Pub Date : 2024-03-08DOI: 10.1109/TGCN.2024.3398362
Zhengyu Zhu;Jiaxue Li;Jing Yang;Bo Ai
Inspired by the cutting-edge technique simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in helping construct a cost-effective, programmable, green, invulnerable, and self-optimized Open Access Radio Network (O-RAN), in this paper, a STAR-RIS-assisted secure simultaneous wireless information and power transfer (SWIPT) system is investigated. Limited by the channel estimation technology, the robust design of this system with bounded channel estimation error is taken into consideration. By jointly designing the transmit beamforming at the access point and the transmission and reflection coefficients of STAR-RIS, a transmit power minimization problem subject to the secrecy rate constraints, energy harvesting constraint and amplitude constraints is formulated. Blocked by the coupled optimization variables and semi-infinite channel estimation errors, an alternating optimization framework along with Shur complement and S-Procedure is proposed to deal with this non-convex problem. The simulation results have proved the effectiveness of the deployment of STAR-RIS and robustness of the proposed algorithm, meanwhile, STAR-RIS can be a promising candidate to complement the construction of O-RAN.
{"title":"Robust Beamforming Design for STAR-RIS-Aided Secure SWIPT System With Bounded CSI Error","authors":"Zhengyu Zhu;Jiaxue Li;Jing Yang;Bo Ai","doi":"10.1109/TGCN.2024.3398362","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3398362","url":null,"abstract":"Inspired by the cutting-edge technique simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in helping construct a cost-effective, programmable, green, invulnerable, and self-optimized Open Access Radio Network (O-RAN), in this paper, a STAR-RIS-assisted secure simultaneous wireless information and power transfer (SWIPT) system is investigated. Limited by the channel estimation technology, the robust design of this system with bounded channel estimation error is taken into consideration. By jointly designing the transmit beamforming at the access point and the transmission and reflection coefficients of STAR-RIS, a transmit power minimization problem subject to the secrecy rate constraints, energy harvesting constraint and amplitude constraints is formulated. Blocked by the coupled optimization variables and semi-infinite channel estimation errors, an alternating optimization framework along with Shur complement and S-Procedure is proposed to deal with this non-convex problem. The simulation results have proved the effectiveness of the deployment of STAR-RIS and robustness of the proposed algorithm, meanwhile, STAR-RIS can be a promising candidate to complement the construction of O-RAN.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 3","pages":"968-977"},"PeriodicalIF":5.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090832","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 : 2024-03-08DOI: 10.1109/TGCN.2024.3374899
Ruslan Seifullaev;Steffi Knorn;Anders Ahlén;Roland Hostettler
We consider a sampled-data control system where a wireless sensor transmits its measurements to a controller over a communication channel. We assume that the sensor has a harvesting element to extract energy from the environment and store it in a rechargeable battery for future use. The harvested energy is modelled as a first-order Markovian stochastic process conditioned on a scenario parameter describing the harvesting environment. The overall model can then be represented as a Markov decision process, and a suitable transmission policy providing both good control performance and efficient energy consumption is designed using reinforcement learning approaches. Finally, supervisory control is used to switch between trained transmission policies depending on the current scenario. Also, we provide a tool for estimating an unknown scenario parameter based on measurements of harvested energy, as well as detecting the time instants of scenario changes. The above problem is solved based on Bayesian filtering and smoothing.
