Backscatter communication, as an important technique in green Internet of Things, has been concerned by academic and industry to improve system capacity and simultaneously reduce network cost in a low-power-consumption way. In this paper, a sum-throughput maximization resource allocation (RA) problem is studied for a full-duplex-enhanced wireless-powered backscatter communication network, where one hybrid access point (HAP) with constant power supply can coordinate wireless energy and information transmission for multiple backscatter users without other energy sources. All users first harvest the wireless energy from the HAP during the downlink transmission and simultaneously backscatter their information to the HAP, and then send their information to the HAP during uplink transmission. Then, a sum-throughput maximization RA problem is formulated by jointly optimizing the beamforming vector of the HAP, energy-harvesting (EH) time, reflection coefficients, and the transmit power of users, where the constraints of the maximum transmit power imposed by the HAP, the minimum throughput and the EH requirement of each user are considered simultaneously. Finally, the non-convex problem is converted into a convex one by applying a series of convex optimization methods, then an iterative-based RA algorithm is proposed to solve it. Simulation results verify the effectiveness of the proposed algorithm.
后向散射通信作为绿色物联网的一项重要技术，一直受到学术界和产业界的关注，它能以低功耗的方式提高系统容量，同时降低网络成本。本文研究了一个全双工增强型无线供电反向散射通信网络的总吞吐量最大化资源分配（RA）问题，在该网络中，一个恒定供电的混合接入点（HAP）可以在没有其他能源的情况下协调多个反向散射用户的无线能量和信息传输。所有用户首先在下行链路传输过程中从混合接入点获取无线能量，同时向混合接入点反向散射信息，然后在上行链路传输过程中向混合接入点发送信息。然后，通过联合优化 HAP 的波束成形向量、能量收集（EH）时间、反射系数和用户的发射功率，提出了总吞吐量最大化 RA 问题，其中同时考虑了 HAP 的最大发射功率、最小吞吐量和每个用户的 EH 要求等约束条件。最后，通过应用一系列凸优化方法将非凸问题转化为凸问题，并提出了一种基于迭代的 RA 算法来解决该问题。仿真结果验证了所提算法的有效性。
{"title":"Full-Duplex-Enhanced Wireless-Powered Backscatter Communication Networks: Radio Resource Allocation and Beamforming Joint Optimization","authors":"Xiaoxi Zhang;Yongjun Xu;Haibo Zhang;Gongpu Wang;Xingwang Li;Chau Yuen","doi":"10.1109/TGCN.2024.3354986","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3354986","url":null,"abstract":"Backscatter communication, as an important technique in green Internet of Things, has been concerned by academic and industry to improve system capacity and simultaneously reduce network cost in a low-power-consumption way. In this paper, a sum-throughput maximization resource allocation (RA) problem is studied for a full-duplex-enhanced wireless-powered backscatter communication network, where one hybrid access point (HAP) with constant power supply can coordinate wireless energy and information transmission for multiple backscatter users without other energy sources. All users first harvest the wireless energy from the HAP during the downlink transmission and simultaneously backscatter their information to the HAP, and then send their information to the HAP during uplink transmission. Then, a sum-throughput maximization RA problem is formulated by jointly optimizing the beamforming vector of the HAP, energy-harvesting (EH) time, reflection coefficients, and the transmit power of users, where the constraints of the maximum transmit power imposed by the HAP, the minimum throughput and the EH requirement of each user are considered simultaneously. Finally, the non-convex problem is converted into a convex one by applying a series of convex optimization methods, then an iterative-based RA algorithm is proposed to solve it. Simulation results verify the effectiveness of the proposed algorithm.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078767","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 : 2024-01-10DOI: 10.1109/TGCN.2024.3352173
Jiansong Miao;Shanling Bai;Shahid Mumtaz;Qian Zhang;Junsheng Mu
The integration of unmanned aerial vehicles (UAVs) in future communication networks has received great attention, and it plays an essential role in many applications, such as military reconnaissance, fire monitoring, etc. In this paper, we consider a UAV-aided video transmission system based on mobile edge computing (MEC). Considering the short latency requirements, the UAV acts as a MEC server to transcode the videos and as a relay to forward the transcoded videos to the ground base station. Subject to constraints on discrete variables and short latency, we aim to maximize the cumulative utility by jointly optimizing the power allocation, video transcoding policy, computational resources allocation, and UAV flight trajectory. The above non-convex optimization problem is modeled as a Markov decision process (MDP) and solved by a deep deterministic policy gradient (DDPG) algorithm to realize continuous action control by policy iteration. Simulation results show that the DDPG algorithm performs better than deep Q-learning network algorithm (DQN) and actor-critic (AC) algorithm.
