub-problem. Gibbs sampling-based and matching game-based algorithms are proposed to efficiently solve these sub-problems iteratively. Via numerical results, we validate the effectiveness of our proposed algorithms. Compared to various benchmarks, we demonstrate that our proposed algorithms can significantly improve the average QoE of our considered network, especially when the caching and computation resources of the UAVs are limited.
{"title":"Joint Content Caching, Service Placement, and Task Offloading in UAV-Enabled Mobile Edge Computing Networks","authors":"Youhan Zhao;Chenxi Liu;Xiaoling Hu;Jianhua He;Mugen Peng;Derrick Wing Kwan Ng;Tony Q. S. Quek","doi":"10.1109/JSAC.2024.3460049","DOIUrl":"10.1109/JSAC.2024.3460049","url":null,"abstract":"In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) network, where multiple UAVs with caching and computation functionalities are deployed to satisfy the heterogeneous content and service requests from the user equipments (UEs). In order to comprehensively characterize the capability of our considered network in satisfying the UEs’ requests, we define the weighted sum of the content cache hit ratio and the service delay shrinkage ratio as the average quality-of-experience (QoE) of our network and adopt it as the performance metric. Through analysis, we show how the average QoE of our network is dependent on the content cache and service placement decisions at the UAVs, as well as the computation task offloading decisions at the UEs, thus enabling us to formulate an average QoE maximization problem, subject to practical constraints on the UAVs’ caching and computation capabilities. To solve this NP-hard problem, we decompose it into two sub-problems, namely, the content cache and service placement optimization sub-problem and the task offloading optimization sub-problem. Gibbs sampling-based and matching game-based algorithms are proposed to efficiently solve these sub-problems iteratively. Via numerical results, we validate the effectiveness of our proposed algorithms. Compared to various benchmarks, we demonstrate that our proposed algorithms can significantly improve the average QoE of our considered network, especially when the caching and computation resources of the UAVs are limited.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"51-63"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231237","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.2024.3460058
Ruimao He;Xuefei Zhang;Qimei Cui;Xiaofeng Tao
Low Earth orbit (LEO) satellite system has revolutionized the way to provide wireless seamless access on a global scale. One of the primary limitations is the low data rates resulting from Doppler shifts induced by the high mobility of LEO satellites. Although orthogonal time frequency space (OTFS) modulation has been proposed to deal with the serious Doppler problem by converting a time-variant fading channel in the time-frequency (TF) domain into a time-invariant channel in the delay-Doppler (DD) domain, it needs to be reconsidered in the LEO satellite system due to the facts that the scale of Doppler axes is not big enough and the velocity of satellite is too fast. In this paper, we analyze two interferences caused by Doppler that will be produced in OTFS-based LEO satellite system. Specifically, we establish a system model of LEO satellite-to-ground communication, involving the fractional Doppler interference (FDI) from the non-integer Doppler tap, and the other is the squint Doppler interference (SDI) from the frequency-dependent Doppler. By deriving the closed-form expressions of FDI and SDI respectively, we find that the simplest but most practical solution to mitigate interference is to increase the value of DD plane bins. Finally, numerical results showcase the significant impact of Doppler on transmission signals by quantifying the signal-to-interference (SIR) ratio and bit error rate (BER) and highlight the dominance of an applicable number of bins on alleviating Doppler in OTFS-based LEO satellite system.
