Pub Date : 2024-02-09DOI: 10.1109/TGCN.2024.3360673
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/TGCN.2024.3360673","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3360673","url":null,"abstract":"","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10430463","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-09DOI: 10.1109/TGCN.2024.3360671
{"title":"IEEE Transactions on Green Communications and Networking","authors":"","doi":"10.1109/TGCN.2024.3360671","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3360671","url":null,"abstract":"","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10430479","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1109/TGCN.2024.3362866
Kexin Li;Huiqin Du;Si Li
This paper considers a reconfigurable intelligent surface (RIS)-assisted multi-user secrecy transmission in the presence of low-resolution digital-to-analog converters (DACs) at a small-cell base station (SBS). The weighted sum secrecy rate (WSSR) is maximized by jointly designing the active beamforming and RIS reflecting phase shift subject to the transmit power and the phase unit-modulus constraints. However, the problem involves two sum-of-logarithms and highly coupled optimization variables. To tackle the non-convex fractional programming problem with multiple ratios, we employ a lower linearization approach for logarithm subtraction and decompose the problem into two quadratically constrained quadratic programming subproblems. The optimum active beamforming is determined using a semi-definite relaxation method, and the a closed-form solution of RIS phase shift matrix is derived through the alternating direction method of multiplier. Moreover, considering practical finite-capacity backhaul link, we develop the user scheduling strategy using the power of transmit beamforming as a discrete indicator and formulate the user scheduling as a mixed-integer constraint. The joint optimization of user scheduling and WSSR is investigated by maximizing the network utility with a $ell _{1}$ -norm constraint. Simulation results demonstrate the effectiveness of the proposed algorithm in achieving significant WSSR performance even in the presence of low-resolution DACs. Furthermore, these results show that the joint optimization of WSSR and user scheduling can maximize the network utility by selecting the activated subset of served users.
{"title":"Reconfigurable Intelligent Surface-Assisted Multi-User Secrecy Transmission With Low-Resolution DACs","authors":"Kexin Li;Huiqin Du;Si Li","doi":"10.1109/TGCN.2024.3362866","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3362866","url":null,"abstract":"This paper considers a reconfigurable intelligent surface (RIS)-assisted multi-user secrecy transmission in the presence of low-resolution digital-to-analog converters (DACs) at a small-cell base station (SBS). The weighted sum secrecy rate (WSSR) is maximized by jointly designing the active beamforming and RIS reflecting phase shift subject to the transmit power and the phase unit-modulus constraints. However, the problem involves two sum-of-logarithms and highly coupled optimization variables. To tackle the non-convex fractional programming problem with multiple ratios, we employ a lower linearization approach for logarithm subtraction and decompose the problem into two quadratically constrained quadratic programming subproblems. The optimum active beamforming is determined using a semi-definite relaxation method, and the a closed-form solution of RIS phase shift matrix is derived through the alternating direction method of multiplier. Moreover, considering practical finite-capacity backhaul link, we develop the user scheduling strategy using the power of transmit beamforming as a discrete indicator and formulate the user scheduling as a mixed-integer constraint. The joint optimization of user scheduling and WSSR is investigated by maximizing the network utility with a \u0000<inline-formula> <tex-math>$ell _{1}$ </tex-math></inline-formula>\u0000-norm constraint. Simulation results demonstrate the effectiveness of the proposed algorithm in achieving significant WSSR performance even in the presence of low-resolution DACs. Furthermore, these results show that the joint optimization of WSSR and user scheduling can maximize the network utility by selecting the activated subset of served users.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123041","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 : 2024-01-31DOI: 10.1109/TGCN.2024.3360472
Rihito Tsuchida;Kazuya Sakai;Min-Te Sun;Wei-Shinn Ku
Battery-powered sensor devices have been an essential component in Internet of Things (IoT) applications. Much effort has been devoted to designing algorithms that identify efficient routes for a mobile wireless charger to feed sensor devices with energy without plugs, in which power is wirelessely transferred from the charger to sensors. However, existing studies assume static sensors. In this paper, we address the problem of finding better mobile charger trajectories for mobile sensors, where sensor devices are assumed to be mobile. We first introduce two problems. One is the MaxAC problem that maximizes the amount of charge from a charger to sensors within a given time constraint; the other is the MinCD problem that minimizes the charging delay to provide all the sensors with at least a target power level. To this end, we design the charging utility prediction model to estimate how much power can be transferred during a given time interval. Then, two trajectory planning algorithms are proposed, namely TPA-MaxAC and TPA-MinCD, for each problem. The simulation results demonstrate that the proposed algorithms outperform a baseline algorithm as well as the state-of-the-art wireless charging algorithms.
