New communication systems require high spectral and energy efficiencies to meet the growing demand for services in future networks.In this paper,an efficient multiple parallel reconfigurable intelligent surfaces(RIS)-...New communication systems require high spectral and energy efficiencies to meet the growing demand for services in future networks.In this paper,an efficient multiple parallel reconfigurable intelligent surfaces(RIS)-assisted multiuser(MU) multiple input-multiple output(MIMO) double quadrature spatial modulation(DQSM) downlink transmission system is presented.In the transmitter,the proposed N-RIS-MU-MIMO-DQSM system uses a modified block diagonalization technique and a genetic algorithm(GA) to jointly design the precoding signals required at the base station(BS) and the optimal phase changes required at multiple RISs.A reduced detection complexity and improved bit error rate(BER) performance are achieved by incorporating spatial modulation.The proposed system is compared under the same conditions and parameters with two reference systems,considering blind and optimized RISs approaches over correlated Rayleigh fading channels.Results show that compared with a similar system that does not use RISs,the proposed system has up to30 dB gain in BER performance.Compared with a similar system based on conventional quadrature amplitude modulation(QAM),the proposed system has gains of up to 2-3 dB in BER performance and up to 55.8% lower detection complexity for the analyzed cases.展开更多
The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyeffic...The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.展开更多
The deployment of reconfigurable intelligent surfaces(RISs)can enhance the coverage ability of millimeter wave(mmWave)communication systems.However,the typical strong line-of-sight(LoS)far-field propagation between a ...The deployment of reconfigurable intelligent surfaces(RISs)can enhance the coverage ability of millimeter wave(mmWave)communication systems.However,the typical strong line-of-sight(LoS)far-field propagation between a base station(BS)and an RIS reduces channel rank,thus affecting multiuser spatial multiplexing.To address this issue,we propose an RIS subarray-assisted mmWave mul-tiuser transmission scheme.To increase channel rank,RIS is divided into multiple subarrays with adjustable spacing,and the channel is modeled using a hybrid spherical-and planar-wave model.To minimize interuser interference,the RIS subarrays are deployed in the discrete Fourier transform(DFT)direction of the BS antenna array.To maximize signal efficiency,the BS precoder,the RIS reflection coefficients,and the user’s combiner are jointly designed.Numerical simulations were conducted to verify the effectiveness of the proposed RIS subarray deployment strategy and the performance of the wireless transmission scheme.In a four-user equipment(UE)communication scenario in the mmWave band,the effective rank of the BS-RIS channel approaches full rank,and the spectral efficiency of each UE is improved by at least 3 bit/(s·Hz).展开更多
This paper investigates a wireless powered communication network(WPCN)facilitated by an unmanned aerial vehicle(UAV)in Internet of Things(IoT)networks,where multiple IoT devices(IoTDs)gather energy from a terrestrial ...This paper investigates a wireless powered communication network(WPCN)facilitated by an unmanned aerial vehicle(UAV)in Internet of Things(IoT)networks,where multiple IoT devices(IoTDs)gather energy from a terrestrial energy station(ES)during the wireless energy transfer(WET)stage,followed by the UAV collecting data from these powered IoTDs with the time division multiple access(TDMA)protocol in the wireless information transfer(WIT)stage.To overcome the challenges of radio propagation caused by obstructions,we incorporate a reconfigurable intelligent surface(RIS)to enhance the link quality of the ES-IoTDs and IoTDs-UAV.The primary objective is to maximize the average sum rate of all IoTDs by jointly optimizing UAV trajectory,ES transmit power,and RIS phase shifts,along with the time allocation for WET and WIT.To this end,we reformulate the optimization problem as a markov decision process(MDP)and introduce a deep reinforcement learning(DRL)approach for addressing the formulated problem,called the proximal policy optimization(PPO)based energy harvesting with trajectory design and phase shift optimization(PPO-EHTDPS)algorithm.By continuously exploring within the environment,the PPO algorithm refines its policy to optimize the UAV trajectory,the energy phase shifts,ES transmit power,and WET/WIT time allocation.Additionally,a continuous phase shift optimization algorithm is employed to determine the information phase shifts for each IoTD to maximize average sum rate.Finally,numerical results demonstrate that the proposed PPOEHTDPS algorithm can significantly achieve higher average sum rate and show better convergence performance over the benchmark algorithms.展开更多
The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-inpu...The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-input multiple-output(MIMO)communication system with a STAR-RIS,a base station(BS),an eavesdropper,and multiple users,the system security rate is studied.A joint design of the power allocation at the transmitter and phase shift matrices for reflection and transmission at the STAR-RIS is conducted,in order to maximize the worst achievable security data rate(ASDR).Since the problem is nonconvex and hence challenging,a particle swarm optimization(PSO)based algorithm is developed to tackle the problem.Both the cases of continuous and discrete phase shift matrices at the STAR-RIS are considered.Simulation results demonstrate the effectiveness of the proposed algorithm and shows the benefits of using STAR-RIS in MIMO mutliuser systems.展开更多
文摘New communication systems require high spectral and energy efficiencies to meet the growing demand for services in future networks.In this paper,an efficient multiple parallel reconfigurable intelligent surfaces(RIS)-assisted multiuser(MU) multiple input-multiple output(MIMO) double quadrature spatial modulation(DQSM) downlink transmission system is presented.In the transmitter,the proposed N-RIS-MU-MIMO-DQSM system uses a modified block diagonalization technique and a genetic algorithm(GA) to jointly design the precoding signals required at the base station(BS) and the optimal phase changes required at multiple RISs.A reduced detection complexity and improved bit error rate(BER) performance are achieved by incorporating spatial modulation.The proposed system is compared under the same conditions and parameters with two reference systems,considering blind and optimized RISs approaches over correlated Rayleigh fading channels.Results show that compared with a similar system that does not use RISs,the proposed system has up to30 dB gain in BER performance.Compared with a similar system based on conventional quadrature amplitude modulation(QAM),the proposed system has gains of up to 2-3 dB in BER performance and up to 55.8% lower detection complexity for the analyzed cases.
