This paper considers an aerial edge computing (AEC) paradigm, where unmanned aerial vehicles (UAVs) are deployed as edge servers to provide computing services to mobile devices (MDs) in remote and hard-to-reach regions. Although offloading compute-intensive tasks to edge servers can improve the quality of experience and reduce energy consumption of MDs, it poses challenges in efficient system management due to the limitation in energy and bandwidth resources of the servers. To address this issue, we propose a green AEC architecture where servers can harvest energy from renewable resources such as solar power. We formulate an optimization problem that aims to maximize the MDs’ long-term satisfaction while guaranteeing sustainable operation for the UAVs by controlling computation offloading and resource allocation decisions. We leverage the Lyapunov optimization theory to handle long-term energy constraints in the formulated problem and then develop a deep deterministic policy gradient (DDPG) algorithm to solve the problem considering a dynamic network environment. We also integrate prioritized experience replay and weighted importance sampling techniques into the DDPG algorithm to improve learning performance. Experimental results demonstrate that the proposed solution achieves high performance and adapts well to network variations.
{"title":"Learning-Based QoE Optimization for Green Edge Computing Networks With Aerial Servers","authors":"Quang Vinh Do;Quoc-Viet Pham;Zhaohui Yang;Won-Joo Hwang","doi":"10.1109/TGCN.2024.3480274","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3480274","url":null,"abstract":"This paper considers an aerial edge computing (AEC) paradigm, where unmanned aerial vehicles (UAVs) are deployed as edge servers to provide computing services to mobile devices (MDs) in remote and hard-to-reach regions. Although offloading compute-intensive tasks to edge servers can improve the quality of experience and reduce energy consumption of MDs, it poses challenges in efficient system management due to the limitation in energy and bandwidth resources of the servers. To address this issue, we propose a green AEC architecture where servers can harvest energy from renewable resources such as solar power. We formulate an optimization problem that aims to maximize the MDs’ long-term satisfaction while guaranteeing sustainable operation for the UAVs by controlling computation offloading and resource allocation decisions. We leverage the Lyapunov optimization theory to handle long-term energy constraints in the formulated problem and then develop a deep deterministic policy gradient (DDPG) algorithm to solve the problem considering a dynamic network environment. We also integrate prioritized experience replay and weighted importance sampling techniques into the DDPG algorithm to improve learning performance. Experimental results demonstrate that the proposed solution achieves high performance and adapts well to network variations.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"1326-1339"},"PeriodicalIF":6.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/TGCN.2024.3479460
Erfan Delfani;George J. Stamatakis;Nikolaos Pappas
In this study, we investigate the optimal transmission policies within an energy harvesting status update system, where the demand for status updates depends on the state of the source. The system monitors a source with two contextual states characterized by a Markovian stochastic process, which can be in either a normal state or an alarm state, with a higher demand for fresh updates when the source is in the alarm state. We propose a metric to capture the freshness of status updates for each state of the stochastic process by introducing two Age of Information (AoI) variables, extending the definition of AoI to account for the state changes of the stochastic process. We formulate the problem as a Markov Decision Process (MDP), utilizing a transition cost function that applies linear and non-linear penalties based on AoI and the state of the stochastic process. Through analytical investigation, we delve into the structure of the optimal transmission policy for the resulting MDP problem. Furthermore, we evaluate the derived policies via numerical results and demonstrate their effectiveness in reserving energy in anticipation of forthcoming alarm states.