{"title":"On Wireless Charging for Mobile Sensors","authors":"Rihito Tsuchida;Kazuya Sakai;Min-Te Sun;Wei-Shinn Ku","doi":"10.1109/TGCN.2024.3360472","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3360472","url":null,"abstract":"Battery-powered sensor devices have been an essential component in Internet of Things (IoT) applications. Much effort has been devoted to designing algorithms that identify efficient routes for a mobile wireless charger to feed sensor devices with energy without plugs, in which power is wirelessely transferred from the charger to sensors. However, existing studies assume static sensors. In this paper, we address the problem of finding better mobile charger trajectories for mobile sensors, where sensor devices are assumed to be mobile. We first introduce two problems. One is the MaxAC problem that maximizes the amount of charge from a charger to sensors within a given time constraint; the other is the MinCD problem that minimizes the charging delay to provide all the sensors with at least a target power level. To this end, we design the charging utility prediction model to estimate how much power can be transferred during a given time interval. Then, two trajectory planning algorithms are proposed, namely TPA-MaxAC and TPA-MinCD, for each problem. The simulation results demonstrate that the proposed algorithms outperform a baseline algorithm as well as the state-of-the-art wireless charging algorithms.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090993","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 : 2024-01-30DOI: 10.1109/TGCN.2024.3360079
Susan Dominic;Lillykutty Jacob
This paper proposes a novel framework for energy efficiency maximization in an intelligent reflecting surface (IRS) aided single-input, single-output (SISO) non-orthogonal multiple access (NOMA) network through distributed learning based power control. A two-timescale based algorithm is presented to jointly optimize the transmit power of the user equipments (UEs) and reflection coefficients of the IRS elements, while ensuring a minimum rate of transmission for the users. The joint optimization problem is solved at two levels by employing two learning algorithms where the action choice updations in the learning algorithms are performed at two different timescales. The base station (BS) assists the IRS to learn its reflection coefficient matrix. The problem is formulated as an exact potential game with common payoffs and a stochastic learning algorithm (SLA) is proposed. During each iteration of SLA, corresponding to a particular reflection coefficient matrix of the IRS, the UEs learn the minimum transmit power required to satisfy their SINR requirements by employing a distributed learning for pareto optimality (DLPO) algorithm. The proposed learning algorithms are fully distributed since the UEs and the BS need to know only their own utilities and need not have the global channel state information (CSI).
{"title":"A Distributed Learning Algorithm for Power Control in Energy Efficient IRS Assisted SISO NOMA Networks","authors":"Susan Dominic;Lillykutty Jacob","doi":"10.1109/TGCN.2024.3360079","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3360079","url":null,"abstract":"This paper proposes a novel framework for energy efficiency maximization in an intelligent reflecting surface (IRS) aided single-input, single-output (SISO) non-orthogonal multiple access (NOMA) network through distributed learning based power control. A two-timescale based algorithm is presented to jointly optimize the transmit power of the user equipments (UEs) and reflection coefficients of the IRS elements, while ensuring a minimum rate of transmission for the users. The joint optimization problem is solved at two levels by employing two learning algorithms where the action choice updations in the learning algorithms are performed at two different timescales. The base station (BS) assists the IRS to learn its reflection coefficient matrix. The problem is formulated as an exact potential game with common payoffs and a stochastic learning algorithm (SLA) is proposed. During each iteration of SLA, corresponding to a particular reflection coefficient matrix of the IRS, the UEs learn the minimum transmit power required to satisfy their SINR requirements by employing a distributed learning for pareto optimality (DLPO) algorithm. The proposed learning algorithms are fully distributed since the UEs and the BS need to know only their own utilities and need not have the global channel state information (CSI).","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123030","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 : 2024-01-30DOI: 10.1109/TGCN.2024.3360242
Chang Liu;Jun-Bo Wang;Cheng Zeng;Yijian Chen;Hongkang Yu;Yijin Pan
Multi-access edge computing (MEC) and wireless power transfer (WPT) have emerged as promising paradigms to address the bottlenecks of computing power and battery capacity of mobile devices. In this paper, we investigate the integrated scheduling of WPT and task offloading in a rechargeable multi-access edge computing network (RMECN). Specifically, we focus on exploring the tradeoff between energy efficiency, buffer stability, and battery level stability in the RMECN to obtain reasonable scheduling. In addition, we adopt a dynamic Li-ion battery model to describe the charge/discharge characteristics. Given the stochastic nature of channel states and task arrivals, we formulate a stochastic optimization problem that minimizes system energy consumption while ensuring buffer and battery level stability. In this problem, we jointly consider offloading decisions, local central processing unit (CPU) frequency, transmission power, and current of charge/discharge as optimization variables. To solve this stochastic non-convex problem, we first transform it into an online optimization problem using the Lyapunov optimization theory. Then, we propose a distributed algorithm based on game theory to overcome the excessive computation and time consumption of traditional centralized optimization algorithms. The numerical results demonstrate that the proposed tradeoff scheme and corresponding algorithm can effectively reduce the system’s energy consumption while ensuring the stability of buffer and battery level.