{"title":"Reinforcement Learning-Based Transmission Policies for Energy Harvesting Powered Sensors","authors":"Ruslan Seifullaev;Steffi Knorn;Anders Ahlén;Roland Hostettler","doi":"10.1109/TGCN.2024.3374899","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3374899","url":null,"abstract":"We consider a sampled-data control system where a wireless sensor transmits its measurements to a controller over a communication channel. We assume that the sensor has a harvesting element to extract energy from the environment and store it in a rechargeable battery for future use. The harvested energy is modelled as a first-order Markovian stochastic process conditioned on a scenario parameter describing the harvesting environment. The overall model can then be represented as a Markov decision process, and a suitable transmission policy providing both good control performance and efficient energy consumption is designed using reinforcement learning approaches. Finally, supervisory control is used to switch between trained transmission policies depending on the current scenario. Also, we provide a tool for estimating an unknown scenario parameter based on measurements of harvested energy, as well as detecting the time instants of scenario changes. The above problem is solved based on Bayesian filtering and smoothing.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1564-1573"},"PeriodicalIF":5.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672001","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}
This paper proposes a novel anti-intelligent jamming framework for unmanned aerial vehicle (UAV) networks. Multiple UAV-to-UAV communication pairs aim to maximize their sum rates with minimal power consumption, where each UAV adaptively adjusts its transmit channel and power in a distributed way to avoid intelligent jamming and co-channel interference. A ground jammer attempts to disrupt the communication quality of the UAV network by adaptively altering its jamming channel and power. We model the anti-jamming problem as a stochastic Stackelberg game, where the intelligent jammer is the leader and the UAV pairs are the followers. Considering that both parties are unwilling to share their utility functions and transmission policies, we propose reinforcement learning (RL) algorithms to solve the best response policies of each agent in the game. We adopt deep Q network (DQN) algorithm to decide the jamming policy at the jammer and propose a decentralized federated learning-assisted DQN algorithm to determine the collaborative anti-jamming policies at the UAV pairs. Simulation results demonstrate that the performance of the proposed algorithm achieves an improvement of 23.3% in anti-jamming performance compared with the independent DQN algorithm.
{"title":"UAV Communication Against Intelligent Jamming: A Stackelberg Game Approach With Federated Reinforcement Learning","authors":"Ziyan Yin;Jun Li;Zhe Wang;Yuwen Qian;Yan Lin;Feng Shu;Wen Chen","doi":"10.1109/TGCN.2024.3373886","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3373886","url":null,"abstract":"This paper proposes a novel anti-intelligent jamming framework for unmanned aerial vehicle (UAV) networks. Multiple UAV-to-UAV communication pairs aim to maximize their sum rates with minimal power consumption, where each UAV adaptively adjusts its transmit channel and power in a distributed way to avoid intelligent jamming and co-channel interference. A ground jammer attempts to disrupt the communication quality of the UAV network by adaptively altering its jamming channel and power. We model the anti-jamming problem as a stochastic Stackelberg game, where the intelligent jammer is the leader and the UAV pairs are the followers. Considering that both parties are unwilling to share their utility functions and transmission policies, we propose reinforcement learning (RL) algorithms to solve the best response policies of each agent in the game. We adopt deep Q network (DQN) algorithm to decide the jamming policy at the jammer and propose a decentralized federated learning-assisted DQN algorithm to determine the collaborative anti-jamming policies at the UAV pairs. Simulation results demonstrate that the performance of the proposed algorithm achieves an improvement of 23.3% in anti-jamming performance compared with the independent DQN algorithm.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1796-1808"},"PeriodicalIF":5.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713796","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 : 2024-03-06DOI: 10.1109/TGCN.2024.3397459
Bishmita Hazarika;Prajwalita Saikia;Keshav Singh;Chih-Peng Li
With 5G technology evolving, Open Radio Access Network (O-RAN) solutions are becoming crucial, especially for handling the diverse Quality of Service (QoS) needs in vehicular networks. These networks are dynamic and have many different applications, calling for effective O-RAN strategies. This paper examines a three-tier hierarchical O-RAN slicing model, created to address the unique challenges of vehicular networks. The top-level follow 3GPP standards like ultra-reliable and low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC). The middle level is organized by vehicle types, and the lowest level is designed for specific vehicle applications. This approach leads to better network resource management. Additionally, this study explores the advantages of a federated deep reinforcement learning (DRL) approach for efficient learning while maintaining privacy. It introduces a federated DRL approach incorporating federated averaging and deep deterministic policy gradient (DDPG) techniques, to enhance inter-slice operations and resource allocation in vehicular networks. Lastly, the effectiveness of this algorithm is demonstrated through a small simulation in a vehicular framework.
