无线供电通信网络(Wireless-powered Communication Network,WPCN)不仅可以实现远程无线充电而且能够提供无线通信服务,因此受到了学术界和工业界的广泛关注。然而,低效率的能量收集和信息传输会限制WPCN的性能,并且在WPCN中的双重远近...无线供电通信网络(Wireless-powered Communication Network,WPCN)不仅可以实现远程无线充电而且能够提供无线通信服务,因此受到了学术界和工业界的广泛关注。然而,低效率的能量收集和信息传输会限制WPCN的性能,并且在WPCN中的双重远近效应会导致物联网设备收集的能量与消耗的能量之间的不平衡。为了解决这些问题,提出基于能量回收的主动智能反射面(Intelligent Reflecting Surface,IRS)辅助WPCN波束成形算法,其中物联网设备既能从功率站端收集能量,还能从其他物联网设备的上行信息传输中回收能量。考虑能量收集、吞吐量、时间分配,以及功率站和主动IRS的最大功率等约束,基于能量回收机制,建立了系统总吞吐量最大化的资源分配模型;然后,提出一种基于内层近似和双线性变换的交替优化算法进行求解。仿真结果表明,在相应的参数配置下,能量回收机制的应用能够提升约8.13%的吞吐量,而主动IRS的应用能够提升约61.1%的吞吐量。展开更多
为提升认知无线网络能效,构建了一个包含认知基站(cognitive base station,CBS)、主基站(primary base station,PBS)及双智能反射面(intelligent reflector surface,IRS)的模型,提出了一种主被动波束成形联合优化方案,并通过仿真实验验...为提升认知无线网络能效,构建了一个包含认知基站(cognitive base station,CBS)、主基站(primary base station,PBS)及双智能反射面(intelligent reflector surface,IRS)的模型,提出了一种主被动波束成形联合优化方案,并通过仿真实验验证所提方案的有效性。结果表明,通过协同优化双IRS的反射相位,能够显著提升认知无线网络频谱效率和能效,在增加IRS反射单元数量时效果更为明显。随着用户数量的增加,认知无线网络能效呈现上升趋势,但增长速度放缓,因此实际部署时需要综合考虑用户数量、网络复杂度和能效之间的关系。展开更多
Intelligent reflecting surface(IRS)is a newly emerged and promising paradigm to substantially improve the performance of wireless communications by constructing favorable communication channels via properly tuning mas...Intelligent reflecting surface(IRS)is a newly emerged and promising paradigm to substantially improve the performance of wireless communications by constructing favorable communication channels via properly tuning massive reflecting elements.This paper considers a distributed IRS aided decode-and-forward(DF)relaying system over Nakagami-m fading channels.Based on a tight approximation for the distribution of the received signalto-noise ratio(SNR),we first derive exact closed-form expressions of the outage probability,ergodic capacity,and energy efficiency for the considered system.Moreover,we propose the optimal IRS configuration considering the energy efficiency and pilot overhead.Finally,we compare the performance between the distributed IRS-aided DF relaying and multi-IRS-only systems,and verify the analytical results by using monte carlo simulations.展开更多
In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of ...In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.展开更多
The utilization of mobile edge computing(MEC)for unmanned aerial vehicle(UAV)communication presents a viable solution for achieving high reliability and low latency communication.This study explores the potential of e...The utilization of mobile edge computing(MEC)for unmanned aerial vehicle(UAV)communication presents a viable solution for achieving high reliability and low latency communication.This study explores the potential of employing intelligent reflective surfaces(IRS)andUAVs as relay nodes to efficiently offload user computing tasks to theMEC server system model.Specifically,the user node accesses the primary user spectrum,while adhering to the constraint of satisfying the primary user peak interference power.Furthermore,the UAV acquires energy without interrupting the primary user’s regular communication by employing two energy harvesting schemes,namely time switching(TS)and power splitting(PS).The selection of the optimal UAV is based on the maximization of the instantaneous signal-to-noise ratio.Subsequently,the analytical expression for the outage probability of the system in Rayleigh channels is derived and analyzed.The study investigates the impact of various system parameters,including the number of UAVs,peak interference power,TS,and PS factors,on the system’s outage performance through simulation.The proposed system is also compared to two conventional benchmark schemes:the optimal UAV link transmission and the IRS link transmission.The simulation results validate the theoretical derivation and demonstrate the superiority of the proposed scheme over the benchmark schemes.展开更多