{"title":"Doppler Interference Analysis for OTFS-Based LEO Satellite System","authors":"Ruimao He;Xuefei Zhang;Qimei Cui;Xiaofeng Tao","doi":"10.1109/JSAC.2024.3460058","DOIUrl":"10.1109/JSAC.2024.3460058","url":null,"abstract":"Low Earth orbit (LEO) satellite system has revolutionized the way to provide wireless seamless access on a global scale. One of the primary limitations is the low data rates resulting from Doppler shifts induced by the high mobility of LEO satellites. Although orthogonal time frequency space (OTFS) modulation has been proposed to deal with the serious Doppler problem by converting a time-variant fading channel in the time-frequency (TF) domain into a time-invariant channel in the delay-Doppler (DD) domain, it needs to be reconsidered in the LEO satellite system due to the facts that the scale of Doppler axes is not big enough and the velocity of satellite is too fast. In this paper, we analyze two interferences caused by Doppler that will be produced in OTFS-based LEO satellite system. Specifically, we establish a system model of LEO satellite-to-ground communication, involving the fractional Doppler interference (FDI) from the non-integer Doppler tap, and the other is the squint Doppler interference (SDI) from the frequency-dependent Doppler. By deriving the closed-form expressions of FDI and SDI respectively, we find that the simplest but most practical solution to mitigate interference is to increase the value of DD plane bins. Finally, numerical results showcase the significant impact of Doppler on transmission signals by quantifying the signal-to-interference (SIR) ratio and bit error rate (BER) and highlight the dominance of an applicable number of bins on alleviating Doppler in OTFS-based LEO satellite system.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"75-89"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231227","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.2024.3460065
Weihao Mao;Yang Lu;Gaofeng Pan;Bo Ai
Both the space-air-ground integrated networks (SAGIN) and the integrated sensing and communication (ISAC) are promising technologies in future communication systems. This paper investigates the mobile user (MU) tracking and robust beamforming design by the unmanned aerial vehicle (UAV) in an SAGIN-ISAC system. Two schemes for acquiring the location information of MUs at the UAV are proposed, namely the space-assisted and ISAC-assisted schemes. The former requires the precise location information from the satellite by the space-air transmission, while the latter estimates the location information of MUs via a proposed extended Kalman filter based algorithm. The obtained location information is then utilized to predict the channel distribution of MUs, which can be used to formulate an outage-constrained energy efficiency (EE) maximization problem. The considered problem is first reformulated based on the Bernstein-type inequality to derive computationally tractable forms of the outage probability constraints. Then, the reformulated problem is solved via the semi-definite relaxation (SDR) and successive convex approximation methods, where the tightness of employing SDR is theoretically proved. Numerical results illustrate the trajectories of the UAV for tracking MUs under the space-assisted and ISAC-assisted schemes, and discuss the impact of the space-air transmission on the EE performance. It is observed that there exists a trade-off between space-air transmission overhead and location prediction precision of MUs. By integrating the ISAC in SAGIN, the information demand from the space is reduced compared with traditional SAGIN.
{"title":"UAV-Assisted Communications in SAGIN-ISAC: Mobile User Tracking and Robust Beamforming","authors":"Weihao Mao;Yang Lu;Gaofeng Pan;Bo Ai","doi":"10.1109/JSAC.2024.3460065","DOIUrl":"10.1109/JSAC.2024.3460065","url":null,"abstract":"Both the space-air-ground integrated networks (SAGIN) and the integrated sensing and communication (ISAC) are promising technologies in future communication systems. This paper investigates the mobile user (MU) tracking and robust beamforming design by the unmanned aerial vehicle (UAV) in an SAGIN-ISAC system. Two schemes for acquiring the location information of MUs at the UAV are proposed, namely the space-assisted and ISAC-assisted schemes. The former requires the precise location information from the satellite by the space-air transmission, while the latter estimates the location information of MUs via a proposed extended Kalman filter based algorithm. The obtained location information is then utilized to predict the channel distribution of MUs, which can be used to formulate an outage-constrained energy efficiency (EE) maximization problem. The considered problem is first reformulated based on the Bernstein-type inequality to derive computationally tractable forms of the outage probability constraints. Then, the reformulated problem is solved via the semi-definite relaxation (SDR) and successive convex approximation methods, where the tightness of employing SDR is theoretically proved. Numerical results illustrate the trajectories of the UAV for tracking MUs under the space-assisted and ISAC-assisted schemes, and discuss the impact of the space-air transmission on the EE performance. It is observed that there exists a trade-off between space-air transmission overhead and location prediction precision of MUs. By integrating the ISAC in SAGIN, the information demand from the space is reduced compared with traditional SAGIN.