Pub Date : 2024-09-12DOI: 10.1109/JSAC.2024.3459268
Wen-Yu Dong;Shaoshi Yang;Ping Zhang;Sheng Chen
Cooperative satellite-aerial-terrestrial networks (CSATNs), where unmanned aerial vehicles (UAVs) are utilized as nomadic aerial relays (A), are highly valuable for many important applications, such as post-disaster urban reconstruction. In this scenario, direct communication between terrestrial terminals (T) and satellites (S) is often unavailable due to poor propagation conditions for satellite signals, and users tend to congregate in regions of finite size. There is a current dearth in the open literature regarding the uplink performance analysis of CSATN operating under the above constraints, and the few contributions on the uplink model terrestrial terminals by a Poisson point process (PPP) relying on the unrealistic assumption of an infinite area. This paper aims to fill the above research gap. First, we propose a stochastic geometry based innovative model to characterize the impact of the finite-size distribution region of terrestrial terminals in the CSATN by jointly using a binomial point process (BPP) and a type-II Matérn hard-core point process (MHCPP). Then, we analyze the relationship between the spatial distribution of the coverage areas of aerial nodes and the finite-size distribution region of terrestrial terminals, thereby deriving the distance distribution of the T-A links. Furthermore, we consider the stochastic nature of the spatial distributions of terrestrial terminals and UAVs, and conduct a thorough analysis of the coverage probability and average ergodic rate of the T-A links under Nakagami fading and the A-S links under shadowed-Rician fading. Finally, the accuracy of our theoretical derivations are confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization for the realistic CSATNs involving nomadic aerial relays and terrestrial terminals confined in a finite-size region.
{"title":"Stochastic Geometry Based Modeling and Analysis of Uplink Cooperative Satellite-Aerial-Terrestrial Networks for Nomadic Communications With Weak Satellite Coverage","authors":"Wen-Yu Dong;Shaoshi Yang;Ping Zhang;Sheng Chen","doi":"10.1109/JSAC.2024.3459268","DOIUrl":"10.1109/JSAC.2024.3459268","url":null,"abstract":"Cooperative satellite-aerial-terrestrial networks (CSATNs), where unmanned aerial vehicles (UAVs) are utilized as nomadic aerial relays (A), are highly valuable for many important applications, such as post-disaster urban reconstruction. In this scenario, direct communication between terrestrial terminals (T) and satellites (S) is often unavailable due to poor propagation conditions for satellite signals, and users tend to congregate in regions of finite size. There is a current dearth in the open literature regarding the uplink performance analysis of CSATN operating under the above constraints, and the few contributions on the uplink model terrestrial terminals by a Poisson point process (PPP) relying on the unrealistic assumption of an infinite area. This paper aims to fill the above research gap. First, we propose a stochastic geometry based innovative model to characterize the impact of the finite-size distribution region of terrestrial terminals in the CSATN by jointly using a binomial point process (BPP) and a type-II Matérn hard-core point process (MHCPP). Then, we analyze the relationship between the spatial distribution of the coverage areas of aerial nodes and the finite-size distribution region of terrestrial terminals, thereby deriving the distance distribution of the T-A links. Furthermore, we consider the stochastic nature of the spatial distributions of terrestrial terminals and UAVs, and conduct a thorough analysis of the coverage probability and average ergodic rate of the T-A links under Nakagami fading and the A-S links under shadowed-Rician fading. Finally, the accuracy of our theoretical derivations are confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization for the realistic CSATNs involving nomadic aerial relays and terrestrial terminals confined in a finite-size region.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3428-3444"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174755","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.2024.3459089
Zicun Wang;Lin Zhang;Daquan Feng;Gang Wu;Lin Yang
In space-air-ground integrated networks (SAGINs), the global energy efficiency (GEE) is a crucial metric for balancing the network throughput and energy consumption, and the maximization of GEE requires the optimizations of both user association and power allocation. Most existing methods optimize user association and power allocation separately or successively, relying on instantaneous non-local channel state information (CSI) exchanges. Nevertheless, both the separate and successive methods may fail to find the jointly optimal solution, and acquiring the instantaneous non-local CSI across the SAGINs is challenging due to the long communication distances between the access points (APs) and users. To address these issues, we leverage cloud-edge collaborations and propose an online delayed-interaction collaborative-learning independent-decision multi-agent DRL (DICLID-MADRL) algorithm. With the proposed algorithm, each AP can independently select users and configure transmit power with only local information to enhance GEE. Simulation results demonstrate that the proposed algorithm achieves a higher GEE with reduced time complexity compared to the state of the arts.
