Pub Date : 2024-07-04DOI: 10.1109/TGCN.2024.3422992
Han Yan;Hua Chen;Wei Liu;Songjie Yang;Gang Wang;Chau Yuen
In this paper, we tackle the challenges of reconfigurable intelligent surfaces (RIS)-aided 3D localization and synchronization in multipath environments, focusing on the near-field of mmWave systems. Specifically, a maximum likelihood (ML) estimation problem is formulated for the channel parameters. To initiate this process, we leverage a combination of canonical polyadic decomposition (CPD) and orthogonal matching pursuit (OMP) to obtain coarse estimates of the time of arrival (ToA) and angle of departure (AoD) under the far-field approximation. Subsequently, distances are estimated using $l_{1}$ -regularization based on a near-field model. A refinement phase is introduced by employing the spatial alternating generalized expectation maximization (SAGE) algorithm. Finally, a weighted least squares approach is applied to convert channel parameters into position and clock offset estimates. To extend the estimation algorithm to ultra-large (UL) RIS-assisted localization scenarios, it is further enhanced to reduce errors associated with far-field approximations, especially in the presence of significant near-field effects, achieved by narrowing the RIS aperture. Moreover, the Cram$acute {text {e}}$ r-Rao Bound (CRB) is derived and the RIS phase shifts are optimized to improve the positioning accuracy. Numerical results affirm the efficacy of the proposed estimation algorithm.
{"title":"RIS-Enabled Joint Near-Field 3D Localization and Synchronization in SISO Multipath Environments","authors":"Han Yan;Hua Chen;Wei Liu;Songjie Yang;Gang Wang;Chau Yuen","doi":"10.1109/TGCN.2024.3422992","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3422992","url":null,"abstract":"In this paper, we tackle the challenges of reconfigurable intelligent surfaces (RIS)-aided 3D localization and synchronization in multipath environments, focusing on the near-field of mmWave systems. Specifically, a maximum likelihood (ML) estimation problem is formulated for the channel parameters. To initiate this process, we leverage a combination of canonical polyadic decomposition (CPD) and orthogonal matching pursuit (OMP) to obtain coarse estimates of the time of arrival (ToA) and angle of departure (AoD) under the far-field approximation. Subsequently, distances are estimated using <inline-formula> <tex-math>$l_{1}$ </tex-math></inline-formula>-regularization based on a near-field model. A refinement phase is introduced by employing the spatial alternating generalized expectation maximization (SAGE) algorithm. Finally, a weighted least squares approach is applied to convert channel parameters into position and clock offset estimates. To extend the estimation algorithm to ultra-large (UL) RIS-assisted localization scenarios, it is further enhanced to reduce errors associated with far-field approximations, especially in the presence of significant near-field effects, achieved by narrowing the RIS aperture. Moreover, the Cram<inline-formula> <tex-math>$acute {text {e}}$ </tex-math></inline-formula>r-Rao Bound (CRB) is derived and the RIS phase shifts are optimized to improve the positioning accuracy. Numerical results affirm the efficacy of the proposed estimation algorithm.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 1","pages":"367-379"},"PeriodicalIF":5.3,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403771","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}
Open Radio Access Network (O-RAN) has brought a significant transformation in the field of communication networks. Its openness propels communication networks towards a more open, flexible, and efficient direction. Meanwhile, Unmanned Aerial Vehicle (UAV) communication, as a key technology in the sixth generation mobile communication network, offers more flexible and efficient solutions to address diverse environments and requirements. On this basis, we investigate the O-RAN-enabled UAV-assisted network architecture, in which the UAV assists the terrestrial network to enhance the wireless coverage performance. To further explore the advantages of the proposed architecture, we propose a joint problem involving radio unit association, aerial radio unit deployment, and resource allocation, with the objective of maximizing network energy efficiency. To tackle this problem, we design a double-loop-based algorithm. Specifically, we employ the Dinkelbach method in the outer loop to handle the fractional form of the objective function and devise an iterative algorithm based on Block Coordinate Descent architecture in the inner loop to optimize the decoupled sub-problems. Comprehensive simulation results are provided to verify the effectiveness of the proposal.