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"186-200"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231242","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 visionary ideals of “Internet of Everything” and “Digital Twins”, the future 6G will deeply integrate diverse heterogeneous networks such as satellite and aerial networks to support seamless connectivity and efficient interoperability, also known as space-air-ground integrated networks (SAGIN), in which the grant-free uplink random access based on Slotted ALOHA (S-ALOHA) can reduce access latency and complexity for massive Internet of Things (IoT) devices. However, with the increasing number of IoT users, the collision probability of S-ALOHA escalates and further degrades the system performance. In this paper, we focus on the massive IoT device uplink access in SAGIN aided by high altitude platform stations (HAPS), investigating power allocation for IoT devices to maximize system access capability and spectral efficiency (SE). Specifically, we first optimize 3D deployment of HAPS. Then the resilient massive access (RMA) based on flexible fusion of S-ALOHA and non-orthogonal multiple access methods is proposed. To maximize system SE with device power constraints, we model the sequential decision problem as a Markov decision process and solve it with the Advantage Actor-Critic (A2C) algorithm. Simulation results demonstrate the proposed RMA can significantly improve the IoT terminal successful access probability and the resource scheduling based on A2C also significantly increases the system SE with low complexity.
{"title":"Resilient Massive Access for SAGIN: A Deep Reinforcement Learning Approach","authors":"Chaowei Wang;Mingliang Pang;Tong Wu;Feifei Gao;Lingli Zhao;Jiabin Chen;Wenyuan Wang;Dongming Wang;Zhi Zhang;Ping Zhang","doi":"10.1109/JSAC.2024.3460030","DOIUrl":"10.1109/JSAC.2024.3460030","url":null,"abstract":"In the visionary ideals of “Internet of Everything” and “Digital Twins”, the future 6G will deeply integrate diverse heterogeneous networks such as satellite and aerial networks to support seamless connectivity and efficient interoperability, also known as space-air-ground integrated networks (SAGIN), in which the grant-free uplink random access based on Slotted ALOHA (S-ALOHA) can reduce access latency and complexity for massive Internet of Things (IoT) devices. However, with the increasing number of IoT users, the collision probability of S-ALOHA escalates and further degrades the system performance. In this paper, we focus on the massive IoT device uplink access in SAGIN aided by high altitude platform stations (HAPS), investigating power allocation for IoT devices to maximize system access capability and spectral efficiency (SE). Specifically, we first optimize 3D deployment of HAPS. Then the resilient massive access (RMA) based on flexible fusion of S-ALOHA and non-orthogonal multiple access methods is proposed. To maximize system SE with device power constraints, we model the sequential decision problem as a Markov decision process and solve it with the Advantage Actor-Critic (A2C) algorithm. Simulation results demonstrate the proposed RMA can significantly improve the IoT terminal successful access probability and the resource scheduling based on A2C also significantly increases the system SE with low complexity.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"297-313"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231222","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.2024.3460061
Anal Paul;Keshav Singh;Aryan Kaushik;Chih-Peng Li;Octavia A. Dobre;Marco Di Renzo;Trung Q. Duong
This work proposes a quantum-aided deep reinforcement learning (DRL) framework designed to enhance the accuracy of direction-of-arrival (DoA) estimation and the efficiency of computational task offloading in integrated sensing and communication systems. Traditional DRL approaches face challenges in handling high-dimensional state spaces and ensuring convergence to optimal policies within complex operational environments. The proposed quantum-aided DRL framework that operates in a military surveillance system exploits quantum computing’s parallel processing capabilities to encode operational states and actions into quantum states, significantly reducing the dimensionality of the decision space. For the very first time in literature, we propose a quantum-enhanced actor-critic method, utilizing quantum circuits for policy representation and optimization. Through comprehensive simulations, we demonstrate that our framework improves DoA estimation accuracy by 91.66% and 82.61% over existing DRL algorithms with faster convergence rate, and effectively manages the trade-off between sensing and communication and by optimizing task offloading decisions under stringent ultra-reliable low-latency communication requirements. Comparative analysis also reveals that our approach reduces the overall task offloading latency by 43.09% and 32.35% compared to the DRL-based deep deterministic policy gradient and proximal policy optimization algorithms, respectively.