在本研究中，我们研究了能量收集状态更新系统中的最优传输策略，其中状态更新的需求取决于源的状态。系统监测具有马尔可夫随机过程特征的两种上下文状态的源，该源可以处于正常状态或警报状态，当源处于警报状态时，对新鲜更新的需求更高。通过引入两个信息年龄（Age of Information, AoI）变量，我们提出了一个度量来捕捉随机过程的每个状态的状态更新的新鲜度，扩展了AoI的定义来解释随机过程的状态变化。我们将该问题表述为马尔可夫决策过程（MDP），利用基于AoI和随机过程状态的应用线性和非线性惩罚的转移成本函数。通过分析研究，我们深入研究了由此产生的MDP问题的最优传输策略的结构。此外，我们通过数值结果评估了导出的策略，并证明了它们在预测即将到来的警报状态时保留能量的有效性。
{"title":"State-Aware Timeliness in Energy Harvesting IoT Systems Monitoring a Markovian Source","authors":"Erfan Delfani;George J. Stamatakis;Nikolaos Pappas","doi":"10.1109/TGCN.2024.3479460","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3479460","url":null,"abstract":"In this study, we investigate the optimal transmission policies within an energy harvesting status update system, where the demand for status updates depends on the state of the source. The system monitors a source with two contextual states characterized by a Markovian stochastic process, which can be in either a normal state or an alarm state, with a higher demand for fresh updates when the source is in the alarm state. We propose a metric to capture the freshness of status updates for each state of the stochastic process by introducing two Age of Information (AoI) variables, extending the definition of AoI to account for the state changes of the stochastic process. We formulate the problem as a Markov Decision Process (MDP), utilizing a transition cost function that applies linear and non-linear penalties based on AoI and the state of the stochastic process. Through analytical investigation, we delve into the structure of the optimal transmission policy for the resulting MDP problem. Furthermore, we evaluate the derived policies via numerical results and demonstrate their effectiveness in reserving energy in anticipation of forthcoming alarm states.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"977-990"},"PeriodicalIF":6.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Simultaneous Wireless Information and Power Transfer (SWIPT) is a unified approach to transfer power and information by exploiting the properties of radio signals. This paper considers a multi-antenna base station, multiple energy harvesting receivers (EHRs), and legitimate receivers in the presence of multiple multi-antenna eavesdroppers. A resource allocation problem of maximizing the harvested energy available at the EHR side in the presence of secrecy capacity and quality of service constraints is formulated for analysis. The transmit power, secrecy, and total transmit power requirements are used to formulate the constraints. We then propose neural network methods that use the channel state information data and constraint characteristics to obtain novel activation functions that can be used to predict beamforming vectors. Simulation results show that the proposed activation functions predict beamforming vectors to achieve better harvested energy and secrecy capacity.
{"title":"Improved Deep Learning Methods for Beamforming in Secure SWIPT Networks","authors":"Vieeralingaam Ganapathy;Ramanathan Ramachandran;Tomoaki Ohtsuki","doi":"10.1109/TGCN.2024.3478736","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3478736","url":null,"abstract":"Simultaneous Wireless Information and Power Transfer (SWIPT) is a unified approach to transfer power and information by exploiting the properties of radio signals. This paper considers a multi-antenna base station, multiple energy harvesting receivers (EHRs), and legitimate receivers in the presence of multiple multi-antenna eavesdroppers. A resource allocation problem of maximizing the harvested energy available at the EHR side in the presence of secrecy capacity and quality of service constraints is formulated for analysis. The transmit power, secrecy, and total transmit power requirements are used to formulate the constraints. We then propose neural network methods that use the channel state information data and constraint characteristics to obtain novel activation functions that can be used to predict beamforming vectors. Simulation results show that the proposed activation functions predict beamforming vectors to achieve better harvested energy and secrecy capacity.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"962-976"},"PeriodicalIF":6.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-30DOI: 10.1109/TGCN.2024.3471078
D. Jim Solomon Raja;N. Hemavathi;R. Sriranjani;Parvathy Arulmozhi
In advanced metering infrastructure, the bidirectional communication over insecure public channels between smart meters and the data concentrator is vulnerable to man-in-the-middle attack. Existing schemes often exhibit insecurities or inefficiencies, necessitating an efficient and secure authentication scheme for advanced metering infrastructure. To address these challenges, this proposal presents an energy-efficient and secured behavioral biometrics based certificateless authentication and key agreement protocol using elliptic curve cryptography. The protocol includes encrypted registration, behavioral biometrics-based identity validation, identity binding through behavioral biometrics and implicit authentication of entities’ public key to mitigate man-in-the-middle attack and its variants. The proposed protocol for real-time advanced metering infrastructure testbed is implemented using Python and security is analyzed through Pro-Verif tool. The performance evaluation confirms that the proposed scheme meets security requirements while minimizing computation and communication costs in terms of energy, thereby showcasing its superiority among counterparts and its suitability for resource-constrained smart grid environments.