{"title":"Intelligent Cloud-Edge Collaborations for Energy-Efficient User Association and Power Allocation in Space-Air-Ground Integrated Networks","authors":"Zicun Wang;Lin Zhang;Daquan Feng;Gang Wu;Lin Yang","doi":"10.1109/JSAC.2024.3459089","DOIUrl":"10.1109/JSAC.2024.3459089","url":null,"abstract":"In space-air-ground integrated networks (SAGINs), the global energy efficiency (GEE) is a crucial metric for balancing the network throughput and energy consumption, and the maximization of GEE requires the optimizations of both user association and power allocation. Most existing methods optimize user association and power allocation separately or successively, relying on instantaneous non-local channel state information (CSI) exchanges. Nevertheless, both the separate and successive methods may fail to find the jointly optimal solution, and acquiring the instantaneous non-local CSI across the SAGINs is challenging due to the long communication distances between the access points (APs) and users. To address these issues, we leverage cloud-edge collaborations and propose an online delayed-interaction collaborative-learning independent-decision multi-agent DRL (DICLID-MADRL) algorithm. With the proposed algorithm, each AP can independently select users and configure transmit power with only local information to enhance GEE. Simulation results demonstrate that the proposed algorithm achieves a higher GEE with reduced time complexity compared to the state of the arts.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3659-3673"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174761","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, the energy consumption (EC) optimization of an aerial high altitude platform station (HAPS) aided mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) in the presence of imperfect successive interference cancellation is studied. Specifically, joint design schemes of the resource allocation (RA) and the two-dimensional (2D) horizontal position are proposed to minimize the sum EC subject to the different constraint conditions. In particular, we jointly optimize the receive beamforming (BF), the power allocation (PA), HAPS position, the local computation resource, the computation task offload coefficient, and the computation resource allocated for each user via the block coordinate descent method. Namely, given the other optimization parameters, we first optimize a 2D position of HAPS. Then, given the 2D position, by introducing the auxiliary variables, a joint design of BF, PA, offload coefficient and computation resource is solved by an efficient iteration algorithm based on the successive convex approximation method. Additionally, a suboptimal joint design scheme is also developed to lower the complexity. Simulation results show that the proposed two design schemes of the joint RA and position are effective in reducing the EC, and they have a lower EC when compared to benchmark schemes.