In this paper,we investigate the energy efficiency maximization for mobile edge computing(MEC)in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communications.In particular,UAVcan collect the ...In this paper,we investigate the energy efficiency maximization for mobile edge computing(MEC)in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communications.In particular,UAVcan collect the computing tasks of the terrestrial users and transmit the results back to them after computing.We jointly optimize the users’transmitted beamforming and uploading ratios,the phase shift matrix of IRS,and the UAV trajectory to improve the energy efficiency.The formulated optimization problem is highly non-convex and difficult to be solved directly.Therefore,we decompose the original problem into three sub-problems.We first propose the successive convex approximation(SCA)based method to design the beamforming of the users and the phase shift matrix of IRS,and apply the Lagrange dual method to obtain a closed-form expression of the uploading ratios.For the trajectory optimization,we propose a block coordinate descent(BCD)based method to obtain a local optimal solution.Finally,we propose the alternating optimization(AO)based overall algorithmand analyzed its complexity to be equivalent or lower than existing algorithms.Simulation results show the superiority of the proposedmethod compared with existing schemes in energy efficiency.展开更多
This paper considers an intelligent reflecting surface(IRS)-assisted multiple-input multiple-output(MIMO)system.To maximize the average achievable rate(AAR)under outdated channel state information(CSI),we propose a tw...This paper considers an intelligent reflecting surface(IRS)-assisted multiple-input multiple-output(MIMO)system.To maximize the average achievable rate(AAR)under outdated channel state information(CSI),we propose a twin-timescale passive beamforming(PBF)and power allocation protocol which can reduce the IRS configuration and training overhead.Specifi-cally,the short-timescale power allocation is designed with the outdated precoder and fixed PBF.A new particle swarm opti-mization(PSO)-based long-timescale PBF optimization is pro-posed,where mini-batch channel samples are utilized to update the fitness function.Finally,simulation results demonstrate the effectiveness of the proposed method.展开更多
文摘无线供电通信网络(Wireless-powered Communication Network,WPCN)不仅可以实现远程无线充电而且能够提供无线通信服务,因此受到了学术界和工业界的广泛关注。然而,低效率的能量收集和信息传输会限制WPCN的性能,并且在WPCN中的双重远近效应会导致物联网设备收集的能量与消耗的能量之间的不平衡。为了解决这些问题,提出基于能量回收的主动智能反射面(Intelligent Reflecting Surface,IRS)辅助WPCN波束成形算法,其中物联网设备既能从功率站端收集能量,还能从其他物联网设备的上行信息传输中回收能量。考虑能量收集、吞吐量、时间分配,以及功率站和主动IRS的最大功率等约束,基于能量回收机制,建立了系统总吞吐量最大化的资源分配模型;然后,提出一种基于内层近似和双线性变换的交替优化算法进行求解。仿真结果表明,在相应的参数配置下,能量回收机制的应用能够提升约8.13%的吞吐量,而主动IRS的应用能够提升约61.1%的吞吐量。
文摘为提升认知无线网络能效,构建了一个包含认知基站(cognitive base station,CBS)、主基站(primary base station,PBS)及双智能反射面(intelligent reflector surface,IRS)的模型,提出了一种主被动波束成形联合优化方案,并通过仿真实验验证所提方案的有效性。结果表明,通过协同优化双IRS的反射相位,能够显著提升认知无线网络频谱效率和能效,在增加IRS反射单元数量时效果更为明显。随着用户数量的增加,认知无线网络能效呈现上升趋势,但增长速度放缓,因此实际部署时需要综合考虑用户数量、网络复杂度和能效之间的关系。
基金supported in part by National Natural Science Foundation of China under Grant 62371262 and 61971467in part by the Key Research and Development Program of Jiangsu Province of China under Grant BE2021013-1+1 种基金in part by the Qinlan Project of Jiangsu Provincein part by the Scientific Research Program of Nantong under Grant JC22022026
文摘Intelligent reflecting surface(IRS)is a newly emerged and promising paradigm to substantially improve the performance of wireless communications by constructing favorable communication channels via properly tuning massive reflecting elements.This paper considers a distributed IRS aided decode-and-forward(DF)relaying system over Nakagami-m fading channels.Based on a tight approximation for the distribution of the received signalto-noise ratio(SNR),we first derive exact closed-form expressions of the outage probability,ergodic capacity,and energy efficiency for the considered system.Moreover,we propose the optimal IRS configuration considering the energy efficiency and pilot overhead.Finally,we compare the performance between the distributed IRS-aided DF relaying and multi-IRS-only systems,and verify the analytical results by using monte carlo simulations.