{"title":"Energy-Efficient Deployment and Resource Allocation for O-RAN-Enabled UAV-Assisted Communication","authors":"Huan Li;Xiao Tang;Daosen Zhai;Ruonan Zhang;Bin Li;Haotong Cao;Neeraj Kumar;Ahmad Almogren","doi":"10.1109/TGCN.2024.3422393","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3422393","url":null,"abstract":"Open Radio Access Network (O-RAN) has brought a significant transformation in the field of communication networks. Its openness propels communication networks towards a more open, flexible, and efficient direction. Meanwhile, Unmanned Aerial Vehicle (UAV) communication, as a key technology in the sixth generation mobile communication network, offers more flexible and efficient solutions to address diverse environments and requirements. On this basis, we investigate the O-RAN-enabled UAV-assisted network architecture, in which the UAV assists the terrestrial network to enhance the wireless coverage performance. To further explore the advantages of the proposed architecture, we propose a joint problem involving radio unit association, aerial radio unit deployment, and resource allocation, with the objective of maximizing network energy efficiency. To tackle this problem, we design a double-loop-based algorithm. Specifically, we employ the Dinkelbach method in the outer loop to handle the fractional form of the objective function and devise an iterative algorithm based on Block Coordinate Descent architecture in the inner loop to optimize the decoupled sub-problems. Comprehensive simulation results are provided to verify the effectiveness of the proposal.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 3","pages":"1128-1140"},"PeriodicalIF":5.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090830","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 : 2024-07-03DOI: 10.1109/TGCN.2024.3422262
Yi Fang;Yuchen Pan;Huan Ma;Dingfei Ma;Mohsen Guizani
The joint radar and communication (JRC) system is an appealing technology that integrates communication and radar sensing functionalities onto a single hardware equipment. The JRC system’s appeal largely stems from its compact form, reduced power demands, and cost-efficiency. Within this context, we present a novel differential chaos shift keying-based linear frequency modulation waveform design (DCSK-LFM) to execute both communication and radar sensing operations in this paper. The scheme employs the LFM waveform as the fundamental radar waveform, while information symbols are embedded by DCSK modulation. Furthermore, the proposed DCSK-LFM scheme offers a controllable trade-off between the data rate and the maximum detection distance, adjustable through the pulse repetition period. The performance of the proposed DCSK-LFM scheme is carefully analyzed using the spectrum characteristic, bit error rate (BER), and ambiguity function. The effectiveness of the proposed DCSK-LFM scheme is validated by simulation results, demonstrating its notable proficiency in spectral characteristics, BER, and detection accuracy. Moreover, the proposed scheme exhibits a smaller leakage ratio compared to alternative approaches. These findings highlight that the proposed transmission scheme has significant potential for JRC systems.
{"title":"A Novel DCSK-Based Linear Frequency Modulation Waveform Design for Joint Radar and Communication Systems","authors":"Yi Fang;Yuchen Pan;Huan Ma;Dingfei Ma;Mohsen Guizani","doi":"10.1109/TGCN.2024.3422262","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3422262","url":null,"abstract":"The joint radar and communication (JRC) system is an appealing technology that integrates communication and radar sensing functionalities onto a single hardware equipment. The JRC system’s appeal largely stems from its compact form, reduced power demands, and cost-efficiency. Within this context, we present a novel differential chaos shift keying-based linear frequency modulation waveform design (DCSK-LFM) to execute both communication and radar sensing operations in this paper. The scheme employs the LFM waveform as the fundamental radar waveform, while information symbols are embedded by DCSK modulation. Furthermore, the proposed DCSK-LFM scheme offers a controllable trade-off between the data rate and the maximum detection distance, adjustable through the pulse repetition period. The performance of the proposed DCSK-LFM scheme is carefully analyzed using the spectrum characteristic, bit error rate (BER), and ambiguity function. The effectiveness of the proposed DCSK-LFM scheme is validated by simulation results, demonstrating its notable proficiency in spectral characteristics, BER, and detection accuracy. Moreover, the proposed scheme exhibits a smaller leakage ratio compared to alternative approaches. These findings highlight that the proposed transmission scheme has significant potential for JRC systems.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 1","pages":"354-366"},"PeriodicalIF":5.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403823","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 : 2024-07-01DOI: 10.1109/TGCN.2024.3421357
