Millimeter-wave(mm Wave) communications will be used in fifth-generation(5G) mobile communication systems, but they experience severe path loss and have high sensitivity to physical objects, leading to smaller cell ra...Millimeter-wave(mm Wave) communications will be used in fifth-generation(5G) mobile communication systems, but they experience severe path loss and have high sensitivity to physical objects, leading to smaller cell radii and complicated network architectures. A coverage extension scheme using large-scale antenna arrays(LSAAs) has been suggested and theoretically proven to be cost-efficient in combination with ultradense small cell networks. To analyze and optimize the LSAA-based network deployments, a comprehensive survey of recent advances in statistical mmWave channel modeling is first presented in terms of channel parameter estimation, large-scale path loss models, and small-scale cluster models. Next, the measurement and modeling results at two 5G candidate mmWave bands(e.g., 28 GHz and 39 GHz) are reviewed and compared in several outdoor scenarios of interest, where the propagation characteristics make crucial contributions to wireless network designs. Finally, the coverage behaviors of systems employing a large number of antenna arrays are discussed, as well as some implications on future mmWave cellular network designs.展开更多
A novel scheme‘user assisted cooperative relaying in beamspace massive multiple input multiple output(M-MIMO)non-orthogonal multiple access(NOMA)system’has been proposed to improve coverage area,spectrum and energy ...A novel scheme‘user assisted cooperative relaying in beamspace massive multiple input multiple output(M-MIMO)non-orthogonal multiple access(NOMA)system’has been proposed to improve coverage area,spectrum and energy efficiency for millimeter wave(mmWave)communications.A downlink system for M users,where base station(BS)is equipped with beamforming lens antenna structure having NRF radio frequency(RF)chains,has been considered.A dynamic cluster of users is formed within a beam and the intermediate users(in that cluster)between beam source and destination(user)act as relaying stations.By the use of successive interference cancellation(SIC)technique of NOMA within a cluster,the relaying stations relay the symbols with improved power to the destination.For maximizing achievable sum rate,transmit precoding and dynamic power allocation for both intra and inter beam power optimization are implemented.Simulations for performance evaluation are carried out to validate that the proposed system outperforms the conventional beamspace M-MIMO NOMA system for mmWave communications in terms of spectrum and energy efficiency.展开更多
The communication in the Millimeter-wave(mmWave)band,i.e.,30~300 GHz,is characterized by short-range transmissions and the use of antenna beamforming(BF).Thus,multiple mmWave access points(APs)should be installed to f...The communication in the Millimeter-wave(mmWave)band,i.e.,30~300 GHz,is characterized by short-range transmissions and the use of antenna beamforming(BF).Thus,multiple mmWave access points(APs)should be installed to fully cover a target environment with gigabits per second(Gbps)connectivity.However,inter-beam interference prevents maximizing the sum rates of the established concurrent links.In this paper,a reinforcement learning(RL)approach is proposed for enabling mmWave concurrent transmissions by finding out beam directions that maximize the long-term average sum rates of the concurrent links.Specifically,the problem is formulated as a multiplayer multiarmed bandit(MAB),where mmWave APs act as the players aiming to maximize their achievable rewards,i.e.,data rates,and the arms to play are the available beam directions.In this setup,a selfish concurrent multiplayer MAB strategy is advocated.Four different MAB algorithms,namely,ϵ-greedy,upper confidence bound(UCB),Thompson sampling(TS),and exponential weight algorithm for exploration and exploitation(EXP3)are examined by employing them in each AP to selfishly enhance its beam selection based only on its previous observations.After a few rounds of interactions,mmWave APs learn how to select concurrent beams that enhance the overall system performance.The proposed MAB based mmWave concurrent BF shows comparable performance to the optimal solution.展开更多