{"title":"Enhancing Vehicular Networks With Hierarchical O-RAN Slicing and Federated DRL","authors":"Bishmita Hazarika;Prajwalita Saikia;Keshav Singh;Chih-Peng Li","doi":"10.1109/TGCN.2024.3397459","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3397459","url":null,"abstract":"With 5G technology evolving, Open Radio Access Network (O-RAN) solutions are becoming crucial, especially for handling the diverse Quality of Service (QoS) needs in vehicular networks. These networks are dynamic and have many different applications, calling for effective O-RAN strategies. This paper examines a three-tier hierarchical O-RAN slicing model, created to address the unique challenges of vehicular networks. The top-level follow 3GPP standards like ultra-reliable and low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC). The middle level is organized by vehicle types, and the lowest level is designed for specific vehicle applications. This approach leads to better network resource management. Additionally, this study explores the advantages of a federated deep reinforcement learning (DRL) approach for efficient learning while maintaining privacy. It introduces a federated DRL approach incorporating federated averaging and deep deterministic policy gradient (DDPG) techniques, to enhance inter-slice operations and resource allocation in vehicular networks. Lastly, the effectiveness of this algorithm is demonstrated through a small simulation in a vehicular framework.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 3","pages":"1099-1117"},"PeriodicalIF":5.3,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090895","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 : 2024-03-06DOI: 10.1109/TGCN.2024.3396454
Abdullah Ayub Khan;Asif Ali Laghari;Abdullah M. Baqasah;Roobaea Alroobaea;Thippa Reddy Gadekallu;Gabriel Avelino Sampedro;Yaodong Zhu
Autonomous decision-making is considered an intercommunication use case that needs to be addressed when integrating open radio access networks with mobile-based 5G communication. The robustness of innovations is diminished by the conventional method of designing an end-to-end radio access network solution. Through an analysis of these possibilities, this paper presents a machine learning-based intelligent system whose primary goal is load balancing using Artificial Neural Networks with Particle Swam Optimization-enabled metaheuristic optimization mechanisms for telecommunication industry requests, like product compatibility. We increase the proposed system’s reliability by using third-generation partnership project standards to automate the distribution of transactional load among various connected units. This intelligent system encloses the hierarchy of automation enabled by artificial intelligence. Conversely, AI-enabled open radio access control explores the barriers to next-generation intercommunication, including those after 5G. It covers deterministic latency and capabilities, physical layer-based dynamic controls, privacy and security, and testing applications for AI-based controller designs.