Haoyu Quan;Qingmiao Zhang;Junhui Zhao
As a crucial solution to the insufficient computing resources of device in Internet of Vehicles (IoVs) systems, mobile edge computing (MEC) has received widespread attention, especially for tackling delay-sensitive tasks in IoVs. This paper focuses on a multi-roadside units (RSUs) multi-vehicle IoV MEC system with different task delay thresholds. To enhance the system performance in terms of task completion rate, service delay, and energy consumption, a hybrid multi-agent deep reinforcement learning algorithm (HMADRL) based adaptive joint optimization scheme was proposed for computation offloading and resource allocation strategies. Further, a centralized computation offloading and distributed resource allocation framework is designed to reduce communication overhead between multiple agents, and federated learning (FL) technology is used to protect user privacy and accelerate training. The numerical results validate that our scheme improves the performance of IoV MEC system significantly while satisfying system resource and task delay constraints.
{"title":"Federated Learning Assisted Intelligent IoV Mobile Edge Computing","authors":"Haoyu Quan;Qingmiao Zhang;Junhui Zhao","doi":"10.1109/TGCN.2024.3421357","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3421357","url":null,"abstract":"As a crucial solution to the insufficient computing resources of device in Internet of Vehicles (IoVs) systems, mobile edge computing (MEC) has received widespread attention, especially for tackling delay-sensitive tasks in IoVs. This paper focuses on a multi-roadside units (RSUs) multi-vehicle IoV MEC system with different task delay thresholds. To enhance the system performance in terms of task completion rate, service delay, and energy consumption, a hybrid multi-agent deep reinforcement learning algorithm (HMADRL) based adaptive joint optimization scheme was proposed for computation offloading and resource allocation strategies. Further, a centralized computation offloading and distributed resource allocation framework is designed to reduce communication overhead between multiple agents, and federated learning (FL) technology is used to protect user privacy and accelerate training. The numerical results validate that our scheme improves the performance of IoV MEC system significantly while satisfying system resource and task delay constraints.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 1","pages":"228-241"},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430348","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 : 2024-07-01DOI: 10.1109/TGCN.2024.3420957
Zouhir Bellal;Laaziz Lahlou;Nadjia Kara;Ibtissam El Khayat
Edge computing-based microservices (ECM) are pivotal infrastructure components for latency-critical applications such as Virtual Reality/Augmented Reality (VR/AR) and the Internet of Things (IoT). ECM involves strategically deploying microservices at the network’s edge to fulfill the low latency needs of modern applications. However, achieving efficient resource and energy consumption while meeting the latency requirement in the ECM environment remains challenging. Dynamic Voltage and Frequency Scaling (DVFS) is a common technique to address this issue. It adjusts the CPU frequency and voltage to balance energy cost and performance. However, selecting the optimal CPU frequency depends on the nature of the microservice workload (e.g., CPU-bound, memory-bound, or mixed). Moreover, various microservices with different latency requirement can be deployed on the same edge node. This makes the DVFS application extremely challenging, particularly for a chip-wide DVFS implementation for which CPU cores operate at the same frequency and voltage. To this end, we propose GAS, enerGy Aware microServices edge computing framework, which enables CPU frequency scaling to meet diverse microservice latency requirement with the minimum energy cost. Our evaluation indicates that our CPU scaling policy decreases energy consumption by 5% to 23% compared to Linux governors while maintaining latency requirement and significantly contributing to sustainable edge computing.