{"title":"Joint Resource Allocations for Energy Consumption Optimization in HAPS-Aided MEC-NOMA Systems","authors":"Xiangbin Yu;Xinyi Zhang;Yun Rui;Kezhi Wang;Xiaoyu Dang;Mohsen Guizani","doi":"10.1109/JSAC.2024.3459084","DOIUrl":"10.1109/JSAC.2024.3459084","url":null,"abstract":"In this paper, the energy consumption (EC) optimization of an aerial high altitude platform station (HAPS) aided mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) in the presence of imperfect successive interference cancellation is studied. Specifically, joint design schemes of the resource allocation (RA) and the two-dimensional (2D) horizontal position are proposed to minimize the sum EC subject to the different constraint conditions. In particular, we jointly optimize the receive beamforming (BF), the power allocation (PA), HAPS position, the local computation resource, the computation task offload coefficient, and the computation resource allocated for each user via the block coordinate descent method. Namely, given the other optimization parameters, we first optimize a 2D position of HAPS. Then, given the 2D position, by introducing the auxiliary variables, a joint design of BF, PA, offload coefficient and computation resource is solved by an efficient iteration algorithm based on the successive convex approximation method. Additionally, a suboptimal joint design scheme is also developed to lower the complexity. Simulation results show that the proposed two design schemes of the joint RA and position are effective in reducing the EC, and they have a lower EC when compared to benchmark schemes.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3632-3646"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174759","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 integrated ground-air-space (IGAS) wireless networks, numerous services require sensing knowledge including location, angle, distance information, etc., which usually can be acquired during the beam training stage. On the other hand, IGAS networks employ large-scale antenna arrays to mitigate obstacle occlusion and path loss. However, large-scale arrays generate pencil-shaped beams, which necessitate a higher number of training beams to cover the desired space. These factors motivate our investigation into the IGAS beam training problem to achieve effective sensing services. To address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme. Specifically, we first construct an IGAS single-beam training codebook for the uniform planar arrays. Then, the hash functions are chosen independently to construct the multi-arm beam training codebooks for each AP. All APs traverse the predefined multi-arm beam training codeword simultaneously and the multi-AP superimposed signals at the user are recorded. Finally, the soft decision and voting methods are applied to obtain the correctly aligned beams only based on the signal powers. In addition, we logically prove that the traversal complexity is at the logarithmic level. Simulation results show that our proposed IGAS HMB training method can achieve 96.4% identification accuracy of the exhaustive beam training method and greatly reduce the training overhead.
{"title":"Hashing Beam Training for Integrated Ground-Air-Space Wireless Networks","authors":"Yuan Xu;Chongwen Huang;Li Wei;Zhaohui Yang;Ahmed Al Hammadi;Jun Yang;Zhaoyang Zhang;Chau Yuen;Mérouane Debbah","doi":"10.1109/JSAC.2024.3459088","DOIUrl":"10.1109/JSAC.2024.3459088","url":null,"abstract":"In integrated ground-air-space (IGAS) wireless networks, numerous services require sensing knowledge including location, angle, distance information, etc., which usually can be acquired during the beam training stage. On the other hand, IGAS networks employ large-scale antenna arrays to mitigate obstacle occlusion and path loss. However, large-scale arrays generate pencil-shaped beams, which necessitate a higher number of training beams to cover the desired space. These factors motivate our investigation into the IGAS beam training problem to achieve effective sensing services. To address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme. Specifically, we first construct an IGAS single-beam training codebook for the uniform planar arrays. Then, the hash functions are chosen independently to construct the multi-arm beam training codebooks for each AP. All APs traverse the predefined multi-arm beam training codeword simultaneously and the multi-AP superimposed signals at the user are recorded. Finally, the soft decision and voting methods are applied to obtain the correctly aligned beams only based on the signal powers. In addition, we logically prove that the traversal complexity is at the logarithmic level. Simulation results show that our proposed IGAS HMB training method can achieve 96.4% identification accuracy of the exhaustive beam training method and greatly reduce the training overhead.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3477-3489"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174762","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.2024.3459037
Ruichen Zhang;Hongyang Du;Yinqiu Liu;Dusit Niyato;Jiawen Kang;Zehui Xiong;Abbas Jamalipour;Dong In Kim
In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregate them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated.