Unmanned aerial vehicle(UAV)has been widely used in many fields and is arousing global attention.As the resolution of the equipped sensors in the UAV becomes higher and the tasks become more complicated,much higher da...Unmanned aerial vehicle(UAV)has been widely used in many fields and is arousing global attention.As the resolution of the equipped sensors in the UAV becomes higher and the tasks become more complicated,much higher data rate and longer communication range are required in the foreseeable future.As the millimeter-wave(mm Wave)band can provide more abundant frequency resources than the microwave band,much higher achievable rate can be guaranteed to support UAV services such as video surveillance,hotspot coverage,and emergency communications,etc.The flexible mm Wave beamforming can be used to overcome the high path loss caused by the long propagation distance.In this paper,we study three typical application scenarios for mm Wave-UAV communications,namely communication terminal,access point,and backbone link.We present several key enabling techniques for UAV communications,including beam tracking,multi-beam forming,joint Tx/Rx beam alignment,and full-duplex relay techniques.We show the coupling relation between mm Wave beamforming and UAV positioning for mm Wave-UAV communications.Lastly,we summarize the challenges and research directions of mm Wave-UAV communications in detail.展开更多
In this paper,we investigate the effective deployment of millimeter wave(mmWave)in unmanned aerial vehicle(UAV)-enabled wireless powered communication network(WPCN).In particular,a novel framework for optimizing the p...In this paper,we investigate the effective deployment of millimeter wave(mmWave)in unmanned aerial vehicle(UAV)-enabled wireless powered communication network(WPCN).In particular,a novel framework for optimizing the performance of such UAV-enabled WPCN in terms of system throughput is proposed.In the considered model,multiple UAVs monitor in the air along the scheduled flight trajectory and transmit monitoring data to micro base stations(mBSs)with the harvested energy via mmWave.In this case,we propose an algorithm for jointly optimizing transmit power and energy transfer time.To solve the non-convex optimization problem with tightly coupled variables,we decouple the problem into more tractable subproblems.By leveraging successive convex approximation(SCA)and block coordinate descent techniques,the optimal solution is obtained by designing a two-stage joint iteration optimization algorithm.Simulation results show that the proposed algorithm with joint transmit power and energy transfer time optimization achieves significant performance gains over Q-learning method and other benchmark schemes.展开更多
针对毫米波(millimeter-wave,mmWave)通信感知一体化(integrated sensing and communication,ISAC)系统中混合波束赋形矩阵求解复杂度过高的问题,提出了一种权衡通信和雷达感知性能的低复杂度算法。首先引入辅助矩阵保证通信和雷达全数...针对毫米波(millimeter-wave,mmWave)通信感知一体化(integrated sensing and communication,ISAC)系统中混合波束赋形矩阵求解复杂度过高的问题,提出了一种权衡通信和雷达感知性能的低复杂度算法。首先引入辅助矩阵保证通信和雷达全数字波束赋形矩阵维度一致,并设计了能够提升权衡性能的构造方法。基于上述结果,在恒模约束和功率约束下设计模拟和数字波束赋形矩阵,最小化其与通信和雷达全数字波束赋形矩阵间的欧氏距离加权和。对于所建立的非凸优化问题,提出了加权正交匹配追踪(weighting-orthogonal matching pursuit,W-OMP)算法进行求解。仿真分析表明,所提算法在保持通信性能的同时,能够有效地生成指向检测目标的波束。展开更多
Channel estimation has been considered as a key issue in the millimeter-wave(mmWave)massive multi-input multioutput(MIMO)communication systems,which becomes more challenging with a large number of antennas.In this pap...Channel estimation has been considered as a key issue in the millimeter-wave(mmWave)massive multi-input multioutput(MIMO)communication systems,which becomes more challenging with a large number of antennas.In this paper,we propose a deep learning(DL)-based fast channel estimation method for mmWave massive MIMO systems.The proposed method can directly and effectively estimate channel state information(CSI)from received data without performing pilot signals estimate in advance,which simplifies the estimation process.Specifically,we develop a convolutional neural network(CNN)-based channel estimation network for the case of dimensional mismatch of input and output data,subsequently denoted as channel(H)neural network(HNN).It can quickly estimate the channel information by learning the inherent characteristics of the received data and the relationship between the received data and the channel,while the dimension of the received data is much smaller than the channel matrix.Simulation results show that the proposed HNN can gain better channel estimation accuracy compared with existing schemes.展开更多
In Unmanned Aerial Vehicle(UAV)-assisted millimeter Wave(mmWave)systems,Channel State Information(CSI)feedback is critical for the selection of modulation schemes,resource management,beamforming,etc.However,traditiona...In Unmanned Aerial Vehicle(UAV)-assisted millimeter Wave(mmWave)systems,Channel State Information(CSI)feedback is critical for the selection of modulation schemes,resource management,beamforming,etc.However,traditional CSI feedback methods lead to significant feedback overhead and energy consumption of the UAV transmitter,therefore shortening the system operation time.To tackle these issues,inspired by superimposed feedback and Integrated Sensing and Communications(ISAC),a Line of Sight(LoS)sensing-based superimposed CSI feedback scheme is proposed.Specifically,on the UAV transmitter