{"title":"GAS: DVFS-Driven Energy Efficiency Approach for Latency-Guaranteed Edge Computing Microservices","authors":"Zouhir Bellal;Laaziz Lahlou;Nadjia Kara;Ibtissam El Khayat","doi":"10.1109/TGCN.2024.3420957","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3420957","url":null,"abstract":"Edge computing-based microservices (ECM) are pivotal infrastructure components for latency-critical applications such as Virtual Reality/Augmented Reality (VR/AR) and the Internet of Things (IoT). ECM involves strategically deploying microservices at the network’s edge to fulfill the low latency needs of modern applications. However, achieving efficient resource and energy consumption while meeting the latency requirement in the ECM environment remains challenging. Dynamic Voltage and Frequency Scaling (DVFS) is a common technique to address this issue. It adjusts the CPU frequency and voltage to balance energy cost and performance. However, selecting the optimal CPU frequency depends on the nature of the microservice workload (e.g., CPU-bound, memory-bound, or mixed). Moreover, various microservices with different latency requirement can be deployed on the same edge node. This makes the DVFS application extremely challenging, particularly for a chip-wide DVFS implementation for which CPU cores operate at the same frequency and voltage. To this end, we propose GAS, enerGy Aware microServices edge computing framework, which enables CPU frequency scaling to meet diverse microservice latency requirement with the minimum energy cost. Our evaluation indicates that our CPU scaling policy decreases energy consumption by 5% to 23% compared to Linux governors while maintaining latency requirement and significantly contributing to sustainable edge computing.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 1","pages":"108-124"},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430394","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 : 2024-06-25DOI: 10.1109/TGCN.2024.3418948
Anderson Augusto Simiscuka;Mohammed Amine Togou;Mikel Zorrilla;Gabriel-Miro Muntean
There is increasing viewer interest and technological support for streaming immersive clips over the Internet. There are, however, challenges in supporting high quality of viewer experience, mostly due to the large amounts of the data associated with immersive video and spatial audio (Ambisonics). In situations where there are limited network resources, the streamed 360° content needs to be adjusted dynamically to meet the network constraints. Dynamic Adaptive Streaming over HTTP (DASH) adaptation is a key technology for delivering high-quality video over open radio access networks (RANs). DASH allows for efficient adaptation of video streams to the available network conditions. This paper introduces 360-ADAPT, a DASH-based adaptation solution on an Open-RAN architecture for increased quality remote 360° opera experiences. Unlike existing schemes, 360-ADAPT gives precedence to audio over the video when selecting bitrates, increasing the overall quality of the artistic act and improving use of resources and energy. The proposed 360-ADAPT was tested with real opera viewers in the context of an artistic-oriented platform for opera delivery, part of the Horizon2020 TRACTION project. Results indicate that 360-ADAPT achieves higher perceived quality levels than alternative solutions both in QoS and QoE metrics.
{"title":"360-ADAPT: An Open-RAN-Based Adaptive Scheme for Quality Enhancement of Opera 360° Content Distribution","authors":"Anderson Augusto Simiscuka;Mohammed Amine Togou;Mikel Zorrilla;Gabriel-Miro Muntean","doi":"10.1109/TGCN.2024.3418948","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3418948","url":null,"abstract":"There is increasing viewer interest and technological support for streaming immersive clips over the Internet. There are, however, challenges in supporting high quality of viewer experience, mostly due to the large amounts of the data associated with immersive video and spatial audio (Ambisonics). In situations where there are limited network resources, the streamed 360° content needs to be adjusted dynamically to meet the network constraints. Dynamic Adaptive Streaming over HTTP (DASH) adaptation is a key technology for delivering high-quality video over open radio access networks (RANs). DASH allows for efficient adaptation of video streams to the available network conditions. This paper introduces 360-ADAPT, a DASH-based adaptation solution on an Open-RAN architecture for increased quality remote 360° opera experiences. Unlike existing schemes, 360-ADAPT gives precedence to audio over the video when selecting bitrates, increasing the overall quality of the artistic act and improving use of resources and energy. The proposed 360-ADAPT was tested with real opera viewers in the context of an artistic-oriented platform for opera delivery, part of the Horizon2020 TRACTION project. Results indicate that 360-ADAPT achieves higher perceived quality levels than alternative solutions both in QoS and QoE metrics.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 3","pages":"924-938"},"PeriodicalIF":5.3,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10571391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090994","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 : 2024-06-25DOI: 10.1109/TGCN.2024.3418842
Juan Carlos Ruiz-Sicilia;Marco Di Renzo;Placido Mursia;Aryan Kaushik;Vincenzo Sciancalepore