{"title":"Mitigation of Man-in-the-Middle Attack in Advanced Metering Infrastructure Through Behavioral Biometrics-Based Elliptic Curve Cryptography","authors":"D. Jim Solomon Raja;N. Hemavathi;R. Sriranjani;Parvathy Arulmozhi","doi":"10.1109/TGCN.2024.3471078","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3471078","url":null,"abstract":"In advanced metering infrastructure, the bidirectional communication over insecure public channels between smart meters and the data concentrator is vulnerable to man-in-the-middle attack. Existing schemes often exhibit insecurities or inefficiencies, necessitating an efficient and secure authentication scheme for advanced metering infrastructure. To address these challenges, this proposal presents an energy-efficient and secured behavioral biometrics based certificateless authentication and key agreement protocol using elliptic curve cryptography. The protocol includes encrypted registration, behavioral biometrics-based identity validation, identity binding through behavioral biometrics and implicit authentication of entities’ public key to mitigate man-in-the-middle attack and its variants. The proposed protocol for real-time advanced metering infrastructure testbed is implemented using Python and security is analyzed through Pro-Verif tool. The performance evaluation confirms that the proposed scheme meets security requirements while minimizing computation and communication costs in terms of energy, thereby showcasing its superiority among counterparts and its suitability for resource-constrained smart grid environments.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"778-788"},"PeriodicalIF":6.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-25DOI: 10.1109/TGCN.2024.3467267
Xiaonan Wang;Yimin Lu
In post-disaster areas where infrastructures are unavailable, mobile robots can construct an infrastructure-free Robotic Mobile Ad hoc NETworking (RMANET) to continuously provide real-time data related to disaster scenes for timely monitoring. The traditional host-centric communication model employed by MANET cannot work for continuous delivery of real-time location-related contents for disaster scenes because a node can only provide data related to its location rather than a target location. Hence, we are motivated to leverage the Information-Centric Networking (ICN) to deliver continuous data related to different target locations. Based on the idea, we propose an information-centric RMANET continuous data routing and delivery framework, aiming to continuously and rapidly access real-time post-disaster data. The solution leverages robot attributes to explore multi-routes towards different target locations, and reuses the best route to continuously forward location-related data in unicast to consumers. The experimental results verify the feasibility and advances of the proposal.
{"title":"Information-Centric Robotic Ad Hoc Networking-Based Continuous Data Routing and Delivery for Disaster Scenes","authors":"Xiaonan Wang;Yimin Lu","doi":"10.1109/TGCN.2024.3467267","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3467267","url":null,"abstract":"In post-disaster areas where infrastructures are unavailable, mobile robots can construct an infrastructure-free Robotic Mobile Ad hoc NETworking (RMANET) to continuously provide real-time data related to disaster scenes for timely monitoring. The traditional host-centric communication model employed by MANET cannot work for continuous delivery of real-time location-related contents for disaster scenes because a node can only provide data related to its location rather than a target location. Hence, we are motivated to leverage the Information-Centric Networking (ICN) to deliver continuous data related to different target locations. Based on the idea, we propose an information-centric RMANET continuous data routing and delivery framework, aiming to continuously and rapidly access real-time post-disaster data. The solution leverages robot attributes to explore multi-routes towards different target locations, and reuses the best route to continuously forward location-related data in unicast to consumers. The experimental results verify the feasibility and advances of the proposal.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"768-777"},"PeriodicalIF":6.7,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1109/TGCN.2024.3465877
Xinyu Fan;Jie Hu;Kun Yang
Radio-frequency (RF) based Wireless Energy Transfer (WET) enables devices to avoid wired power-supply and battery replacements. The ubiquitous availability of RF energy elevates “energy self-sustainability” as a pivotal goal for wireless sensor networks. However, for RF energy harvesting networks, the term “energy self-sustainability” lacks a precise mathematical characterization. This paper presents a robust mathematical definition for energy self-sustainability within integrated data and energy networks. By utilizing Martingale theory, we develop a mathematical framework that determines the energy self-sustainability, by acquiring the stochastic properties of energy harvesting and consumption processes. The fundamental paradigm of utilizing this framework to derive energy self-sustainability is demonstrated. In-depth explorations have been conducted on the diverse stochastic characteristics of energy harvesting and consuming processes. Additionally, this study delves into the anticipated uninterrupted operating duration and associated energy expectation. Monte-Carlo simulations confirm the precision of our theoretical evaluations. By analyzing the correlation between harvested and consumed energy in the context of varied energy self-sustainability requirements, this paper provides design guidance for energy transmitters and batteryless wireless devices.