{"title":"ORAN-B5G: A Next-Generation Open Radio Access Network Architecture With Machine Learning for Beyond 5G in Industrial 5.0","authors":"Abdullah Ayub Khan;Asif Ali Laghari;Abdullah M. Baqasah;Roobaea Alroobaea;Thippa Reddy Gadekallu;Gabriel Avelino Sampedro;Yaodong Zhu","doi":"10.1109/TGCN.2024.3396454","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3396454","url":null,"abstract":"Autonomous decision-making is considered an intercommunication use case that needs to be addressed when integrating open radio access networks with mobile-based 5G communication. The robustness of innovations is diminished by the conventional method of designing an end-to-end radio access network solution. Through an analysis of these possibilities, this paper presents a machine learning-based intelligent system whose primary goal is load balancing using Artificial Neural Networks with Particle Swam Optimization-enabled metaheuristic optimization mechanisms for telecommunication industry requests, like product compatibility. We increase the proposed system’s reliability by using third-generation partnership project standards to automate the distribution of transactional load among various connected units. This intelligent system encloses the hierarchy of automation enabled by artificial intelligence. Conversely, AI-enabled open radio access control explores the barriers to next-generation intercommunication, including those after 5G. It covers deterministic latency and capabilities, physical layer-based dynamic controls, privacy and security, and testing applications for AI-based controller designs.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 3","pages":"1026-1036"},"PeriodicalIF":5.3,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090947","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}
Low-latency computational tasks in Internet-of-Things (IoT) networks require short-packet communications. In this paper, we consider a mobile edge computing (MEC) network under time division multiple access (TDMA)-based short-packet communications. Within the considering network, a mobile user partitions an urgent task into multiple sub-tasks and delegates portions of these sub-tasks to edge computing nodes (ECNs). However, the required computing resource varies randomly along with execution failure. Thus, we explore the execution uncertainty of the proposed MEC network, which holds broader implications across the MEC network. In order to minimize the probability of execution failure in computational tasks, we present an optimal solution that determines the sub-task lengths and the blocklengths for offloading. However, the complexity of the optimal solution increases due to the involvement of the Q function and incomplete Gamma function. Consequently, we develop a low-complexity algorithm that leverages alternating optimization and majorization-maximization (MM) methods, enabling efficient computation of semi-closed-form solutions. Furthermore, to reduce the computational complexity associated with sorting the offloading order of sub-tasks, we propose two sorting criteria based on the computing speeds of the ECNs and the channel gains of the transmission links, respectively. Numerical results have validated the effectiveness of the proposed algorithm and criteria. The results also suggest that the proposed network achieves significant performance gains over the non-orthogonal multiple access (NOMA) and full offloading networks.
{"title":"Short-Packet Edge Computing Networks With Execution Uncertainty","authors":"Xiazhi Lai;Tuo Wu;Cunhua Pan;Lifeng Mai;Arumugam Nallanathan","doi":"10.1109/TGCN.2024.3373911","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3373911","url":null,"abstract":"Low-latency computational tasks in Internet-of-Things (IoT) networks require short-packet communications. In this paper, we consider a mobile edge computing (MEC) network under time division multiple access (TDMA)-based short-packet communications. Within the considering network, a mobile user partitions an urgent task into multiple sub-tasks and delegates portions of these sub-tasks to edge computing nodes (ECNs). However, the required computing resource varies randomly along with execution failure. Thus, we explore the execution uncertainty of the proposed MEC network, which holds broader implications across the MEC network. In order to minimize the probability of execution failure in computational tasks, we present an optimal solution that determines the sub-task lengths and the blocklengths for offloading. However, the complexity of the optimal solution increases due to the involvement of the Q function and incomplete Gamma function. Consequently, we develop a low-complexity algorithm that leverages alternating optimization and majorization-maximization (MM) methods, enabling efficient computation of semi-closed-form solutions. Furthermore, to reduce the computational complexity associated with sorting the offloading order of sub-tasks, we propose two sorting criteria based on the computing speeds of the ECNs and the channel gains of the transmission links, respectively. Numerical results have validated the effectiveness of the proposed algorithm and criteria. The results also suggest that the proposed network achieves significant performance gains over the non-orthogonal multiple access (NOMA) and full offloading networks.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1875-1887"},"PeriodicalIF":5.3,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713758","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 : 2024-03-04DOI: 10.1109/TGCN.2024.3372695
Nikolaos Koursioumpas;Lina Magoula;Nikolaos Petropouleas;Alexandros-Ioannis Thanopoulos;Theodora Panagea;Nancy Alonistioti;M. A. Gutierrez-Estevez;Ramin Khalili
Progressing towards a new era of Artificial Intelligence (AI) - enabled wireless networks, concerns regarding the environmental impact of AI have been raised both in industry and academia. Federated Learning (FL) has emerged as a key privacy preserving decentralized AI technique. Despite efforts currently being made in FL, its environmental impact is still an open problem. Targeting the minimization of the overall energy consumption of an FL process, we propose the orchestration of computational and communication resources of the involved devices to minimize the total energy required, while guaranteeing a certain performance of the model. To this end, we propose a Soft Actor Critic Deep Reinforcement Learning (DRL) solution, where a penalty function is introduced during training, penalizing the strategies that violate the constraints of the environment, and contributing towards a safe RL process. A device level synchronization method, along with a computationally cost effective FL environment are proposed, with the goal of further reducing the energy consumption and communication overhead. Evaluation results show the effectiveness and robustness of the proposed scheme compared to four state-of-the-art baseline solutions on different network environments and FL architectures, achieving a decrease of up to 94% in the total energy consumption.