{"title":"Joint Optimization of Transmission and Computation Resources for Rechargeable Multi-Access Edge Computing Networks","authors":"Chang Liu;Jun-Bo Wang;Cheng Zeng;Yijian Chen;Hongkang Yu;Yijin Pan","doi":"10.1109/TGCN.2024.3360242","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3360242","url":null,"abstract":"Multi-access edge computing (MEC) and wireless power transfer (WPT) have emerged as promising paradigms to address the bottlenecks of computing power and battery capacity of mobile devices. In this paper, we investigate the integrated scheduling of WPT and task offloading in a rechargeable multi-access edge computing network (RMECN). Specifically, we focus on exploring the tradeoff between energy efficiency, buffer stability, and battery level stability in the RMECN to obtain reasonable scheduling. In addition, we adopt a dynamic Li-ion battery model to describe the charge/discharge characteristics. Given the stochastic nature of channel states and task arrivals, we formulate a stochastic optimization problem that minimizes system energy consumption while ensuring buffer and battery level stability. In this problem, we jointly consider offloading decisions, local central processing unit (CPU) frequency, transmission power, and current of charge/discharge as optimization variables. To solve this stochastic non-convex problem, we first transform it into an online optimization problem using the Lyapunov optimization theory. Then, we propose a distributed algorithm based on game theory to overcome the excessive computation and time consumption of traditional centralized optimization algorithms. The numerical results demonstrate that the proposed tradeoff scheme and corresponding algorithm can effectively reduce the system’s energy consumption while ensuring the stability of buffer and battery level.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123029","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 : 2024-01-29DOI: 10.1109/TGCN.2024.3359208
Siting Lv;Xiaohui Li;Jiawen Liu;Mingli Shi
This paper focuses on a deep learning (DL) framework for the Sub-6G aided millimeter-wave (mmWave) communication system, aiming to reduce the overhead of mmWave systems. The proposed framework consists of two-stage cascaded networks, named HestNet and HBFNet, for mmWave channel estimation and hybrid beamforming (HBF) design, respectively. The number of parameters for channel estimation is reduced by using channel covariance matrix (CCM) estimation instead. However, a new challenge of estimating high-dimensional data from low-dimensional data should be considered since the dimension of Sub-6G channel data is much smaller than that of mmWave. Subsequently, a data deformation approach is introduced into the framework to match the size of Sub-6G channel data with that of mmWave. The simulation results show that the application of statistical channel information based on Sub-6G channel information to aid mmWave communication is reasonable and effective, it achieves good estimation performance and spectral efficiency. Moreover, the two-stage cascaded network architecture proposed in this paper is also more robust to channel estimation errors.
{"title":"Sub-6G Aided Millimeter Wave Hybrid Beamforming: A Two-Stage Deep Learning Framework With Statistical Channel Information","authors":"Siting Lv;Xiaohui Li;Jiawen Liu;Mingli Shi","doi":"10.1109/TGCN.2024.3359208","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3359208","url":null,"abstract":"This paper focuses on a deep learning (DL) framework for the Sub-6G aided millimeter-wave (mmWave) communication system, aiming to reduce the overhead of mmWave systems. The proposed framework consists of two-stage cascaded networks, named HestNet and HBFNet, for mmWave channel estimation and hybrid beamforming (HBF) design, respectively. The number of parameters for channel estimation is reduced by using channel covariance matrix (CCM) estimation instead. However, a new challenge of estimating high-dimensional data from low-dimensional data should be considered since the dimension of Sub-6G channel data is much smaller than that of mmWave. Subsequently, a data deformation approach is introduced into the framework to match the size of Sub-6G channel data with that of mmWave. The simulation results show that the application of statistical channel information based on Sub-6G channel information to aid mmWave communication is reasonable and effective, it achieves good estimation performance and spectral efficiency. Moreover, the two-stage cascaded network architecture proposed in this paper is also more robust to channel estimation errors.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123063","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 : 2024-01-24DOI: 10.1109/TGCN.2024.3358230
Hrishikesh Dutta;Amit Kumar Bhuyan;Subir Biswas
Efficient slot allocation and transmit-sleep scheduling is an effective access control mechanism for improving communication performance and network lifetime in resource-constrained wireless networks. In this paper, a decentralized and multi-tier framework is presented for joint slot allocation and transmit-sleep scheduling in wireless network nodes with thin energy budget. The key learning objectives of this architecture are: collision-free transmission scheduling, reducing energy consumption, and improving network performance. This is achieved using a cooperative and decentralized learning behavior of multiple Reinforcement Learning (RL) agents. The resulting architecture provides throughput-sustainable support for data flows while minimizing energy expenditure and sleep-induced packet losses. To achieve this, a concept of Context is introduced to the RL framework in order to capture network traffic dynamics. The resulting Contextual Deep Q-Learning (CDQL) model makes the system adaptive to dynamic and heterogeneous network load. It also improves energy efficiency when compared with the traditional tabular Q-learning-based approaches. The results demonstrate how this framework can be used for prioritizing application-specific requirements, namely, energy saving and communication reliability. The trade-offs among packet drop, energy expenditure, and learning convergence are studied, and an application-specific solution is proposed for managing them. The performance is compared against an existing state-of-the-art scheduling approach. Moreover, an analytical model of the system dynamics is developed and validated using simulation for arbitrary mesh topologies and traffic patterns.