{"title":"Utility-Oriented Optimization for Video Streaming in UAV-Aided MEC Network: A DRL Approach","authors":"Jiansong Miao;Shanling Bai;Shahid Mumtaz;Qian Zhang;Junsheng Mu","doi":"10.1109/TGCN.2024.3352173","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3352173","url":null,"abstract":"The integration of unmanned aerial vehicles (UAVs) in future communication networks has received great attention, and it plays an essential role in many applications, such as military reconnaissance, fire monitoring, etc. In this paper, we consider a UAV-aided video transmission system based on mobile edge computing (MEC). Considering the short latency requirements, the UAV acts as a MEC server to transcode the videos and as a relay to forward the transcoded videos to the ground base station. Subject to constraints on discrete variables and short latency, we aim to maximize the cumulative utility by jointly optimizing the power allocation, video transcoding policy, computational resources allocation, and UAV flight trajectory. The above non-convex optimization problem is modeled as a Markov decision process (MDP) and solved by a deep deterministic policy gradient (DDPG) algorithm to realize continuous action control by policy iteration. Simulation results show that the DDPG algorithm performs better than deep Q-learning network algorithm (DQN) and actor-critic (AC) algorithm.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078817","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 : 2024-01-08DOI: 10.1109/TGCN.2024.3350787
Hao Lin;Mustafa A. Kishk;Mohamed-Slim Alouini
Bridging the digital divide is one of the goals of mobile networks in the future, and further building IoT networks in rural areas is a feasible solution. This paper studies the downlink performance of rural wireless networks, where IoT devices we consider are battery-less and powered only by ambient radio-frequency (RF) signals. We model a rural area as a finite area that is far from the city center. The base stations (BSs) in the whole city and the access points (APs) in the finite network both act as sources of wireless RF signals harvested by IoT devices. We assume that BSs follow an inhomogeneous Poisson Point Process (PPP) with a 2D-Gaussian density, and a fixed number of APs are uniformly distributed inside the finite area following a Binomial Point Process (BPP). The IoT devices we consider can harvest energy and receive downlink signals in each time slot, which is divided into two parts: (1) a charging sub-slot, where the RF signals from BSs and APs are harvested by IoT devices, and (2) a transmission sub-slot, where each IoT device uses the harvested energy to receive and process downlink signals. We consider two main system requirements: minimum energy requirement and signal-to-interference-plus-noise ratio (SINR). Using these two parameters, we investigate the overall coverage probability (OCP) related to them. We first study the effect of remoteness in rural areas on energy harvesting performance. Then we analyze the influence of IoT device’s location and the number of APs on coverage probability when the effect of BSs can be ignored. This paper shows that the IoT devices located inside the rural area can obtain about twice the ECP and OCP of IoT devices located near the edge. For the average downlink performance in rural areas with radii less than 100 m, more than 80% of the RF-powered IoT devices can be supported when there are 100 APs deployed.