{"title":"Generative AI Agents With Large Language Model for Satellite Networks via a Mixture of Experts Transmission","authors":"Ruichen Zhang;Hongyang Du;Yinqiu Liu;Dusit Niyato;Jiawen Kang;Zehui Xiong;Abbas Jamalipour;Dong In Kim","doi":"10.1109/JSAC.2024.3459037","DOIUrl":"10.1109/JSAC.2024.3459037","url":null,"abstract":"In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregate them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3581-3596"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174767","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.2024.3459090
Dong-Jun Han;Wenzhi Fang;Seyyedali Hosseinalipour;Mung Chiang;Christopher G. Brinton
Devices located in remote regions often lack coverage from well-developed terrestrial communication infrastructure. This not only prevents them from experiencing high quality communication services but also hinders the delivery of machine learning services in remote regions. In this paper, we propose a new federated learning (FL) methodology tailored to space-air-ground integrated networks (SAGINs) to tackle this issue. Our approach strategically leverages the nodes within space and air layers as both 1) edge computing units and 2) model aggregators during the FL process, addressing the challenges that arise from the limited computation powers of ground devices and the absence of terrestrial base stations in the target region. The key idea behind our methodology is the adaptive data offloading and handover procedures that incorporate various network dynamics in SAGINs, including the mobility, heterogeneous computation powers, and inconsistent coverage times of incoming satellites. We analyze the latency of our scheme and develop an adaptive data offloading optimizer, and also characterize the theoretical convergence bound of our proposed algorithm. Experimental results confirm the advantage of our SAGIN-assisted FL methodology in terms of training time and test accuracy compared with various baselines.
{"title":"Orchestrating Federated Learning in Space-Air- Ground Integrated Networks: Adaptive Data Offloading and Seamless Handover","authors":"Dong-Jun Han;Wenzhi Fang;Seyyedali Hosseinalipour;Mung Chiang;Christopher G. Brinton","doi":"10.1109/JSAC.2024.3459090","DOIUrl":"10.1109/JSAC.2024.3459090","url":null,"abstract":"Devices located in remote regions often lack coverage from well-developed terrestrial communication infrastructure. This not only prevents them from experiencing high quality communication services but also hinders the delivery of machine learning services in remote regions. In this paper, we propose a new federated learning (FL) methodology tailored to space-air-ground integrated networks (SAGINs) to tackle this issue. Our approach strategically leverages the nodes within space and air layers as both 1) edge computing units and 2) model aggregators during the FL process, addressing the challenges that arise from the limited computation powers of ground devices and the absence of terrestrial base stations in the target region. The key idea behind our methodology is the adaptive data offloading and handover procedures that incorporate various network dynamics in SAGINs, including the mobility, heterogeneous computation powers, and inconsistent coverage times of incoming satellites. We analyze the latency of our scheme and develop an adaptive data offloading optimizer, and also characterize the theoretical convergence bound of our proposed algorithm. Experimental results confirm the advantage of our SAGIN-assisted FL methodology in terms of training time and test accuracy compared with various baselines.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3505-3520"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174796","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.2024.3459078
Gabriel Maiolini Capez;Mauricio A. Cáceres;Roberto Armellin;Chris P. Bridges;Juan A. Fraire;Stefan Frey;Roberto Garello
This paper presents a framework for integrating Low-Earth Orbit (LEO) platforms with Non-Terrestrial Networks (NTNs) in the emerging 6G communication landscape. Our work applies the Mega-Constellation Services in Space (MCSS) paradigm, leveraging LEO mega-constellations’ expansive coverage and capacity, designed initially for terrestrial devices, to serve platforms in lower LEO orbits. Results show that this approach overcomes the limitation of sporadic and time-bound satellite communication links, a challenge not fully resolved by available Ground Station Networks and Data Relay Systems. We contribute three key elements: (i) a detailed MCSS evaluation framework employing Monte Carlo simulations to assess space user links and distributions; (ii) a novel Space User Terminal (SUT) design optimized for MCSS, using different configurations and 5G New Radio Adaptive Coding and Modulation; (iii) extensive results demonstrating MCSS’s substantial improvement over existing Ground Station Networks and Data Relay Systems, motivating its role in the upcoming 6G NTNs. The space terminal, incorporating a multi-system, multi-orbit, and software-defined architecture, can handle Terabit-scale daily data volumes and minute-scale latencies. It offers a compact, power-efficient solution for properly integrating LEO platforms as space internet nodes.