{"title":"Contextual Deep Reinforcement Learning for Flow and Energy Management in Wireless Sensor and IoT Networks","authors":"Hrishikesh Dutta;Amit Kumar Bhuyan;Subir Biswas","doi":"10.1109/TGCN.2024.3358230","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3358230","url":null,"abstract":"Efficient slot allocation and transmit-sleep scheduling is an effective access control mechanism for improving communication performance and network lifetime in resource-constrained wireless networks. In this paper, a decentralized and multi-tier framework is presented for joint slot allocation and transmit-sleep scheduling in wireless network nodes with thin energy budget. The key learning objectives of this architecture are: collision-free transmission scheduling, reducing energy consumption, and improving network performance. This is achieved using a cooperative and decentralized learning behavior of multiple Reinforcement Learning (RL) agents. The resulting architecture provides throughput-sustainable support for data flows while minimizing energy expenditure and sleep-induced packet losses. To achieve this, a concept of Context is introduced to the RL framework in order to capture network traffic dynamics. The resulting Contextual Deep Q-Learning (CDQL) model makes the system adaptive to dynamic and heterogeneous network load. It also improves energy efficiency when compared with the traditional tabular Q-learning-based approaches. The results demonstrate how this framework can be used for prioritizing application-specific requirements, namely, energy saving and communication reliability. The trade-offs among packet drop, energy expenditure, and learning convergence are studied, and an application-specific solution is proposed for managing them. The performance is compared against an existing state-of-the-art scheduling approach. Moreover, an analytical model of the system dynamics is developed and validated using simulation for arbitrary mesh topologies and traffic patterns.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121601","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 : 2024-01-19DOI: 10.1109/TGCN.2024.3356065
Deepika Saxena;Ashutosh Kumar Singh
This paper addresses the pivotal challenge of achieving seamless performance in Cloud Data Centres $( mathbb {CDC}text{s}$ ) while meeting high availability, security, and sustainability requirements. Existing approaches often struggle to cater to all critical objectives simultaneously and overlook the significance of inter-dependent Virtual Machines (VMs) during resource distribution. To tackle these issues, a novel sustainable resource management model is proposed to provide high availability and reduce security breaches within $ mathbb {CDC}text{s}$ . The contributions include computing VM ranks to prioritize critical VMs for high availability, workload distribution with power and heat constraints for a sustainable environment, and minimizing security breaches through monitoring and terminating malicious VMs. Real-world Google Cluster workloads validate the model’s efficacy, showcasing improved availability, resource utilization, Power Usage Effectiveness (PUE), up to 15.11%, 19%, and 23.4%, respectively with reduced security breaches, and energy consumption up to 53.8% and 17.1%, respectively.