side,the Ground-to-UAV(G2U)CSI is superimposed on the UAV-to-Ground(U2G)data to feed back to the ground Base Station(gBS).At the gBS,the dedicated LoS Sensing Network(LoS-SenNet)is designed to sense the U2G CSI in LoS and NLoS scenarios.With the sensed result of LoS-SenNet,the determined G2U CSI from the initial feature extraction will work as the priori information to guide the subsequent operation.Specifically,for the G2U CSI in NLoS,a CSI Recovery Network(CSI-RecNet)and superimposed interference cancellation are developed to recover the G2U CSI and U2G data.As for the LoS scenario,a dedicated LoS Aid Network(LoS-Aid Net)is embedded before the CSI-RecNet and the block of superimposed interference cancellation to highlight the feature of the G2U CSI.Compared with other methods of superimposed CSI feedback,simulation results demonstrate that the proposed feedback scheme effectively improves the recovery accuracy of the G2U CSI and U2G data.Besides,against parameter variations,the proposed feedback scheme presents its robustness.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.61671145the Key R&D Program of Jiangsu Province of China under Grant BE2018121
文摘Millimeter-wave(mm Wave) communications will be used in fifth-generation(5G) mobile communication systems, but they experience severe path loss and have high sensitivity to physical objects, leading to smaller cell radii and complicated network architectures. A coverage extension scheme using large-scale antenna arrays(LSAAs) has been suggested and theoretically proven to be cost-efficient in combination with ultradense small cell networks. To analyze and optimize the LSAA-based network deployments, a comprehensive survey of recent advances in statistical mmWave channel modeling is first presented in terms of channel parameter estimation, large-scale path loss models, and small-scale cluster models. Next, the measurement and modeling results at two 5G candidate mmWave bands(e.g., 28 GHz and 39 GHz) are reviewed and compared in several outdoor scenarios of interest, where the propagation characteristics make crucial contributions to wireless network designs. Finally, the coverage behaviors of systems employing a large number of antenna arrays are discussed, as well as some implications on future mmWave cellular network designs.
文摘A novel scheme‘user assisted cooperative relaying in beamspace massive multiple input multiple output(M-MIMO)non-orthogonal multiple access(NOMA)system’has been proposed to improve coverage area,spectrum and energy efficiency for millimeter wave(mmWave)communications.A downlink system for M users,where base station(BS)is equipped with beamforming lens antenna structure having NRF radio frequency(RF)chains,has been considered.A dynamic cluster of users is formed within a beam and the intermediate users(in that cluster)between beam source and destination(user)act as relaying stations.By the use of successive interference cancellation(SIC)technique of NOMA within a cluster,the relaying stations relay the symbols with improved power to the destination.For maximizing achievable sum rate,transmit precoding and dynamic power allocation for both intra and inter beam power optimization are implemented.Simulations for performance evaluation are carried out to validate that the proposed system outperforms the conventional beamspace M-MIMO NOMA system for mmWave communications in terms of spectrum and energy efficiency.
文摘The communication in the Millimeter-wave(mmWave)band,i.e.,30~300 GHz,is characterized by short-range transmissions and the use of antenna beamforming(BF).Thus,multiple mmWave access points(APs)should be installed to fully cover a target environment with gigabits per second(Gbps)connectivity.However,inter-beam interference prevents maximizing the sum rates of the established concurrent links.In this paper,a reinforcement learning(RL)approach is proposed for enabling mmWave concurrent transmissions by finding out beam directions that maximize the long-term average sum rates of the concurrent links.Specifically,the problem is formulated as a multiplayer multiarmed bandit(MAB),where mmWave APs act as the players aiming to maximize their achievable rewards,i.e.,data rates,and the arms to play are the available beam directions.In this setup,a selfish concurrent multiplayer MAB strategy is advocated.Four different MAB algorithms,namely,ϵ-greedy,upper confidence bound(UCB),Thompson sampling(TS),and exponential weight algorithm for exploration and exploitation(EXP3)are examined by employing them in each AP to selfishly enhance its beam selection based only on its previous observations.After a few rounds of interactions,mmWave APs learn how to select concurrent beams that enhance the overall system performance.The proposed MAB based mmWave concurrent BF shows comparable performance to the optimal solution.