基金This work was supported by the Key Scientific and Technological Project of Henan Province(Grant Number 222102210212)Doctoral Research Start Project of Henan Institute of Technology(Grant Number KQ2005)Key Research Projects of Colleges and Universities in Henan Province(Grant Number 23B510006).
文摘In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.
基金the National Natural Science Foundation of China(62271192)Henan Provincial Scientists Studio(GZS2022015)+10 种基金Central Plains Talents Plan(ZYYCYU202012173)NationalKeyR&DProgramofChina(2020YFB2008400)the Program ofCEMEE(2022Z00202B)LAGEO of Chinese Academy of Sciences(LAGEO-2019-2)Program for Science&Technology Innovation Talents in the University of Henan Province(20HASTIT022)Natural Science Foundation of Henan under Grant 202300410126Program for Innovative Research Team in University of Henan Province(21IRTSTHN015)Equipment Pre-Research Joint Research Program of Ministry of Education(8091B032129)Training Program for Young Scholar of Henan Province for Colleges and Universities(2020GGJS172)Program for Science&Technology Innovation Talents in Universities of Henan Province under Grand(22HASTIT020)Henan Province Science Fund for Distinguished Young Scholars(222300420006).
文摘The utilization of mobile edge computing(MEC)for unmanned aerial vehicle(UAV)communication presents a viable solution for achieving high reliability and low latency communication.This study explores the potential of employing intelligent reflective surfaces(IRS)andUAVs as relay nodes to efficiently offload user computing tasks to theMEC server system model.Specifically,the user node accesses the primary user spectrum,while adhering to the constraint of satisfying the primary user peak interference power.Furthermore,the UAV acquires energy without interrupting the primary user’s regular communication by employing two energy harvesting schemes,namely time switching(TS)and power splitting(PS).The selection of the optimal UAV is based on the maximization of the instantaneous signal-to-noise ratio.Subsequently,the analytical expression for the outage probability of the system in Rayleigh channels is derived and analyzed.The study investigates the impact of various system parameters,including the number of UAVs,peak interference power,TS,and PS factors,on the system’s outage performance through simulation.The proposed system is also compared to two conventional benchmark schemes:the optimal UAV link transmission and the IRS link transmission.The simulation results validate the theoretical derivation and demonstrate the superiority of the proposed scheme over the benchmark schemes.
基金the Key Scientific and Technological Project of Henan Province(Grant Number 222102210212)Doctoral Research Start Project of Henan Institute of Technology(Grant Number KQ2005)+1 种基金Doctoral Research Start Project of Henan Institute of Technology(Grant Number KQ2110)Key Research Projects of Colleges and Universities in Henan Province(Grant Number 23B510006).
文摘In this paper,we investigate the energy efficiency maximization for mobile edge computing(MEC)in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communications.In particular,UAVcan collect the computing tasks of the terrestrial users and transmit the results back to them after computing.We jointly optimize the users’transmitted beamforming and uploading ratios,the phase shift matrix of IRS,and the UAV trajectory to improve the energy efficiency.The formulated optimization problem is highly non-convex and difficult to be solved directly.Therefore,we decompose the original problem into three sub-problems.We first propose the successive convex approximation(SCA)based method to design the beamforming of the users and the phase shift matrix of IRS,and apply the Lagrange dual method to obtain a closed-form expression of the uploading ratios.For the trajectory optimization,we propose a block coordinate descent(BCD)based method to obtain a local optimal solution.Finally,we propose the alternating optimization(AO)based overall algorithmand analyzed its complexity to be equivalent or lower than existing algorithms.Simulation results show the superiority of the proposedmethod compared with existing schemes in energy efficiency.
基金supported by the National Natural Science Foundation of China(62271068)the Beijing Natural Science Foundation(L222046).
文摘This paper considers an intelligent reflecting surface(IRS)-assisted multiple-input multiple-output(MIMO)system.To maximize the average achievable rate(AAR)under outdated channel state information(CSI),we propose a twin-timescale passive beamforming(PBF)and power allocation protocol which can reduce the IRS configuration and training overhead.Specifi-cally,the short-timescale power allocation is designed with the outdated precoder and fixed PBF.A new particle swarm opti-mization(PSO)-based long-timescale PBF optimization is pro-posed,where mini-batch channel samples are utilized to update the fitness function.Finally,simulation results demonstrate the effectiveness of the proposed method.