{"title":"A High Up-Time and Security Centered Resource Provisioning Model Toward Sustainable Cloud Service Management","authors":"Deepika Saxena;Ashutosh Kumar Singh","doi":"10.1109/TGCN.2024.3356065","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3356065","url":null,"abstract":"This paper addresses the pivotal challenge of achieving seamless performance in Cloud Data Centres \u0000<inline-formula> <tex-math>$( mathbb {CDC}text{s}$ </tex-math></inline-formula>\u0000) while meeting high availability, security, and sustainability requirements. Existing approaches often struggle to cater to all critical objectives simultaneously and overlook the significance of inter-dependent Virtual Machines (VMs) during resource distribution. To tackle these issues, a novel sustainable resource management model is proposed to provide high availability and reduce security breaches within \u0000<inline-formula> <tex-math>$ mathbb {CDC}text{s}$ </tex-math></inline-formula>\u0000. The contributions include computing VM ranks to prioritize critical VMs for high availability, workload distribution with power and heat constraints for a sustainable environment, and minimizing security breaches through monitoring and terminating malicious VMs. Real-world Google Cluster workloads validate the model’s efficacy, showcasing improved availability, resource utilization, Power Usage Effectiveness (PUE), up to 15.11%, 19%, and 23.4%, respectively with reduced security breaches, and energy consumption up to 53.8% and 17.1%, respectively.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123062","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 : 2024-01-18DOI: 10.1109/TGCN.2024.3355894
Thanh V. Pham;Anh T. Pham;Susumu Ishihara
This paper studies the design of energy-efficient artificial noise (AN) schemes in the context of physical layer security in visible light communications (VLC). Two different transmission schemes termed selective AN-aided single-input single-output (SISO) and AN-aided multiple-input single-output (MISO) are examined and compared in terms of secrecy energy efficiency (SEE). In the former, the closest LED luminaire to the legitimate user (Bob) is the information-bearing signal’s transmitter. At the same time, the rest of the luminaries act as jammers transmitting AN to degrade the channels of eavesdroppers (Eves). In the latter, the information-bearing signal and AN are combined and transmitted by all luminaries. When Eves’ CSI is unknown, an indirect design to improve the SEE is formulated by maximizing Bob’s channel’s energy efficiency. A low-complexity design based on the zero-forcing criterion is also proposed. In the case of known Eves’ CSI, we study the design that maximizes the minimum SEE among those corresponding to all eavesdroppers. At their respective optimal SEEs, simulation results reveal that when Eves’ CSI is unknown, the selective AN-aided SISO transmission can archive twice as good SEE as the AN-aided MISO does. In contrast, when Eves’ CSI is known, the AN-aided MISO outperforms by 30%.
本文在可见光通信（VLC）物理层安全的背景下，研究了高能效人工噪声（AN）方案的设计。本文研究了两种不同的传输方案，分别称为选择性人工噪音辅助单输入单输出（SISO）和人工噪音辅助多输入单输出（MISO），并从保密能效（SEE）的角度进行了比较。在前者中，离合法用户（Bob）最近的 LED 灯具是信息信号的发射器。同时，其他灯具充当干扰器，发射 AN 以削弱窃听者（Eves）的信道。在后一种情况下，信息信号和 AN 合并后由所有灯具发射。当 Eves 的 CSI 未知时，可以通过最大化 Bob 信道的能效来间接设计改进 SEE。此外，还提出了一种基于零强迫准则的低复杂度设计。在已知 Eves CSI 的情况下，我们研究了在所有窃听者对应的设计中使最小 SEE 最大化的设计。仿真结果表明，在各自的最佳 SEE 下，当 Eves 的 CSI 未知时，选择性 AN 辅助 SISO 传输的 SEE 是 AN 辅助 MISO 的两倍。相比之下，当已知 Eves 的 CSI 时，AN 辅助 MISO 的性能要高出 30%。
{"title":"Design of Energy-Efficient Artificial Noise for Physical Layer Security in Visible Light Communications","authors":"Thanh V. Pham;Anh T. Pham;Susumu Ishihara","doi":"10.1109/TGCN.2024.3355894","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3355894","url":null,"abstract":"This paper studies the design of energy-efficient artificial noise (AN) schemes in the context of physical layer security in visible light communications (VLC). Two different transmission schemes termed selective AN-aided single-input single-output (SISO) and AN-aided multiple-input single-output (MISO) are examined and compared in terms of secrecy energy efficiency (SEE). In the former, the closest LED luminaire to the legitimate user (Bob) is the information-bearing signal’s transmitter. At the same time, the rest of the luminaries act as jammers transmitting AN to degrade the channels of eavesdroppers (Eves). In the latter, the information-bearing signal and AN are combined and transmitted by all luminaries. When Eves’ CSI is unknown, an indirect design to improve the SEE is formulated by maximizing Bob’s channel’s energy efficiency. A low-complexity design based on the zero-forcing criterion is also proposed. In the case of known Eves’ CSI, we study the design that maximizes the minimum SEE among those corresponding to all eavesdroppers. At their respective optimal SEEs, simulation results reveal that when Eves’ CSI is unknown, the selective AN-aided SISO transmission can archive twice as good SEE as the AN-aided MISO does. In contrast, when Eves’ CSI is known, the AN-aided MISO outperforms by 30%.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078734","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}
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