Sixth generation (6G) wireless networks are envisioned to include aspects of energy footprint reduction (sustainability), besides those of network capacity and connectivity, at the design stage. This paradigm change requires radically new physical layer technologies. Notably, the integration of large-aperture arrays and the transmission over high frequency bands, such as the sub-terahertz spectrum, are two promising options. In many communication scenarios of practical interest, the use of large antenna arrays in the sub-terahertz frequency range often results in short-range transmission distances that are characterized by line-of-sight channels, in which pairs of transmitters and receivers are located in the (radiating) near field of one another. These features make the traditional designs, based on the far-field approximation, for multiple-input multiple-output (MIMO) systems sub-optimal in terms of spatial multiplexing gains. To overcome these limitations, new designs for MIMO systems are required, which account for the spherical wavefront that characterizes the electromagnetic waves in the near field, in order to ensure the highest spatial multiplexing gain without increasing the power expenditure. In this paper, we introduce an analytical framework for optimizing the deployment of antenna arrays in line-of-sight channels, which can be applied to paraxial and non-paraxial network deployments. In the paraxial setting, we devise a simpler analytical framework, which, compared to those available in the literature, provides explicit information about the impact of key design parameters. In the non-paraxial setting, we introduce a novel analytical framework that allows us to identify a set of sufficient conditions to be fulfilled for achieving the highest spatial multiplexing gain. The proposed designs are validated with numerical simulations.
{"title":"Spatial Multiplexing in Near-Field Line-of-Sight MIMO Communications: Paraxial and Non-Paraxial Deployments","authors":"Juan Carlos Ruiz-Sicilia;Marco Di Renzo;Placido Mursia;Aryan Kaushik;Vincenzo Sciancalepore","doi":"10.1109/TGCN.2024.3418842","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3418842","url":null,"abstract":"Sixth generation (6G) wireless networks are envisioned to include aspects of energy footprint reduction (sustainability), besides those of network capacity and connectivity, at the design stage. This paradigm change requires radically new physical layer technologies. Notably, the integration of large-aperture arrays and the transmission over high frequency bands, such as the sub-terahertz spectrum, are two promising options. In many communication scenarios of practical interest, the use of large antenna arrays in the sub-terahertz frequency range often results in short-range transmission distances that are characterized by line-of-sight channels, in which pairs of transmitters and receivers are located in the (radiating) near field of one another. These features make the traditional designs, based on the far-field approximation, for multiple-input multiple-output (MIMO) systems sub-optimal in terms of spatial multiplexing gains. To overcome these limitations, new designs for MIMO systems are required, which account for the spherical wavefront that characterizes the electromagnetic waves in the near field, in order to ensure the highest spatial multiplexing gain without increasing the power expenditure. In this paper, we introduce an analytical framework for optimizing the deployment of antenna arrays in line-of-sight channels, which can be applied to paraxial and non-paraxial network deployments. In the paraxial setting, we devise a simpler analytical framework, which, compared to those available in the literature, provides explicit information about the impact of key design parameters. In the non-paraxial setting, we introduce a novel analytical framework that allows us to identify a set of sufficient conditions to be fulfilled for achieving the highest spatial multiplexing gain. The proposed designs are validated with numerical simulations.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 1","pages":"338-353"},"PeriodicalIF":5.3,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403785","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 : 2024-06-24DOI: 10.1109/TGCN.2024.3418410
Fulai Liu;Xuefei Sun;Ruxin Liu;Hao Qin;Baozhu Shi;Ruiyan Du
As one of the promising technologies of wideband beamforming, the anti-mainlobe interference wideband beamforming (AWB) algorithm can effectively suppress mainlobe distortion and sidelobe level rise, thereby improving the output signal to interference plus noise ratio (SINR) performance. Therefore, an AWB algorithm is proposed via a feature fusion convolutional neural network (FCNN) in this paper, named as AWB-FCNN algorithm. It can improve the beamforming performance and ensure the computational efficiency. For this algorithm, an AWB algorithm firstly is used to generate the network training label. Then, an FCNN model is constructed to predict beamforming weight vectors, which consists of a feature extraction module, a feature fusion module, and a weight vector prediction module. Specially, an atrous convolution layer is introduced into the feature extraction module to extract dense features, which be achieved by enlarging the receptive field without increasing the parameters of the network. Besides, the feature fusion module is used to reduce the irrelevant features such as mainlobe interference by fusing features at different scales. Finally, the well-trained FCNN model can rapidly and precisely output beamforming weight vectors. Simulation results show that the proposed algorithm has excellent interference suppression ability and high computational efficiency.