{"title":"Quantum-Enhanced DRL Optimization for DoA Estimation and Task Offloading in ISAC Systems","authors":"Anal Paul;Keshav Singh;Aryan Kaushik;Chih-Peng Li;Octavia A. Dobre;Marco Di Renzo;Trung Q. Duong","doi":"10.1109/JSAC.2024.3460061","DOIUrl":"10.1109/JSAC.2024.3460061","url":null,"abstract":"This work proposes a quantum-aided deep reinforcement learning (DRL) framework designed to enhance the accuracy of direction-of-arrival (DoA) estimation and the efficiency of computational task offloading in integrated sensing and communication systems. Traditional DRL approaches face challenges in handling high-dimensional state spaces and ensuring convergence to optimal policies within complex operational environments. The proposed quantum-aided DRL framework that operates in a military surveillance system exploits quantum computing’s parallel processing capabilities to encode operational states and actions into quantum states, significantly reducing the dimensionality of the decision space. For the very first time in literature, we propose a quantum-enhanced actor-critic method, utilizing quantum circuits for policy representation and optimization. Through comprehensive simulations, we demonstrate that our framework improves DoA estimation accuracy by 91.66% and 82.61% over existing DRL algorithms with faster convergence rate, and effectively manages the trade-off between sensing and communication and by optimizing task offloading decisions under stringent ultra-reliable low-latency communication requirements. Comparative analysis also reveals that our approach reduces the overall task offloading latency by 43.09% and 32.35% compared to the DRL-based deep deterministic policy gradient and proximal policy optimization algorithms, respectively.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"364-381"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231223","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 are the key enabler for achieving 6G ubiquitous connectivity. With the rapid progress of small satellite technology and the surging demands on direct-to-satellite services, a global wave of building LEO satellite constellations has been arisen. LEO satellite communications are the typical high mobility scenarios and suffer from severe Doppler effects. To overcome this challenge, orthogonal time frequency space (OTFS)-based LEO satellite communications have recently been studied, which exploit high mobility to obtain delay-Doppler diversity. However, due to limited satellite transmit power and very long propagation distance, the satellite-to-ground (S2G) links are very weak, and also suffer from inter-beam and inter-satellite interference. In this paper, we study coordinated multi-satellite transmission for OTFS-based LEO satellite communications to significantly improve the performance of S2G transmission, through enabling multiple satellites to cooperatively serve ground users. Furthermore, considering different delay and Doppler offsets among cooperative LEO satellites, we propose simultaneous pilots-based aggregate channel estimation (SP-ACE) scheme to improve channel estimation, which aggregately estimates the channels in S2G joint transmission by regarding the channels of all cooperative links as a single channel. Besides integer Doppler, we also consider fractional Doppler and propose three-stage peak-searching correlation (PSC)-based fractional Doppler estimation. Finally, simulations are conducted and the results demonstrate the effectiveness of the proposed coordinated multi-satellite transmission scheme, SP-ACE and three-stage PSC fractional Doppler estimation schemes.