基金funded by the deanship of scientific research(DSR),King Abdukaziz University,Jeddah,under grant No.(G-1436-611-225)。
文摘The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.
基金The National Natural Science Foundation of China(No.62261160576,62401137)the China Postdoctoral Science Foundation(No.BX20230065,2024M750421)+2 种基金the Natural Science Foundation of Jiangsu Province(No.BK20241281),the Jiangsu Excellent Postdoctoral Program(No.2023ZB476)the Fundamental Research Funds for the Central Universities(No.2242023K5003)the Youth Science&Technology Talents Lifting Project by JSAST(No.TJ-2022-083).
文摘The deployment of reconfigurable intelligent surfaces(RISs)can enhance the coverage ability of millimeter wave(mmWave)communication systems.However,the typical strong line-of-sight(LoS)far-field propagation between a base station(BS)and an RIS reduces channel rank,thus affecting multiuser spatial multiplexing.To address this issue,we propose an RIS subarray-assisted mmWave mul-tiuser transmission scheme.To increase channel rank,RIS is divided into multiple subarrays with adjustable spacing,and the channel is modeled using a hybrid spherical-and planar-wave model.To minimize interuser interference,the RIS subarrays are deployed in the discrete Fourier transform(DFT)direction of the BS antenna array.To maximize signal efficiency,the BS precoder,the RIS reflection coefficients,and the user’s combiner are jointly designed.Numerical simulations were conducted to verify the effectiveness of the proposed RIS subarray deployment strategy and the performance of the wireless transmission scheme.In a four-user equipment(UE)communication scenario in the mmWave band,the effective rank of the BS-RIS channel approaches full rank,and the spectral efficiency of each UE is improved by at least 3 bit/(s·Hz).
文摘This paper investigates a wireless powered communication network(WPCN)facilitated by an unmanned aerial vehicle(UAV)in Internet of Things(IoT)networks,where multiple IoT devices(IoTDs)gather energy from a terrestrial energy station(ES)during the wireless energy transfer(WET)stage,followed by the UAV collecting data from these powered IoTDs with the time division multiple access(TDMA)protocol in the wireless information transfer(WIT)stage.To overcome the challenges of radio propagation caused by obstructions,we incorporate a reconfigurable intelligent surface(RIS)to enhance the link quality of the ES-IoTDs and IoTDs-UAV.The primary objective is to maximize the average sum rate of all IoTDs by jointly optimizing UAV trajectory,ES transmit power,and RIS phase shifts,along with the time allocation for WET and WIT.To this end,we reformulate the optimization problem as a markov decision process(MDP)and introduce a deep reinforcement learning(DRL)approach for addressing the formulated problem,called the proximal policy optimization(PPO)based energy harvesting with trajectory design and phase shift optimization(PPO-EHTDPS)algorithm.By continuously exploring within the environment,the PPO algorithm refines its policy to optimize the UAV trajectory,the energy phase shifts,ES transmit power,and WET/WIT time allocation.Additionally,a continuous phase shift optimization algorithm is employed to determine the information phase shifts for each IoTD to maximize average sum rate.Finally,numerical results demonstrate that the proposed PPOEHTDPS algorithm can significantly achieve higher average sum rate and show better convergence performance over the benchmark algorithms.
文摘The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-input multiple-output(MIMO)communication system with a STAR-RIS,a base station(BS),an eavesdropper,and multiple users,the system security rate is studied.A joint design of the power allocation at the transmitter and phase shift matrices for reflection and transmission at the STAR-RIS is conducted,in order to maximize the worst achievable security data rate(ASDR).Since the problem is nonconvex and hence challenging,a particle swarm optimization(PSO)based algorithm is developed to tackle the problem.Both the cases of continuous and discrete phase shift matrices at the STAR-RIS are considered.Simulation results demonstrate the effectiveness of the proposed algorithm and shows the benefits of using STAR-RIS in MIMO mutliuser systems.