{"title":"A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks","authors":"Nikolaos Koursioumpas;Lina Magoula;Nikolaos Petropouleas;Alexandros-Ioannis Thanopoulos;Theodora Panagea;Nancy Alonistioti;M. A. Gutierrez-Estevez;Ramin Khalili","doi":"10.1109/TGCN.2024.3372695","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3372695","url":null,"abstract":"Progressing towards a new era of Artificial Intelligence (AI) - enabled wireless networks, concerns regarding the environmental impact of AI have been raised both in industry and academia. Federated Learning (FL) has emerged as a key privacy preserving decentralized AI technique. Despite efforts currently being made in FL, its environmental impact is still an open problem. Targeting the minimization of the overall energy consumption of an FL process, we propose the orchestration of computational and communication resources of the involved devices to minimize the total energy required, while guaranteeing a certain performance of the model. To this end, we propose a Soft Actor Critic Deep Reinforcement Learning (DRL) solution, where a penalty function is introduced during training, penalizing the strategies that violate the constraints of the environment, and contributing towards a safe RL process. A device level synchronization method, along with a computationally cost effective FL environment are proposed, with the goal of further reducing the energy consumption and communication overhead. Evaluation results show the effectiveness and robustness of the proposed scheme compared to four state-of-the-art baseline solutions on different network environments and FL architectures, achieving a decrease of up to 94% in the total energy consumption.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1862-1874"},"PeriodicalIF":5.3,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713906","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 : 2024-03-02DOI: 10.1109/TGCN.2024.3396162
Haneet Kour;Rakesh Kumar Jha;Sanjeev Jain
With the wireless Internet access being increasingly popular with services such as HD video streaming and so on, the demand for high data consuming applications is also rising. This increment in demand is coupled with a proportional rise in the power consumption. It is required that the Internet traffic is offloaded to technologies that serve the users and contribute in energy consumption. There is a need to decrease the carbon footprint in the atmosphere and also make the network safe and reliable. In this article we propose a hybrid system of RF (Radio Frequency) and VLC (Visible Light Communication) for indoor communication that can provide communication along with illumination with least power consumption. The hybrid network is viable as it utilizes power with respect to the user demand and maintains the required Quality of Service (QoS) and Quality of Experience (QoE) for a particular application in use. This scheme aims for Green Communication and reduction in Electromagnetic (EM) Radiation. A comparative analysis for RF communication, Hybrid (RF+ VLC) and pure VLC is made and simulations are carried out using Python, Scilab and MathWorks tool. The proposal achieves high energy efficiency of about 37%, low Specific Absorption Rate (SAR), lower incident and absorbed power density, complexity and temperature elevation in human body tissues exposed to the radiation. It also enhances the battery lifetime of the mobile device in use by increasing the lifetime approximately by 7 hours as validated from the obtained results. Thus the overall network reliability and safety factor is enhanced with the proposed approach.