{"title":"Martingale Theory-Based Definition and Analysis of Energy Self-Sustainability in Batteryless Internet of Things","authors":"Xinyu Fan;Jie Hu;Kun Yang","doi":"10.1109/TGCN.2024.3465877","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3465877","url":null,"abstract":"Radio-frequency (RF) based Wireless Energy Transfer (WET) enables devices to avoid wired power-supply and battery replacements. The ubiquitous availability of RF energy elevates “energy self-sustainability” as a pivotal goal for wireless sensor networks. However, for RF energy harvesting networks, the term “energy self-sustainability” lacks a precise mathematical characterization. This paper presents a robust mathematical definition for energy self-sustainability within integrated data and energy networks. By utilizing Martingale theory, we develop a mathematical framework that determines the energy self-sustainability, by acquiring the stochastic properties of energy harvesting and consumption processes. The fundamental paradigm of utilizing this framework to derive energy self-sustainability is demonstrated. In-depth explorations have been conducted on the diverse stochastic characteristics of energy harvesting and consuming processes. Additionally, this study delves into the anticipated uninterrupted operating duration and associated energy expectation. Monte-Carlo simulations confirm the precision of our theoretical evaluations. By analyzing the correlation between harvested and consumed energy in the context of varied energy self-sustainability requirements, this paper provides design guidance for energy transmitters and batteryless wireless devices.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"756-767"},"PeriodicalIF":6.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we investigate the secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted down link multiple-input single-output (MISO) wireless network. The secrecy rate is maximized by the joint design of the transmit beamforming, the transmission and reflection coefficients of the STAR-RIS, while satisfying the electromagnetic property of the STAR-RIS and transmit power limit of the base station. Since this communication network is in a dynamic environment, the optimization problem is non-convex and mathematically difficult to solve. To address this issue, two deep reinforcement learning (DRL)-based algorithms, namely soft actor-critic (SAC) algorithm and soft actor-critic based on loss-adjusted approximate actor prioritized experience replay (L3APER-SAC) are proposed to obtain the maximum reward by constantly interacting with and learning from the dynamic environment. Moreover, for the L3APER-SAC algorithm, to achieve higher performance and stability, we introduce two experience replay buffers—one is regular experience replay and the other is prioritized experience replay. Simulation results comprehensively assess the performance of two DRL algorithms and indicate that both proposed algorithms outperform benchmark approaches. Particularly, L3APER-SAC, exhibits superior performance, albeit with an associated increase in computational complexity.