{"title":"Performance Evaluation of RF-Powered IoT in Rural Areas: The Wireless Power Digital Divide","authors":"Hao Lin;Mustafa A. Kishk;Mohamed-Slim Alouini","doi":"10.1109/TGCN.2024.3350787","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3350787","url":null,"abstract":"Bridging the digital divide is one of the goals of mobile networks in the future, and further building IoT networks in rural areas is a feasible solution. This paper studies the downlink performance of rural wireless networks, where IoT devices we consider are battery-less and powered only by ambient radio-frequency (RF) signals. We model a rural area as a finite area that is far from the city center. The base stations (BSs) in the whole city and the access points (APs) in the finite network both act as sources of wireless RF signals harvested by IoT devices. We assume that BSs follow an inhomogeneous Poisson Point Process (PPP) with a 2D-Gaussian density, and a fixed number of APs are uniformly distributed inside the finite area following a Binomial Point Process (BPP). The IoT devices we consider can harvest energy and receive downlink signals in each time slot, which is divided into two parts: (1) a charging sub-slot, where the RF signals from BSs and APs are harvested by IoT devices, and (2) a transmission sub-slot, where each IoT device uses the harvested energy to receive and process downlink signals. We consider two main system requirements: minimum energy requirement and signal-to-interference-plus-noise ratio (SINR). Using these two parameters, we investigate the overall coverage probability (OCP) related to them. We first study the effect of remoteness in rural areas on energy harvesting performance. Then we analyze the influence of IoT device’s location and the number of APs on coverage probability when the effect of BSs can be ignored. This paper shows that the IoT devices located inside the rural area can obtain about twice the ECP and OCP of IoT devices located near the edge. For the average downlink performance in rural areas with radii less than 100 m, more than 80% of the RF-powered IoT devices can be supported when there are 100 APs deployed.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078733","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 : 2024-01-08DOI: 10.1109/TGCN.2024.3350735
Abdullatif Albaseer;Abegaz Mohammed Seid;Mohamed Abdallah;Ala Al-Fuqaha;Aiman Erbad
Researchers and practitioners have recently shown interest in deploying federated learning for enhanced privacy preservation in wireless edge networks. In such settings, resource-constrained user equipment (UE) often experiences unfair energy consumption and performance degradation of machine learning models due to data heterogeneity and constrained computation and communication resources. Several approaches have been proposed in the literature to reduce energy consumption, including scheduling a subset of UEs to undertake learning tasks based on their energy budgets. However, these approaches are inherently unfair as the frequently selected UEs rapidly deplete their energy and are rendered inaccessible. Furthermore, the server may be unable to capture the incongruent data distribution, resulting in a biased model. In this paper, we propose a novel approach that addresses those challenges, considering the historical participation of the UEs to ensure that all the training data of the UEs are incorporated into the global model. Specifically, using Jain’s fairness index, we formulate the overall optimization problem, decompose it into two sub-problems, and iteratively solve the sub-problems. Towards this end, we partition the optimization variables into two-blocks; one on the server-side and another on the UEs’ side. The server-side algorithm aims to balance energy usage and learning performance, while the client-side algorithm seeks to optimize CPU frequency and transmit power. Extensive experiments using two realistic datasets, FEMNIST and CIFAR-10, indicate that the proposed algorithms minimize overall energy while curbing unfair energy consumption between the UEs, accelerating convergence rates, and significantly enhancing local accuracy for all UEs.