{"title":"Green and Safe 6G Wireless Networks: A Hybrid Approach","authors":"Haneet Kour;Rakesh Kumar Jha;Sanjeev Jain","doi":"10.1109/TGCN.2024.3396162","DOIUrl":"10.1109/TGCN.2024.3396162","url":null,"abstract":"With the wireless Internet access being increasingly popular with services such as HD video streaming and so on, the demand for high data consuming applications is also rising. This increment in demand is coupled with a proportional rise in the power consumption. It is required that the Internet traffic is offloaded to technologies that serve the users and contribute in energy consumption. There is a need to decrease the carbon footprint in the atmosphere and also make the network safe and reliable. In this article we propose a hybrid system of RF (Radio Frequency) and VLC (Visible Light Communication) for indoor communication that can provide communication along with illumination with least power consumption. The hybrid network is viable as it utilizes power with respect to the user demand and maintains the required Quality of Service (QoS) and Quality of Experience (QoE) for a particular application in use. This scheme aims for Green Communication and reduction in Electromagnetic (EM) Radiation. A comparative analysis for RF communication, Hybrid (RF+ VLC) and pure VLC is made and simulations are carried out using Python, Scilab and MathWorks tool. The proposal achieves high energy efficiency of about 37%, low Specific Absorption Rate (SAR), lower incident and absorbed power density, complexity and temperature elevation in human body tissues exposed to the radiation. It also enhances the battery lifetime of the mobile device in use by increasing the lifetime approximately by 7 hours as validated from the obtained results. Thus the overall network reliability and safety factor is enhanced with the proposed approach.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1729-1741"},"PeriodicalIF":5.3,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141836151","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}
This paper investigates the performance of a sub-connected active reconfigurable intelligent surface (RIS)-assisted communication system under imperfect channel state information (CSI). To ensure reliable transmission, we formulate an optimization problem aimed at maximizing the energy efficiency (EE) of the system. This optimization problem involves the joint optimization of the transmit precoder at the base station (BS) and the beamforming matrix at the RIS while considering a norm-bounded CSI error model. Given the non-convex nature of this problem, we employ deep reinforcement learning (DRL)-based methods, including deep deterministic policy gradient (DDPG), proximal policy optimization (PPO), and modified PPO, to find the optimal transmit precoder and beamforming matrix ensuring an energy-efficient operation. Additionally, we introduce an analytical framework to address this problem using traditional analytical optimization (TAO) techniques. Through extensive simulations, we showcase the convergence, robustness, and effectiveness of the proposed algorithms when compared to TAO-based solutions. Furthermore, we also highlight the impact of various system parameters, such as the total number of elements, the required number of amplifiers, and the maximum available transmit power at the BS, on the performance of the examined communication system.
本文研究了在信道状态信息（CSI）不完善的情况下，子连接主动可重构智能表面（RIS）辅助通信系统的性能。为确保可靠传输，我们提出了一个优化问题，旨在最大限度地提高系统的能效（EE）。这个优化问题涉及基站（BS）的发射前置编码器和 RIS 的波束成形矩阵的联合优化，同时考虑到规范约束的 CSI 误差模型。鉴于该问题的非凸性质，我们采用了基于深度强化学习（DRL）的方法，包括深度确定性策略梯度（DDPG）、近端策略优化（PPO）和修正的 PPO，以找到最佳发射前置编码器和波束成形矩阵，确保高能效运行。此外，我们还引入了一个分析框架，利用传统的分析优化（TAO）技术来解决这一问题。通过大量仿真，我们展示了与基于 TAO 的解决方案相比，所提算法的收敛性、鲁棒性和有效性。此外，我们还强调了各种系统参数对所研究的通信系统性能的影响，如元件总数、所需放大器数量和 BS 的最大可用发射功率。
{"title":"Robust Transmission for Energy-Efficient Sub-Connected Active RIS-Assisted Wireless Networks: DRL Versus Traditional Optimization","authors":"Vatsala Sharma;Anal Paul;Sandeep Kumar Singh;Keshav Singh;Sudip Biswas","doi":"10.1109/TGCN.2024.3370691","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3370691","url":null,"abstract":"This paper investigates the performance of a sub-connected active reconfigurable intelligent surface (RIS)-assisted communication system under imperfect channel state information (CSI). To ensure reliable transmission, we formulate an optimization problem aimed at maximizing the energy efficiency (EE) of the system. This optimization problem involves the joint optimization of the transmit precoder at the base station (BS) and the beamforming matrix at the RIS while considering a norm-bounded CSI error model. Given the non-convex