{"title":"STAR-RIS Assisted Secrecy Communication With Deep Reinforcement Learning","authors":"Miao Zhang;Xuran Ding;Yanqun Tang;Shixun Wu;Kai Xu","doi":"10.1109/TGCN.2024.3466189","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3466189","url":null,"abstract":"In this paper, we investigate the secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted down link multiple-input single-output (MISO) wireless network. The secrecy rate is maximized by the joint design of the transmit beamforming, the transmission and reflection coefficients of the STAR-RIS, while satisfying the electromagnetic property of the STAR-RIS and transmit power limit of the base station. Since this communication network is in a dynamic environment, the optimization problem is non-convex and mathematically difficult to solve. To address this issue, two deep reinforcement learning (DRL)-based algorithms, namely soft actor-critic (SAC) algorithm and soft actor-critic based on loss-adjusted approximate actor prioritized experience replay (L3APER-SAC) are proposed to obtain the maximum reward by constantly interacting with and learning from the dynamic environment. Moreover, for the L3APER-SAC algorithm, to achieve higher performance and stability, we introduce two experience replay buffers—one is regular experience replay and the other is prioritized experience replay. Simulation results comprehensively assess the performance of two DRL algorithms and indicate that both proposed algorithms outperform benchmark approaches. Particularly, L3APER-SAC, exhibits superior performance, albeit with an associated increase in computational complexity.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 2","pages":"739-753"},"PeriodicalIF":5.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1109/TGCN.2024.3466311
Jibril Abdi Mead;Keshav Singh;Raviteja Allu;Sudip Biswas;Meng-Lin Ku
To enhance system performance, future wireless networks are expected to integrate various unconventional physical layer schemes. This work investigates a reconfigurable intelligent surface (RIS)-aided secure full-duplex (FD) non-orthogonal multiple access (NOMA) system, considering transceiver hardware impairments (HWI). Our primary objective is to minimize the total transmit power of the base station (BS) and uplink (UL) users by designing an optimal power allocation scheme and passive beamforming at the RIS with HWIs. This design ensures compliance with minimum rate requirements despite significant cross-interference and unit-modulus constraints for passive beamforming at the RIS. We propose an iterative algorithm that optimizes the transmit power at the BS and UL users and the passive beamforming at the RIS. Given the non-convex nature of the problem, we employ generalized convex approximations to achieve a near-optimal solution. Simulation results demonstrate the superiority of the proposed secure RIS-aided FD-NOMA system over conventional half-duplex (HD), orthogonal multiple access (OMA), and space division multiple access (SDMA) systems in terms of average total transmit power. Additionally, we analyze the impact of various key system parameters, such as the number of UL and downlink users, the number of RIS elements, noise power, and residual self-interference on system performance.
{"title":"Hardware Impairment Aware Transmit Power Minimization for Secure RIS-Aided Full-Duplex NOMA Communications","authors":"Jibril Abdi Mead;Keshav Singh;Raviteja Allu;Sudip Biswas;Meng-Lin Ku","doi":"10.1109/TGCN.2024.3466311","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3466311","url":null,"abstract":"To enhance system performance, future wireless networks are expected to integrate various unconventional physical layer schemes. This work investigates a reconfigurable intelligent surface (RIS)-aided secure full-duplex (FD) non-orthogonal multiple access (NOMA) system, considering transceiver hardware impairments (HWI). Our primary objective is to minimize the total transmit power of the base station (BS) and uplink (UL) users by designing an optimal power allocation scheme and passive beamforming at the RIS with HWIs. This design ensures compliance with minimum rate requirements despite significant cross-interference and unit-modulus constraints for passive beamforming at the RIS. We propose an iterative algorithm that optimizes the transmit power at the BS and UL users and the passive beamforming at the RIS. Given the non-convex nature of the problem, we employ generalized convex approximations to achieve a near-optimal solution. Simulation results demonstrate the superiority of the proposed secure RIS-aided FD-NOMA system over conventional half-duplex (HD), orthogonal multiple access (OMA), and space division multiple access (SDMA) systems in terms of average total transmit power. Additionally, we analyze the impact of various key system parameters, such as the number of UL and downlink users, the number of RIS elements, noise power, and residual self-interference on system performance.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"1153-1166"},"PeriodicalIF":6.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1109/TGCN.2024.3466469
Muhammad Asif;Xu Bao;Zain Ali;Asim Ihsan;Manzoor Ahmed;Xingwang Li
This manuscript introduces an efficient resource allocation framework for a transmissive reconfigurable intelligent surface (T-RIS) assisted Low Earth Orbit (LEO) satellite non-orthogonal multiple access (NOMA) system under residual hardware impairments (RHIs) resulting from imperfect transceiver hardware design. In particular, the goal is to maximize the sum rate of the considered multi-cluster based T-RIS assisted LEO-satellite NOMA network. This is achieved by optimizing the power-allocation of users, time-allocation for each cluster, and transmit passive beamforming of T-RIS node, while adhering to quality-of-service (QoS), time-allocation, and power-budget constraints. Moreover, the presented optimization algorithm tackles the considered highly non-convex problem in three steps. Firstly, the power-allocation of NOMA users is computed by exploiting Lagrange duality and sub-gradient methods. Secondly, the time-allocation for each cluster is determined based on the interior point method by exploiting Mosek-assisted CVX toolbox. Thirdly, the computation of passive beamforming employs semi-definite programming (SDP) and successive convex approximation (SCA) techniques, where a rank-1 solution is achieved by incorporating the Gaussian randomization method. Finally, numerical simulations affirm the effectiveness of the proposed optimization strategy, highlighting its superior performance in comparison to benchmark techniques. Notably, it proves to be highly effective in achieving fast convergence with only a few iterations.