{"title":"Novel Approach for Curbing Unfair Energy Consumption and Biased Model in Federated Edge Learning","authors":"Abdullatif Albaseer;Abegaz Mohammed Seid;Mohamed Abdallah;Ala Al-Fuqaha;Aiman Erbad","doi":"10.1109/TGCN.2024.3350735","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3350735","url":null,"abstract":"Researchers and practitioners have recently shown interest in deploying federated learning for enhanced privacy preservation in wireless edge networks. In such settings, resource-constrained user equipment (UE) often experiences unfair energy consumption and performance degradation of machine learning models due to data heterogeneity and constrained computation and communication resources. Several approaches have been proposed in the literature to reduce energy consumption, including scheduling a subset of UEs to undertake learning tasks based on their energy budgets. However, these approaches are inherently unfair as the frequently selected UEs rapidly deplete their energy and are rendered inaccessible. Furthermore, the server may be unable to capture the incongruent data distribution, resulting in a biased model. In this paper, we propose a novel approach that addresses those challenges, considering the historical participation of the UEs to ensure that all the training data of the UEs are incorporated into the global model. Specifically, using Jain’s fairness index, we formulate the overall optimization problem, decompose it into two sub-problems, and iteratively solve the sub-problems. Towards this end, we partition the optimization variables into two-blocks; one on the server-side and another on the UEs’ side. The server-side algorithm aims to balance energy usage and learning performance, while the client-side algorithm seeks to optimize CPU frequency and transmit power. Extensive experiments using two realistic datasets, FEMNIST and CIFAR-10, indicate that the proposed algorithms minimize overall energy while curbing unfair energy consumption between the UEs, accelerating convergence rates, and significantly enhancing local accuracy for all UEs.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078855","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 : 2024-01-04DOI: 10.1109/TGCN.2024.3349697
Alexander Herzog;Robbie Southam;Othmane Belarbi;Saif Anwar;Marcello Bullo;Pietro Carnelli;Aftab Khan
Federated Learning (FL) is fast becoming one of the most prevalent distributed learning techniques focused on privacy preservation and communication efficiency for large-scale Internet of Things (IoT) deployments. FL is a distributed learning approach to training models on distributed devices. Since local data remains on-device, communication through the network is reduced. However, in large-scale IoT environments or resource constrained networks, typical FL approaches significantly suffer in performance due to longer communication times. In this paper, we propose two methods for further reducing communication volume in resource restricted FL deployments. In our first method, which we term Selective Updates (SU), local models are trained until a dynamic threshold on model performance is surpassed before sending updates to a centralised Parameter Server (PS). This allows for minimal updates being transmitted, thus reducing communication overheads. Our second method, Adaptive Masking (AM), performs parameter masking on both the global and local models prior to sharing. With AM, we select model parameters that have changed the most between training rounds. We extensively evaluate our proposed methods against state-of-the-art communication reduction strategies using two common benchmark datasets, and under different communication constrained settings. Our proposed methods reduce the overall communication volume by over 20%, without affecting the model accuracy.
{"title":"Selective Updates and Adaptive Masking for Communication-Efficient Federated Learning","authors":"Alexander Herzog;Robbie Southam;Othmane Belarbi;Saif Anwar;Marcello Bullo;Pietro Carnelli;Aftab Khan","doi":"10.1109/TGCN.2024.3349697","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3349697","url":null,"abstract":"Federated Learning (FL) is fast becoming one of the most prevalent distributed learning techniques focused on privacy preservation and communication efficiency for large-scale Internet of Things (IoT) deployments. FL is a distributed learning approach to training models on distributed devices. Since local data remains on-device, communication through the network is reduced. However, in large-scale IoT environments or resource constrained networks, typical FL approaches significantly suffer in performance due to longer communication times. In this paper, we propose two methods for further reducing communication volume in resource restricted FL deployments. In our first method, which we term Selective Updates (SU), local models are trained until a dynamic threshold on model performance is surpassed before sending updates to a centralised Parameter Server (PS). This allows for minimal updates being transmitted, thus reducing communication overheads. Our second method, Adaptive Masking (AM), performs parameter masking on both the global and local models prior to sharing. With AM, we select model parameters that have changed the most between training rounds. We extensively evaluate our proposed methods against state-of-the-art communication reduction strategies using two common benchmark datasets, and under different communication constrained settings. Our proposed methods reduce the overall communication volume by over 20%, without affecting the model accuracy.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078775","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}
Vehicle-to-everything (V2X) communications in future 6G intelligent transportation systems are expected to enable various convenience applications which consume amount of computation and storage resources in vehicular networks to deliver high-quality, low-latency immersive experiences via vehicular edge computing (VEC). However, as the number of intensive tasks increases, the trade-off problem between task latency requirements and energy consumption becomes more prominent. In this paper, we study the problem of system-wide energy efficient computation offloading in speed-adjustable vehicular edge computing. We firstly consider a novel task offloading environment that considers vehicle speed adjustment to provide latency-constrained computation services for resource-limited vehicles, which fully stimulates the collaborative ability of the transportation system. We formulate the problem as a mixed-integer nonlinear programming problem to minimize the weighted energy consumption of multiple tasks. To solve this problem, we decouple it into two sub-problems, namely the task offloading decision and resource allocation problem, and the vehicle speed adjustment problem. We propose a low-complexity algorithm based on dynamic programming and a speed adjustment algorithm using a direction operator. Simulation results demonstrate the effectiveness of the proposed algorithms in optimizing the weighted energy consumption of the whole system.