{"title":"AWB-FCNN Algorithm for Mainlobe Interference Suppression","authors":"Fulai Liu;Xuefei Sun;Ruxin Liu;Hao Qin;Baozhu Shi;Ruiyan Du","doi":"10.1109/TGCN.2024.3418410","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3418410","url":null,"abstract":"As one of the promising technologies of wideband beamforming, the anti-mainlobe interference wideband beamforming (AWB) algorithm can effectively suppress mainlobe distortion and sidelobe level rise, thereby improving the output signal to interference plus noise ratio (SINR) performance. Therefore, an AWB algorithm is proposed via a feature fusion convolutional neural network (FCNN) in this paper, named as AWB-FCNN algorithm. It can improve the beamforming performance and ensure the computational efficiency. For this algorithm, an AWB algorithm firstly is used to generate the network training label. Then, an FCNN model is constructed to predict beamforming weight vectors, which consists of a feature extraction module, a feature fusion module, and a weight vector prediction module. Specially, an atrous convolution layer is introduced into the feature extraction module to extract dense features, which be achieved by enlarging the receptive field without increasing the parameters of the network. Besides, the feature fusion module is used to reduce the irrelevant features such as mainlobe interference by fusing features at different scales. Finally, the well-trained FCNN model can rapidly and precisely output beamforming weight vectors. Simulation results show that the proposed algorithm has excellent interference suppression ability and high computational efficiency.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 1","pages":"218-227"},"PeriodicalIF":5.3,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430391","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}
The low-carbon and efficient operation of smart parks requires high-precision and real-time energy management model training. Multi-mode power Internet of Things (PIoT) consisting of open radio access networks (O-RAN) and power line communications (PLC) can effectively improve the model training performance. However, the negative effects of network threats, such as model inversion attacks, cannot be neglected. To solve this problem, we propose a diFferential pRivacy-aware gEnErative aDversarial netwOrk-assisted resource scheduling algorithM (FREEDOM). Firstly, we integrate a differential privacy mechanism with the energy management model training process and the related system model. Then, a joint resource scheduling optimization problem is constructed, the goal of which is to minimize the weighted sum of the global loss function, total energy consumption, and differential privacy cost under the long-term differential privacy constraint. The original problem is converted based on virtual queue theory and addressed by the FREEDOM. FREEDOM leverages a deep Q-learning network (DQN) to learn the resource scheduling strategy via differential privacy awareness. It improves optimization and convergence performances with the assistance of generative adversarial network (GAN). Simulation results show that FREEDOM can achieve excellent performances of global loss function, total energy consumption, differential privacy cost, and privacy preservation.