{"title":"Transmissive RIS-Empowered LEO-Satellite Communications With Hybrid-NOMA Under Residual Hardware Impairments","authors":"Muhammad Asif;Xu Bao;Zain Ali;Asim Ihsan;Manzoor Ahmed;Xingwang Li","doi":"10.1109/TGCN.2024.3466469","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3466469","url":null,"abstract":"This manuscript introduces an efficient resource allocation framework for a transmissive reconfigurable intelligent surface (T-RIS) assisted Low Earth Orbit (LEO) satellite non-orthogonal multiple access (NOMA) system under residual hardware impairments (RHIs) resulting from imperfect transceiver hardware design. In particular, the goal is to maximize the sum rate of the considered multi-cluster based T-RIS assisted LEO-satellite NOMA network. This is achieved by optimizing the power-allocation of users, time-allocation for each cluster, and transmit passive beamforming of T-RIS node, while adhering to quality-of-service (QoS), time-allocation, and power-budget constraints. Moreover, the presented optimization algorithm tackles the considered highly non-convex problem in three steps. Firstly, the power-allocation of NOMA users is computed by exploiting Lagrange duality and sub-gradient methods. Secondly, the time-allocation for each cluster is determined based on the interior point method by exploiting Mosek-assisted CVX toolbox. Thirdly, the computation of passive beamforming employs semi-definite programming (SDP) and successive convex approximation (SCA) techniques, where a rank-1 solution is achieved by incorporating the Gaussian randomization method. Finally, numerical simulations affirm the effectiveness of the proposed optimization strategy, highlighting its superior performance in comparison to benchmark techniques. Notably, it proves to be highly effective in achieving fast convergence with only a few iterations.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"1167-1178"},"PeriodicalIF":6.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1109/TGCN.2024.3466295
Raviteja Allu;Mayur Katwe;Keshav Singh;Simon L. Cotton;Chih-Peng Li;Trung Q. Duong
In this paper, we investigate an unconventional full-duplex (FD) integrated rate-splitting multiple access (RSMA) scheme for improved spectral efficiency (SE) and energy efficiency (EE) performance when compared to the conventional power-domain schemes. In particular, we focus on improving the EE and SE trade-off for the multiple users subject to robust beamforming design and smart inter-user interference mitigation under imperfect channel state information (CSI). We formulate a multi-objective optimization (MOO) problem, specifically aiming to jointly maximize EE and SE within the FD-RSMA system by jointly optimizing the resource allocation subject to the limits on transmit power and minimum rate, under the assumption of a CSI error model with a bound. Initially, the MOO problem is converted into a single objective optimization (SOO) problem using the weighted sum method, with a trade-off parameter. An iterative algorithm is employed, utilizing successive convex approximation and the S-procedure to achieve near-optimal resource allocation for the transformed SOO problem, with a particular emphasis on effective interference management. Simulation results highlight the effectiveness of the FD-RSMA scheme, demonstrating its superiority over the multi-user FD space division multiple access by 16.93 % and non-orthogonal multiple access scheme by 76.04 %.
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