{"title":"System-Wide Energy Efficient Computation Offloading in Vehicular Edge Computing With Speed Adjustment","authors":"Haotian Li;Xujie Li;Mingyue Zhang;Buyankhishig Ulziinyam","doi":"10.1109/TGCN.2023.3349273","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3349273","url":null,"abstract":"Vehicle-to-everything (V2X) communications in future 6G intelligent transportation systems are expected to enable various convenience applications which consume amount of computation and storage resources in vehicular networks to deliver high-quality, low-latency immersive experiences via vehicular edge computing (VEC). However, as the number of intensive tasks increases, the trade-off problem between task latency requirements and energy consumption becomes more prominent. In this paper, we study the problem of system-wide energy efficient computation offloading in speed-adjustable vehicular edge computing. We firstly consider a novel task offloading environment that considers vehicle speed adjustment to provide latency-constrained computation services for resource-limited vehicles, which fully stimulates the collaborative ability of the transportation system. We formulate the problem as a mixed-integer nonlinear programming problem to minimize the weighted energy consumption of multiple tasks. To solve this problem, we decouple it into two sub-problems, namely the task offloading decision and resource allocation problem, and the vehicle speed adjustment problem. We propose a low-complexity algorithm based on dynamic programming and a speed adjustment algorithm using a direction operator. Simulation results demonstrate the effectiveness of the proposed algorithms in optimizing the weighted energy consumption of the whole system.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078784","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 : 2023-12-27DOI: 10.1109/TGCN.2023.3347567
Qi Li;Pengbo Si;Yibo Zhang;Jingjing Wang;Dajun Zhang;F. Richard Yu
Unmanned aerial vehicles (UAVs) are widely adopted as aerial relays assisting seamless coverage due to flexible deployment and high maneuverability, but their service time is considered as the bottleneck due to the constrained energy. The energy-efficient intelligent reflecting surface (IRS) is adopted in the UAV system for long-term service. In this paper, the cooperative relay-transmission in UAV-IRS based terahertz (THz) networks is studied, where UAVs with IRSs as relays facilitate long-term seamless coverage through THz transmission, and the maximum transmission capacity based on sum rate and energy consumption is formulated as a maximization problem to optimize UAV altitude, relay selection and user association. In the single-user-single-UAV scenario, how relay selection is affected by the IRS element number and UAV transmitting power is analyzed. In the multiple-user-multiple-UAV scenario, the maximization problem is decomposed into three sub-problems and solved by an alternating optimization method: optimizing UAV height using gradient descent and interior point algorithms, solving relay selection as a linear programming problem by continuous variable relaxation, and optimizing user association as a knapsack problem through association matrix transformation. Simulation results demonstrate the effectiveness of the proposed method and indicate that the cooperative relay-transmission scheme achieves quasi-optimal performance when compared with existing schemes.
