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
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.