{"title":"On the Use of Mega Constellation Services in Space: Integrating LEO Platforms Into 6G Non-Terrestrial Networks","authors":"Gabriel Maiolini Capez;Mauricio A. Cáceres;Roberto Armellin;Chris P. Bridges;Juan A. Fraire;Stefan Frey;Roberto Garello","doi":"10.1109/JSAC.2024.3459078","DOIUrl":"10.1109/JSAC.2024.3459078","url":null,"abstract":"This paper presents a framework for integrating Low-Earth Orbit (LEO) platforms with Non-Terrestrial Networks (NTNs) in the emerging 6G communication landscape. Our work applies the Mega-Constellation Services in Space (MCSS) paradigm, leveraging LEO mega-constellations’ expansive coverage and capacity, designed initially for terrestrial devices, to serve platforms in lower LEO orbits. Results show that this approach overcomes the limitation of sporadic and time-bound satellite communication links, a challenge not fully resolved by available Ground Station Networks and Data Relay Systems. We contribute three key elements: (i) a detailed MCSS evaluation framework employing Monte Carlo simulations to assess space user links and distributions; (ii) a novel Space User Terminal (SUT) design optimized for MCSS, using different configurations and 5G New Radio Adaptive Coding and Modulation; (iii) extensive results demonstrating MCSS’s substantial improvement over existing Ground Station Networks and Data Relay Systems, motivating its role in the upcoming 6G NTNs. The space terminal, incorporating a multi-system, multi-orbit, and software-defined architecture, can handle Terabit-scale daily data volumes and minute-scale latencies. It offers a compact, power-efficient solution for properly integrating LEO platforms as space internet nodes.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3490-3504"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174754","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.2024.3459029
Jiahui Li;Geng Sun;Qingqing Wu;Dusit Niyato;Jiawen Kang;Abbas Jamalipour;Victor C. M. Leung
Low Earth Orbit (LEO) satellites have emerged as crucial enablers of direct connections with remote terrestrial terminals. However, energy limitations and insufficient antenna capabilities at the terminals often hamper these connections, resulting in inefficient communications and frequent ping-pong handovers. This paper proposes a Distributed Collaborative Beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. Specifically, DCB treats the terminals that are unable to establish efficient direct connections with the LEO satellites as distributed antennas, forming a virtual antenna array to enhance the terminal-to-satellite uplink achievable rates and durations. However, such systems need multiple trade-off policies that jointly balance the terminal-satellite uplink achievable rate, energy consumption of terminals, and satellite switching frequency to satisfy the scenario requirement changes. Thus, we formulate a long-term multi-objective optimization problem to optimize these goals simultaneously. To address availability in different terminal cluster scales, we reformulate this problem into an action space-reduced and universal Multi-Objective Markov Decision Process (MOMDP). Then, we propose an Evolutionary Multi-Objective Deep Reinforcement Learning (EMODRL) algorithm to obtain multiple policies, in which the low-value actions are masked to speed up the training process. Simulation results show that DCB enables terminals that cannot reach the uplink achievable rate threshold to achieve efficient direct uplink transmission. Moreover, the proposed algorithm outmatches various baselines and saves 30% handover frequency with a similar uplink achievable rate compared with the rate greedy method, which thus reveals that the proposed method is an effective solution for enabling direct ground-space communications.