{"title":"UAV Altitude, Relay Selection, and User Association Optimization for Cooperative Relay-Transmission in UAV-IRS-Based THz Networks","authors":"Qi Li;Pengbo Si;Yibo Zhang;Jingjing Wang;Dajun Zhang;F. Richard Yu","doi":"10.1109/TGCN.2023.3347567","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3347567","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are widely adopted as aerial relays assisting seamless coverage due to flexible deployment and high maneuverability, but their service time is considered as the bottleneck due to the constrained energy. The energy-efficient intelligent reflecting surface (IRS) is adopted in the UAV system for long-term service. In this paper, the cooperative relay-transmission in UAV-IRS based terahertz (THz) networks is studied, where UAVs with IRSs as relays facilitate long-term seamless coverage through THz transmission, and the maximum transmission capacity based on sum rate and energy consumption is formulated as a maximization problem to optimize UAV altitude, relay selection and user association. In the single-user-single-UAV scenario, how relay selection is affected by the IRS element number and UAV transmitting power is analyzed. In the multiple-user-multiple-UAV scenario, the maximization problem is decomposed into three sub-problems and solved by an alternating optimization method: optimizing UAV height using gradient descent and interior point algorithms, solving relay selection as a linear programming problem by continuous variable relaxation, and optimizing user association as a knapsack problem through association matrix transformation. Simulation results demonstrate the effectiveness of the proposed method and indicate that the cooperative relay-transmission scheme achieves quasi-optimal performance when compared with existing schemes.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078766","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 : 2023-12-26DOI: 10.1109/TGCN.2023.3347276
Fatemeh Shabestari;Nima Jafari Navimipour
Apache Spark is a popular framework for processing big data. Running Spark on Hadoop YARN allows it to schedule Spark workloads alongside other data-processing frameworks on Hadoop. When an application is deployed in a YARN cluster, its resources are given without considering energy efficiency. Furthermore, there is no way to enforce any user-specified deadline constraints. To address these issues, we propose a new deadline-aware resource management system and a scheduling algorithm to minimize the total energy consumption in Spark on YARN for heterogeneous clusters. First, a deadline-aware energy-efficient model for the considered problem is proposed. Then, using a locality-aware method, executors are assigned to applications. This algorithm sorts the nodes based on the performance per watt (PPW) metric, the number of application data blocks on nodes, and the rack locality. It also offers three ways to choose executors from different machines: greedy, random, and Pareto-based. Finally, the proposed heuristic task scheduler schedules tasks on executors to minimize total energy and tardiness. We evaluated the performance of the suggested algorithm regarding energy efficiency and satisfying the Service Level Agreement (SLA). The results showed that the method outperforms the popular algorithms regarding energy consumption and meeting deadlines.
{"title":"An Energy-Aware Resource Management Strategy Based on Spark and YARN in Heterogeneous Environments","authors":"Fatemeh Shabestari;Nima Jafari Navimipour","doi":"10.1109/TGCN.2023.3347276","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3347276","url":null,"abstract":"Apache Spark is a popular framework for processing big data. Running Spark on Hadoop YARN allows it to schedule Spark workloads alongside other data-processing frameworks on Hadoop. When an application is deployed in a YARN cluster, its resources are given without considering energy efficiency. Furthermore, there is no way to enforce any user-specified deadline constraints. To address these issues, we propose a new deadline-aware resource management system and a scheduling algorithm to minimize the total energy consumption in Spark on YARN for heterogeneous clusters. First, a deadline-aware energy-efficient model for the considered problem is proposed. Then, using a locality-aware method, executors are assigned to applications. This algorithm sorts the nodes based on the performance per watt (PPW) metric, the number of application data blocks on nodes, and the rack locality. It also offers three ways to choose executors from different machines: greedy, random, and Pareto-based. Finally, the proposed heuristic task scheduler schedules tasks on executors to minimize total energy and tardiness. We evaluated the performance of the suggested algorithm regarding energy efficiency and satisfying the Service Level Agreement (SLA). The results showed that the method outperforms the popular algorithms regarding energy consumption and meeting deadlines.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078785","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 : 2023-12-22DOI: 10.1109/TGCN.2023.3346367