文摘Unmanned aerial vehicle(UAV)has been widely used in many fields and is arousing global attention.As the resolution of the equipped sensors in the UAV becomes higher and the tasks become more complicated,much higher data rate and longer communication range are required in the foreseeable future.As the millimeter-wave(mm Wave)band can provide more abundant frequency resources than the microwave band,much higher achievable rate can be guaranteed to support UAV services such as video surveillance,hotspot coverage,and emergency communications,etc.The flexible mm Wave beamforming can be used to overcome the high path loss caused by the long propagation distance.In this paper,we study three typical application scenarios for mm Wave-UAV communications,namely communication terminal,access point,and backbone link.We present several key enabling techniques for UAV communications,including beam tracking,multi-beam forming,joint Tx/Rx beam alignment,and full-duplex relay techniques.We show the coupling relation between mm Wave beamforming and UAV positioning for mm Wave-UAV communications.Lastly,we summarize the challenges and research directions of mm Wave-UAV communications in detail.
文摘In this paper,we investigate the effective deployment of millimeter wave(mmWave)in unmanned aerial vehicle(UAV)-enabled wireless powered communication network(WPCN).In particular,a novel framework for optimizing the performance of such UAV-enabled WPCN in terms of system throughput is proposed.In the considered model,multiple UAVs monitor in the air along the scheduled flight trajectory and transmit monitoring data to micro base stations(mBSs)with the harvested energy via mmWave.In this case,we propose an algorithm for jointly optimizing transmit power and energy transfer time.To solve the non-convex optimization problem with tightly coupled variables,we decouple the problem into more tractable subproblems.By leveraging successive convex approximation(SCA)and block coordinate descent techniques,the optimal solution is obtained by designing a two-stage joint iteration optimization algorithm.Simulation results show that the proposed algorithm with joint transmit power and energy transfer time optimization achieves significant performance gains over Q-learning method and other benchmark schemes.
基金supported by the National Key R&D Program of China(2018YFB1802004)111 Project(B08038)。
文摘Channel estimation has been considered as a key issue in the millimeter-wave(mmWave)massive multi-input multioutput(MIMO)communication systems,which becomes more challenging with a large number of antennas.In this paper,we propose a deep learning(DL)-based fast channel estimation method for mmWave massive MIMO systems.The proposed method can directly and effectively estimate channel state information(CSI)from received data without performing pilot signals estimate in advance,which simplifies the estimation process.Specifically,we develop a convolutional neural network(CNN)-based channel estimation network for the case of dimensional mismatch of input and output data,subsequently denoted as channel(H)neural network(HNN).It can quickly estimate the channel information by learning the inherent characteristics of the received data and the relationship between the received data and the channel,while the dimension of the received data is much smaller than the channel matrix.Simulation results show that the proposed HNN can gain better channel estimation accuracy compared with existing schemes.
基金the support of the Sichuan Science and Technology Program,China(Nos.2021JDRC0003,2023YFG0316,and 2021YFG0064)the Demonstration Project of Chengdu Major Science and Technology Application,China(No.2020-YF09-00048-SN)+1 种基金the Special Funds of Industry Development of Sichuan Province,China(No.zyf-2018-056)the Industry-University Research Innovation Fund of China University(No.2021ITA10016/cxy0743)。
文摘In Unmanned Aerial Vehicle(UAV)-assisted millimeter Wave(mmWave)systems,Channel State Information(CSI)feedback is critical for the selection of modulation schemes,resource management,beamforming,etc.However,traditional CSI feedback methods lead to significant feedback overhead and energy consumption of the UAV transmitter,therefore shortening the system operation time.To tackle these issues,inspired by superimposed feedback and Integrated Sensing and Communications(ISAC),a Line of Sight(LoS)sensing-based superimposed CSI feedback scheme is proposed.Specifically,on the UAV transmitter side,the Ground-to-UAV(G2U)CSI is superimposed on the UAV-to-Ground(U2G)data to feed back to the ground Base Station(gBS).At the gBS,the dedicated LoS Sensing Network(LoS-SenNet)is designed to sense the U2G CSI in LoS and NLoS scenarios.With the sensed result of LoS-SenNet,the determined G2U CSI from the initial feature extraction will work as the priori information to guide the subsequent operation.Specifically,for the G2U CSI in NLoS,a CSI Recovery Network(CSI-RecNet)and superimposed interference cancellation are developed to recover the G2U CSI and U2G data.As for the LoS scenario,a dedicated LoS Aid Network(LoS-Aid Net)is embedded before the CSI-RecNet and the block of superimposed interference cancellation to highlight the feature of the G2U CSI.Compared with other methods of superimposed CSI feedback,simulation results demonstrate that the proposed feedback scheme effectively improves the recovery accuracy of the G2U CSI and U2G data.Besides,against parameter variations,the proposed feedback scheme presents its robustness.