{"title":"Differential Privacy-Aware Generative Adversarial Network-Assisted Resource Scheduling for Green Multi-Mode Power IoT","authors":"Sunxuan Zhang;Jiapeng Xue;Jiayi Liu;Zhenyu Zhou;Xiaomei Chen;Shahid Mumtaz","doi":"10.1109/TGCN.2024.3417379","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3417379","url":null,"abstract":"The low-carbon and efficient operation of smart parks requires high-precision and real-time energy management model training. Multi-mode power Internet of Things (PIoT) consisting of open radio access networks (O-RAN) and power line communications (PLC) can effectively improve the model training performance. However, the negative effects of network threats, such as model inversion attacks, cannot be neglected. To solve this problem, we propose a diFferential pRivacy-aware gEnErative aDversarial netwOrk-assisted resource scheduling algorithM (FREEDOM). Firstly, we integrate a differential privacy mechanism with the energy management model training process and the related system model. Then, a joint resource scheduling optimization problem is constructed, the goal of which is to minimize the weighted sum of the global loss function, total energy consumption, and differential privacy cost under the long-term differential privacy constraint. The original problem is converted based on virtual queue theory and addressed by the FREEDOM. FREEDOM leverages a deep Q-learning network (DQN) to learn the resource scheduling strategy via differential privacy awareness. It improves optimization and convergence performances with the assistance of generative adversarial network (GAN). Simulation results show that FREEDOM can achieve excellent performances of global loss function, total energy consumption, differential privacy cost, and privacy preservation.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 3","pages":"956-967"},"PeriodicalIF":5.3,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090699","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 : 2024-06-20DOI: 10.1109/TGCN.2024.3417697
Tinh T. Bui;Thinh Quang Do;Dang Van Huynh;Tan Do Duy;Long D. Nguyen;Tuan-Vu Cao;Vishal Sharma;Trung Q. Duong
Mobile edge computing (MEC) is widely employed to allow users to offload computation-intensive tasks due to high energy efficiency, low latency, enhanced privacy, and security. Thanks to advances in manufacturing technologies, MEC-based unmanned aerial vehicle (UAV) networks can be extensions or replacements for edge servers at ground base stations to improve the network flexibility and quality of communication. This study focuses on the non-orthogonal multiple access (NOMA) scheme, emphasizing the coexistence of near-field and far-field regions, particularly in the context of multiple UAVs integrated with edge servers. We address the challenge of the latency minimization problem by efficiently optimizing both communications and computing variables such as user association, capacity allocation, and transmit power. The designed optimization problem is a mixed integer programming problem that has extremely high complexity. To solve this problem, we propose an iterative algorithm that is designed by using block coordinate descent, convex transformation, and relaxation. Through extensive simulations, our proposed solution demonstrates effectiveness in minimizing total task offloading latency across various scenarios. The findings not only contribute a practical convex optimization method to reduce the latency in MEC systems using UAV-aided NOMA networks but also enable the operations of modern applications such as augmented reality and virtual reality on handheld user devices.
{"title":"Task Offloading Optimization for UAV-Aided NOMA Networks With Coexistence of Near-Field and Far-Field Communications","authors":"Tinh T. Bui;Thinh Quang Do;Dang Van Huynh;Tan Do Duy;Long D. Nguyen;Tuan-Vu Cao;Vishal Sharma;Trung Q. Duong","doi":"10.1109/TGCN.2024.3417697","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3417697","url":null,"abstract":"Mobile edge computing (MEC) is widely employed to allow users to offload computation-intensive tasks due to high energy efficiency, low latency, enhanced privacy, and security. Thanks to advances in manufacturing technologies, MEC-based unmanned aerial vehicle (UAV) networks can be extensions or replacements for edge servers at ground base stations to improve the network flexibility and quality of communication. This study focuses on the non-orthogonal multiple access (NOMA) scheme, emphasizing the coexistence of near-field and far-field regions, particularly in the context of multiple UAVs integrated with edge servers. We address the challenge of the latency minimization problem by efficiently optimizing both communications and computing variables such as user association, capacity allocation, and transmit power. The designed optimization problem is a mixed integer programming problem that has extremely high complexity. To solve this problem, we propose an iterative algorithm that is designed by using block coordinate descent, convex transformation, and relaxation. Through extensive simulations, our proposed solution demonstrates effectiveness in minimizing total task offloading latency across various scenarios. The findings not only contribute a practical convex optimization method to reduce the latency in MEC systems using UAV-aided NOMA networks but also enable the operations of modern applications such as augmented reality and virtual reality on handheld user devices.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 1","pages":"327-337"},"PeriodicalIF":5.3,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403783","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：