Wilson de Souza Junior;Taufik Abrão
In this work, we address the energy efficiency (EE) maximization problem in a downlink communication system utilizing reconfigurable intelligent surface (RIS) in a multi-user massive multiple-input multiple-output (mMIMO) setup with zero-forcing (ZF) precoding. The channel between the base station (BS) and RIS operates under a Rician fading with Rician factor $K_{1}$ . Since systematically optimizing the RIS phase shifts in each channel coherence time interval is challenging and burdensome, we employ the statistical channel state information (CSI)-based optimization strategy to alleviate this overhead. By treating the RIS phase shifts matrix as a constant over multiple channel coherence time intervals, we can reduce the computational complexity while maintaining an interesting performance. Based on an ergodic rate (ER) lower bound closed-form, the EE optimization problem is formulated. Such a problem is non-convex and challenging to tackle due to the coupled variables. To circumvent such an obstacle, we explore the sequential optimization approach where the power allocation vector p, the number of antennas ${M}$ , and the RIS phase shifts v are separated and sequentially solved iteratively until convergence. With the help of the Lagrangian dual method, fractional programming (FP) techniques, and supported by Lemma 1 , insightful compact closed-form expressions for each of the three optimization variables are derived. Simulation results validate the effectiveness of the proposed method across different generalized channel scenarios, including non-line-of-sight (NLoS) $(K_{1}=0)$ and partially line-of-sight (LoS) $(K_{1}neq 0)$ conditions. Our numerical results demonstrate an impressive performance of the proposed Statistical CSI-based EE optimization method, achieving $approx 92$ % of the performance attained through perfect instantaneous CSI-based EE optimization. This underscores its potential to significantly reduce power consumption, decrease the number of active antennas at the base station, and effectively incorporate RIS structure in mMIMO communication setup with just statistical CSI knowledge.
在这项研究中，我们探讨了在多用户大规模多输入多输出（mMIMO）设置中利用可重构智能表面（RIS）和零强迫（ZF）预编码的下行链路通信系统中的能效（EE）最大化问题。基站（BS）和 RIS 之间的信道是在里西尔衰落（里西尔系数为 K1）条件下运行的。由于在每个信道相干时间间隔内系统优化 RIS 相移具有挑战性且负担沉重，我们采用了基于统计信道状态信息（CSI）的优化策略来减轻这一开销。通过在多个信道相干时间间隔内将 RIS 相移矩阵视为常数，我们可以降低计算复杂度，同时保持令人感兴趣的性能。基于遍历率（ER）下限闭式，我们提出了 EE 优化问题。由于存在耦合变量，这样的问题是非凸的，处理起来具有挑战性。为了规避这一障碍，我们探索了一种顺序优化方法，即将功率分配向量 p、天线数量 M 和 RIS 相移 v 分离开来，依次迭代求解，直到收敛。在拉格朗日对偶法、分数编程（FP）技术和谚语 1 的帮助下，得出了三个优化变量各自的精辟紧凑的闭式表达式。仿真结果验证了所提方法在不同广义信道场景下的有效性，包括非视距（NLoS）和部分视距（LoS）条件。这凸显了该方法在大幅降低功耗、减少基站有源天线数量，以及仅利用统计 CSI 知识将 RIS 结构有效纳入 mMIMO 通信设置方面的潜力。
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Pub Date : 2023-12-21DOI: 10.1109/TGCN.2023.3345542
Anurag Satpathy;Manmath Narayan Sahoo;Chittaranjan Swain;Muhammad Bilal;Sambit Bakshi;Houbing Song
Network virtualization allows the service providers (SPs) to divide the substrate resources into isolated entities called virtual data centers (VDCs). Typically, a VDC comprises multiple cooperative virtual machines (VMs) and virtual links (VLs) capturing their communication relationships. The SPs often re-embed VDCs entirely or partially to meet dynamic resource demands, balance the load, and perform routine maintenance activities. This paper proposes a genetic algorithm (GA)-based effective VDC re-embedding (GAMap) framework that focuses on a use case where the SPs relocate the VDCs to meet their excess resource demands, introducing the following challenges. Firstly, it encompasses the re-embedding of VMs. Secondly, VL re-embedding follows the re-embedding of the VMs, which adds to the complexity. Thirdly, VM and VL re-embedding are computationally intractable problems and are proven to be $mathcal {NP}$ -Hard. Given these challenges, we adopt the GA-based solution that generates an efficient re-embedding plan with minimum costs. Experimental evaluations confirm that the proposed scheme shows promising performance by achieving an 11.94% reduction in the re-embedding cost compared to the baselines.
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