{"title":"Collaborative Ground-Space Communications via Evolutionary Multi-Objective Deep Reinforcement Learning","authors":"Jiahui Li;Geng Sun;Qingqing Wu;Dusit Niyato;Jiawen Kang;Abbas Jamalipour;Victor C. M. Leung","doi":"10.1109/JSAC.2024.3459029","DOIUrl":"10.1109/JSAC.2024.3459029","url":null,"abstract":"Low Earth Orbit (LEO) satellites have emerged as crucial enablers of direct connections with remote terrestrial terminals. However, energy limitations and insufficient antenna capabilities at the terminals often hamper these connections, resulting in inefficient communications and frequent ping-pong handovers. This paper proposes a Distributed Collaborative Beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. Specifically, DCB treats the terminals that are unable to establish efficient direct connections with the LEO satellites as distributed antennas, forming a virtual antenna array to enhance the terminal-to-satellite uplink achievable rates and durations. However, such systems need multiple trade-off policies that jointly balance the terminal-satellite uplink achievable rate, energy consumption of terminals, and satellite switching frequency to satisfy the scenario requirement changes. Thus, we formulate a long-term multi-objective optimization problem to optimize these goals simultaneously. To address availability in different terminal cluster scales, we reformulate this problem into an action space-reduced and universal Multi-Objective Markov Decision Process (MOMDP). Then, we propose an Evolutionary Multi-Objective Deep Reinforcement Learning (EMODRL) algorithm to obtain multiple policies, in which the low-value actions are masked to speed up the training process. Simulation results show that DCB enables terminals that cannot reach the uplink achievable rate threshold to achieve efficient direct uplink transmission. Moreover, the proposed algorithm outmatches various baselines and saves 30% handover frequency with a similar uplink achievable rate compared with the rate greedy method, which thus reveals that the proposed method is an effective solution for enabling direct ground-space communications.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3395-3411"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174797","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.2024.3459026
Sun Mao;Lei Liu;Xiangwang Hou;Mohammed Atiquzzaman;Kun Yang
To support emerging environmentally-aware intelligent applications, a massive amount of data needs to be collected by sensor devices and transmitted to edge/cloud servers for further computation and analysis. However, due to the high deployment and operational cost, only depending on terrestrial infrastructures cannot satisfy the communication and computation requirements of sensor devices in the unexpected and emergency situations. To tackle this issue, this paper presents a digital twin-enabled space-air-ground integrated sensing, communication and computation network framework, where unmanned aerial vehicles (UAVs) serve as aerial edge access point to provide wireless access and edge computing services for ground sensor devices, and satellites provide access to cloud data center. In order to tackle the complex network environments and coupled multi-dimensional resources, the digital twin technique is utilized to realize real-time network monitoring and resource management, and the mapping deviation is also considered. To realize real-time data sensing and analysis, we formulate a maximum execution latency minimization problem while satisfying the energy consumption constraints and network resource restrictions. Based on the block coordinate descent method and successive convex approximation technique, we develop an efficient algorithm to obtain the optimal sensing time, transmit power, bandwidth allocation, UAV deployment position, data assignment strategy, and computation capability allocation scheme. Simulation results demonstrate that the proposed method outperforms several benchmark methods in terms of maximum execution latency among all sensor devices.
{"title":"Multi-Domain Resource Management for Space–Air–Ground Integrated Sensing, Communication, and Computation Networks","authors":"Sun Mao;Lei Liu;Xiangwang Hou;Mohammed Atiquzzaman;Kun Yang","doi":"10.1109/JSAC.2024.3459026","DOIUrl":"10.1109/JSAC.2024.3459026","url":null,"abstract":"To support emerging environmentally-aware intelligent applications, a massive amount of data needs to be collected by sensor devices and transmitted to edge/cloud servers for further computation and analysis. However, due to the high deployment and operational cost, only depending on terrestrial infrastructures cannot satisfy the communication and computation requirements of sensor devices in the unexpected and emergency situations. To tackle this issue, this paper presents a digital twin-enabled space-air-ground integrated sensing, communication and computation network framework, where unmanned aerial vehicles (UAVs) serve as aerial edge access point to provide wireless access and edge computing services for ground sensor devices, and satellites provide access to cloud data center. In order to tackle the complex network environments and coupled multi-dimensional