针对空中大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统中宽带多用户混合波束赋形设计面临的高计算开销问题,提出了一种基于深度学习的混合波束赋形网络(Attention Mechanism Based on Hybrid Beamforming Network,AMHB...针对空中大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统中宽带多用户混合波束赋形设计面临的高计算开销问题,提出了一种基于深度学习的混合波束赋形网络(Attention Mechanism Based on Hybrid Beamforming Network,AMHBNet)框架。该框架通过端到端神经网络对时分双工和频分双工系统的关键传输模块进行一体化建模。对于时分双工系统,AMHBNet联合优化上行导频组合与下行混合波束赋形;对于频分双工系统,联合优化下行导频传输、上行信道状态信息反馈和下行混合波束赋形,避免了显式信道重建,减少了导频和反馈开销。最后,AMHBNet结合卷积神经网络(Convolutional Neural Network,CNN)与基于混合波束赋形的注意力机制(Attention Mechanism Based on Hybrid Beamforming,AMHB),利用CNN提取隐式信道特征并捕捉复杂非线性关系,同时通过AMHB机制中的随机特征映射、低维近似和高效计算策略,降低计算复杂度,保持高精度和稳定性。仿真结果表明,与基于CNN的混合波束赋形网络(CNN-based Hybrid Beamforming Network,CNN-HBFN)方案相比,AMHBNet在和速率上平均提升6.5%,计算复杂度平均降低5.8%。展开更多
In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capabil...In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capability.The main objective is to simultaneously minimize the transmission power,suppress the transmit sidelobe levels,and minimize the probability of intercept,thus bolstering the LPI performance of the radar system while maintaining the desired target detection performance.An alternative optimization method is proposed to jointly optimize the transmit and receive beamformers,yielding an unified LPI optimization framework.Particularly,the proposed iterative algorithm based on the Lagrange duality theory for transmit beamforming is more efficient than the conventional convex optimization method.Numerical experiments highlight the effectiveness of the proposed approach in sidelobe suppression and computational efficiency.展开更多
文摘针对空中大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统中宽带多用户混合波束赋形设计面临的高计算开销问题,提出了一种基于深度学习的混合波束赋形网络(Attention Mechanism Based on Hybrid Beamforming Network,AMHBNet)框架。该框架通过端到端神经网络对时分双工和频分双工系统的关键传输模块进行一体化建模。对于时分双工系统,AMHBNet联合优化上行导频组合与下行混合波束赋形;对于频分双工系统,联合优化下行导频传输、上行信道状态信息反馈和下行混合波束赋形,避免了显式信道重建,减少了导频和反馈开销。最后,AMHBNet结合卷积神经网络(Convolutional Neural Network,CNN)与基于混合波束赋形的注意力机制(Attention Mechanism Based on Hybrid Beamforming,AMHB),利用CNN提取隐式信道特征并捕捉复杂非线性关系,同时通过AMHB机制中的随机特征映射、低维近似和高效计算策略,降低计算复杂度,保持高精度和稳定性。仿真结果表明,与基于CNN的混合波束赋形网络(CNN-based Hybrid Beamforming Network,CNN-HBFN)方案相比,AMHBNet在和速率上平均提升6.5%,计算复杂度平均降低5.8%。
基金supported by the National Natural Science Foundation of China(62271247)the Natural Science Foundation of Jiangsu Province(BK20240181)+4 种基金the Dreams Foundation of Jianghuai Advance Technology Center(2023-ZM01D001)the National Aerospace Science Foundation of China(20220055052001)the Qing Lan Project of Jiangsu Provincethe Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronauticsthe Key Laboratory of Radar Imaging and Microwave Photonics(Nanjing University of Aeronautics and Astronautics),Ministry of Education。
文摘In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capability.The main objective is to simultaneously minimize the transmission power,suppress the transmit sidelobe levels,and minimize the probability of intercept,thus bolstering the LPI performance of the radar system while maintaining the desired target detection performance.An alternative optimization method is proposed to jointly optimize the transmit and receive beamformers,yielding an unified LPI optimization framework.Particularly,the proposed iterative algorithm based on the Lagrange duality theory for transmit beamforming is more efficient than the conventional convex optimization method.Numerical experiments highlight the effectiveness of the proposed approach in sidelobe suppression and computational efficiency.