{"title":"Coordinated Multi-Satellite Transmission for OTFS-Based 6G LEO Satellite Communication Systems","authors":"Zhengquan Zhang;Yuchen Wu;Zheng Ma;Xianfu Lei;Lei Lei;Zhiqiang Wei","doi":"10.1109/JSAC.2024.3460108","DOIUrl":"10.1109/JSAC.2024.3460108","url":null,"abstract":"Low Earth orbit (LEO) satellite communications are the key enabler for achieving 6G ubiquitous connectivity. With the rapid progress of small satellite technology and the surging demands on direct-to-satellite services, a global wave of building LEO satellite constellations has been arisen. LEO satellite communications are the typical high mobility scenarios and suffer from severe Doppler effects. To overcome this challenge, orthogonal time frequency space (OTFS)-based LEO satellite communications have recently been studied, which exploit high mobility to obtain delay-Doppler diversity. However, due to limited satellite transmit power and very long propagation distance, the satellite-to-ground (S2G) links are very weak, and also suffer from inter-beam and inter-satellite interference. In this paper, we study coordinated multi-satellite transmission for OTFS-based LEO satellite communications to significantly improve the performance of S2G transmission, through enabling multiple satellites to cooperatively serve ground users. Furthermore, considering different delay and Doppler offsets among cooperative LEO satellites, we propose simultaneous pilots-based aggregate channel estimation (SP-ACE) scheme to improve channel estimation, which aggregately estimates the channels in S2G joint transmission by regarding the channels of all cooperative links as a single channel. Besides integer Doppler, we also consider fractional Doppler and propose three-stage peak-searching correlation (PSC)-based fractional Doppler estimation. Finally, simulations are conducted and the results demonstrate the effectiveness of the proposed coordinated multi-satellite transmission scheme, SP-ACE and three-stage PSC fractional Doppler estimation schemes.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"156-170"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231228","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.2024.3460057
Jiachen Sun;Xu Chen;Chunxiao Jiang;Song Guo
With the rapid development of on-board computing technology, on-orbit information processing has become a new direction for reducing service response delays and improving the quality of space-based information services. Especially in space-air–ground integrated applications in 6G networks, remote sensing image processing tasks are highly important because of their critical role in applications such as environmental monitoring and public safety. However, the fluctuations in data volume due to significant scene differences, along with the limitations in individual satellite capabilities caused by size and power constraints, present new challenges for on-orbit image processing. To address these challenges, we model a data-driven on-orbit resource scheduling problem for space-air-ground integrated networks based on distributionally robust optimization, aiming to minimize the average image processing delay. We first construct an ambiguity set based on the Wasserstein distance and the historical distribution of image data, which helps transform the original upper-bound expectation problem into an explicitly expressed mixed-integer nonlinear (MINLP) problem. Furthermore, to reduce complexity and expedite the solution process, we decouple the MINLP problem into three subproblems using the block coordinate descent method and designed an iterative solving algorithm. The numerical results demonstrate that our proposed method achieves better fitting accuracy than traditional methods and reduces the average image processing delay.
{"title":"Distributionally Robust Optimization of On-Orbit Resource Scheduling for Remote Sensing in Space-Air-Ground Integrated 6G Networks","authors":"Jiachen Sun;Xu Chen;Chunxiao Jiang;Song Guo","doi":"10.1109/JSAC.2024.3460057","DOIUrl":"10.1109/JSAC.2024.3460057","url":null,"abstract":"With the rapid development of on-board computing technology, on-orbit information processing has become a new direction for reducing service response delays and improving the quality of space-based information services. Especially in space-air–ground integrated applications in 6G networks, remote sensing image processing tasks are highly important because of their critical role in applications such as environmental monitoring and public safety. However, the fluctuations in data volume due to significant scene differences, along with the limitations in individual satellite capabilities caused by size and power constraints, present new challenges for on-orbit image processing. To address these challenges, we model a data-driven on-orbit resource scheduling problem for space-air-ground integrated networks based on distributionally robust optimization, aiming to minimize the average image processing delay. We first construct an ambiguity set based on the Wasserstein distance and the historical distribution of image data, which helps transform the original upper-bound expectation problem into an explicitly expressed mixed-integer nonlinear (MINLP) problem. Furthermore, to reduce complexity and expedite the solution process, we decouple the MINLP problem into three subproblems using the block coordinate descent method and designed an iterative solving algorithm. The numerical results demonstrate that our proposed method achieves better fitting accuracy than traditional methods and reduces the average image processing delay.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 1","pages":"382-395"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231232","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}
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