nature of this problem, we employ deep reinforcement learning (DRL)-based methods, including deep deterministic policy gradient (DDPG), proximal policy optimization (PPO), and modified PPO, to find the optimal transmit precoder and beamforming matrix ensuring an energy-efficient operation. Additionally, we introduce an analytical framework to address this problem using traditional analytical optimization (TAO) techniques. Through extensive simulations, we showcase the convergence, robustness, and effectiveness of the proposed algorithms when compared to TAO-based solutions. Furthermore, we also highlight the impact of various system parameters, such as the total number of elements, the required number of amplifiers, and the maximum available transmit power at the BS, on the performance of the examined communication system.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1902-1916"},"PeriodicalIF":5.3,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713938","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}
As a revolutionary paradigm for green communication architecture for the next-generation communication system, reconfigurable intelligent surfaces (RISs) have been considered a holistic solution for simultaneous wireless information and power transfer (SWIPT). Recently, a novel concept called simultaneous transmitting and reflecting (STAR-RIS), has been recently introduced which facilitates both transmission and reflection through the meta-material surface and thus leads to full-space coverage and even better control than conventional RIS. This paper investigates the resource allocation problem in a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, we focus on the problem of power minimization for multi-user (MU) multi-input multi-output (MIMO) systems via a joint beamforming design at the BS and the STAR-RIS while guaranteeing the minimum rate and energy harvesting requirement for information receivers (R-IRs and T-IRs) and energy receivers, respectively. Owing to the non-convex and NP-hard nature of the formulated problem, we first utilize a minimum mean square error (MMSE) technique to transform the problem into its simplified form, and later utilize an alternating optimization framework which solves the problems of beamforming design at the BS and the STAR-RIS independently in an iterative manner using successive convex approximations. Simulation results confirm that the STAR-RIS can significantly reduce the required transmission power approximately by 15–20% when compared to passive RIS while satisfying given QoS constraints for SWIPT systems.
{"title":"Toward Green Communication: Power-Efficient Beamforming for STAR-RIS-Aided SWIPT","authors":"Jetti Yaswanth;Mayur Katwe;Keshav Singh;Omid Taghizadeh;Cunhua Pan;Anke Schmeink","doi":"10.1109/TGCN.2024.3370555","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3370555","url":null,"abstract":"As a revolutionary paradigm for green communication architecture for the next-generation communication system, reconfigurable intelligent surfaces (RISs) have been considered a holistic solution for simultaneous wireless information and power transfer (SWIPT). Recently, a novel concept called simultaneous transmitting and reflecting (STAR-RIS), has been recently introduced which facilitates both transmission and reflection through the meta-material surface and thus leads to full-space coverage and even better control than conventional RIS. This paper investigates the resource allocation problem in a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, we focus on the problem of power minimization for multi-user (MU) multi-input multi-output (MIMO) systems via a joint beamforming design at the BS and the STAR-RIS while guaranteeing the minimum rate and energy harvesting requirement for information receivers (R-IRs and T-IRs) and energy receivers, respectively. Owing to the non-convex and NP-hard nature of the formulated problem, we first utilize a minimum mean square error (MMSE) technique to transform the problem into its simplified form, and later utilize an alternating optimization framework which solves the problems of beamforming design at the BS and the STAR-RIS independently in an iterative manner using successive convex approximations. Simulation results confirm that the STAR-RIS can significantly reduce the required transmission power approximately by 15–20% when compared to passive RIS while satisfying given QoS constraints for SWIPT systems.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1545-1563"},"PeriodicalIF":5.3,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142671997","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：