resources, the digital twin technique is utilized to realize real-time network monitoring and resource management, and the mapping deviation is also considered. To realize real-time data sensing and analysis, we formulate a maximum execution latency minimization problem while satisfying the energy consumption constraints and network resource restrictions. Based on the block coordinate descent method and successive convex approximation technique, we develop an efficient algorithm to obtain the optimal sensing time, transmit power, bandwidth allocation, UAV deployment position, data assignment strategy, and computation capability allocation scheme. Simulation results demonstrate that the proposed method outperforms several benchmark methods in terms of maximum execution latency among all sensor devices.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3380-3394"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174756","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}
Mobile edge computing-assisted integrated aerial-ground network (MEC-IAGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage and computing services. However, the inevitable existences of computation-intensive tasks, uncontrollable propagation environment, and malicious jamming attacks pose three significant bottlenecks for enabling efficient MEC-IAGN. With these focuses, we propose a novel framework of multi-functional reconfigurable intelligent surface (MF-RIS) aided semantic anti-jamming communication and computing in MEC-IAGN. Under this framework, a semantic transceiver exhibits inherent robustness and data compression capability, and MF-RIS can customize the full-space wireless environment by leveraging its signal reflection, refraction, amplification, and energy harvesting functions, thereby achieving substantial global coverage, reliable connectivity, and high-rate computing. Based on our proposed framework, we formulate a semantic computation rate maximization problem considering the impacts of jammer’s channel state information (CSI) imperfection, while maintaining the energy partition constraint for computation offloading decision, semantic similarity requirement, semantic computation rate target, and MF-RIS’s self-sustainability. Then, by transforming the imperfect CSI into a worst-case one by exploiting a discretization method, we propose a fast-converging monotonic optimization algorithm that is combined with decoupling second-order cone programming to obtain a globally optimal solution with fewer feasibility evaluations. Furthermore, to strike a satisfactory tradeoff between performance and computational complexity, we develop a suboptimal generalized power iteration algorithm. Numerical simulations demonstrate the superiority of our proposed framework and algorithms compared to various benchmarks.
{"title":"Multi-Functional RIS-Assisted Semantic Anti-Jamming Communication and Computing in Integrated Aerial-Ground Networks","authors":"Yifu Sun;Zhi Lin;Kang An;Dong Li;Cheng Li;Yonggang Zhu;Derrick Wing Kwan Ng;Naofal Al-Dhahir;Jiangzhou Wang","doi":"10.1109/JSAC.2024.3459028","DOIUrl":"10.1109/JSAC.2024.3459028","url":null,"abstract":"Mobile edge computing-assisted integrated aerial-ground network (MEC-IAGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage and computing services. However, the inevitable existences of computation-intensive tasks, uncontrollable propagation environment, and malicious jamming attacks pose three significant bottlenecks for enabling efficient MEC-IAGN. With these focuses, we propose a novel framework of multi-functional reconfigurable intelligent surface (MF-RIS) aided semantic anti-jamming communication and computing in MEC-IAGN. Under this framework, a semantic transceiver exhibits inherent robustness and data compression capability, and MF-RIS can customize the full-space wireless environment by leveraging its signal reflection, refraction, amplification, and energy harvesting functions, thereby achieving substantial global coverage, reliable connectivity, and high-rate computing. Based on our proposed framework, we formulate a semantic computation rate maximization problem considering the impacts of jammer’s channel state information (CSI) imperfection, while maintaining the energy partition constraint for computation offloading decision, semantic similarity requirement, semantic computation rate target, and MF-RIS’s self-sustainability. Then, by transforming the imperfect CSI into a worst-case one by exploiting a discretization method, we propose a fast-converging monotonic optimization algorithm that is combined with decoupling second-order cone programming to obtain a globally optimal solution with fewer feasibility evaluations. Furthermore, to strike a satisfactory tradeoff between performance and computational complexity, we develop a suboptimal generalized power iteration algorithm. Numerical simulations demonstrate the superiority of our proposed framework and algorithms compared to various benchmarks.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 12","pages":"3597-3617"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174758","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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